Microcontroller Training Kit Design Compatible with Drawings of the ISIS Simulation Program

— Microcontrollers are inside all areas of industry today. They are being used in many electronic circuit designs, ranging from very simple systems to highly complex systems. For that reason, microcontrollers have a great importance in the electronics sector. Microcontrollers are especially important in terms of students enrolled in engineering departments in the fields of computers, electrical engineering, electronics, and mechatronics. Experimental sets and simulation programs are used in the learning and teaching of microcontrollers in real-world simulations. In this study, a 16F877 microcontroller training set, designed for use in microcontroller courses, was used. The designed training kit allows experimentation with the 16F877 microcontroller circuits as a subprogram of the Proteus program of Lab center Electronics, which is electronic circuit drawing in Proteus ISIS (Intelligent Schematic Input System) simulation software tested in a real environment. The microcontroller basic hardware connections and the application circuits on the training kit were implemented as modules similar to the ISIS program drawings. Transition from the simulation environment to the real-life environment can be done easily by using these modules, and the information learned in this way becomes concrete.

I. INTRODUCTION
dvancements in today's semiconductor technology have changed the way industrial fabrication and processcontrol problems are resolved. The microcontroller makes it econ H. Apaydın is a Lecturer in the Department of Biomedical Device Technology Program, Kütahya Dumlupınar University, Turkey (e-mail: hasbi.apaydin@dpu.edu.tr).
N. Fusun Oyman Serteller is a Professor in the Department of Electrical Engineering, Marmara University, Turkey (corresponding author to provide phone: +90 (216) 777 40 01; fax: +90 (216) 777 40 01; e-mail: fserteller@marmara.edu.tr). economical according to digitally control more devices and processes, by reducing size and cost, as compared to a design using separate microprocessors, memories, and input/output devices [1]. Many control problems can be solved effectively by using microcontrollers [2], [3]. Because of this advantage, microcontrollers are widely used and developed rapidly in circuit designs in many areas such as day life devices, industrial control devices and metering -measurement devices [4], [5], [6]. That makes the widely used microcontrollers important for the education of students enrolled in electrical, electronic, computer, and mechatronics training programs [7], [8], [9], [10], [23].
Learning about microcontrollers is quite a complicated lesson for students. Traditionally, microcontroller courses have been taught by concentrating mainly on the architecture and programming of these devices, with very little spending effort to their practical applications [11], [12]. Thus, the most difficult task is to keep students engaged in that lesson, which will be the backbone of their careers. The aim should be to decrease the complicated lesson structure and to integrate theory with practice in their real lives [13], [14], [15], [16]. Students use simulation programs and experiment sets in training environments to facilitate learning the real-life applications of microcontrollers. Various microcontroller programming and simulation software programs are available for industrial and educational use. The software allows the user to develop and test all microcontroller-based designs before making a physical prototype [17], [18], [19]. If digital tools are fully exploited, they can offer innovation in teaching and ways to increase the attractiveness of learning [20]. Experiment sets are used to test the operation of the circuits that are created and tested with software packages in the real environment. Various microcontroller programming and electrical software programs are available for industrial and educational use [21], [22]. By using those sets of experiments, it is possible to carry out the work of many departments, from the simple applications to the complex applications, and the learning becomes permanent.
In the literature, there are various microcontroller experiments set. According to the study of Topaloğlu [24], he designed the experiment set with a 16F628 microcontroller. It has realized the connection points that will obtain the highest efficiency from 16F628. The time spent on hardware design A has been reduced to a minimum before developing programs on the card. Transitions between application modules on the card are provided with sockets used to join electronic materials.
Another study Özemirci et al. [10], They have developed Arduino Uno application set which is aimed to be easy to apply. It is a set that saves time with its hardware preparation in the work of the users and is portable and provides ease of transportation and storage. Thanks to the circuits on the application set, data detection, control and algorithm development applications can be made.
Another study Engin et al. [25], they designed a modular experiment set where each student performed his own microprocessor experiment set step by step depending on the course of the lesson. It has been observed that students' enjoyment items and success scores increased.
Another study Özcan et al. [26], Communication, Electronics, Control and Automation Technology Program students have been designed and implemented a 16f877 experiment set so that students can perform the subjects they have learned in the microprocessors lessons. The developed experimental set was used by students in practice.
Another study İnce et al. [27], They designed an experiment set where they could develop pulse width modulation(PWM) signal generation, filter design, and digital analog conversion (DAC) applications with a microcontroller to teach students these subjects and practices.
Another study Kassem et al. [28], Created a training set for electrical and computer engineering students to develop microcontroller circuit designs. This set contains many applications on a single platform. Their aim is to prepare students for the actual world using the education set.
Asparuhova et al. [29], Performed the operation of temperature, humidity, flame and motion sensors controlled by the arduino uno microcontroller in the simulation program Proteus environment. They said that using the simulation program makes students flexible and creative.
Suryanarayana et al. [30], Microcontrollers called Micro-LABlet programmable from USB have developed a training set. The microcontroller used in the training set is the 8051 microcontroller. The created training set was introduced and applied to the fourth term engineering students. It was seen that the students enjoyed creating prototypes and it was easier to understand the subject by the students.
In this study, the design of the 16F877 microcontroller experiment set is realized. We have described and performed a novel learning environment design to comprehend the response of electric circuits. The modules in the experiment set are similar to those in the ISIS program. Because the modules are similar to the circuit diagrams of the simulations, students will not be strangers when practicing in the real environment. At the same time, the students will be able to reinforce what they have learned by using the experiment set outside the school.

II. MATERIALS AND METHODS
An important object of learning in engineering education is that students should understand and learn to use theories and models and link them to objects and events [13]. The new approach with the real-life equipment experiment set as methodological tools was prepared with that idea in mind. The designed experiment set consists of the main circuit platform, consisting of the 16F877 microcontroller and the modules on which experiments can be done. The single-sided copper plate used in the main circuit platform measures 200x300 mm and the plate thickness is 1.60 mm. In the circuit diagram of the project a 40 pin integrated socket is placed in the center of the copper plate where the 16F877 microcontroller will be installed and a bus system is built around the microcontroller. The thickness of the data path created with pins around the microcontroller was set as 1 mm. The energy paths thickness in the feed section is selected as 2 mm. A total of 113 connection pads have been created on the main circuit board where the experimental modules can be installed. Signal and supply lines are combined with pads. Connection pads are drilled 4mm wide. Female bullet banana plug Connectors are soldered to the copper part on the pads of the main circuit board. The paper containing the connection points, material drawings and naming is printed on the printer and adhered to the main circuit board. Figure 1 shows the 16F877 training kit and 3D printed circuit diagram. The microcontroller main circuit supply is provided by connecting the adapter to the +12-volt power input connector and the main circuit supply is made active/passive with the ON/OFF switch. An in-circuit serial programming (ICSP) connection socket was created to load the microcontroller while experiments are carried out on the circuit. That means that it is not necessary to program the microcontroller from the circuit by cable and disconnect it each time, and it prevents the microcontroller's legs from being damaged.
The modules of the main circuit set were designed to be similar to the drawings in the ISIS program in order to make the microcontroller ready for operation. The basic connections of the microcontroller are modules of oscillator, +5-volt supply, and reset pin connections. Oscillators are used as a source for clock pulses used to process commands in microcontrollers. Since our microcontroller experiment set contains applications that require moderate power and frequency, the crystal oscillator (XT) circuit module is used oscillator. It is ideal to work with 5 Volts as supply voltage in the microcontroller. The pins 11 and 32 of the 16F877 Microcontroller used are +5 Volts positive feed, and the pins 12 and 31 are GND negative feed. The +5-volt supply module is designed with these ports in mind. Pin 4 is reset pin connection in 16F877. If this pin is not connected over the +5volt supply resistance the system operates unstable. To prevent this a reset pin connection module has been created. +5-volt supply and reset pin connection modules, single sided copper plate is used and plate thickness is 1.60 mm. Male bullet banana plug connectors are soldered to the copper part on the pads of the modules. Paper containing connection points, material drawings and material name is printed on the printer and adhered to the respective modules. These new approaches, modules, ISIS drawings and 3D printed circuit diagram visualizations are introduced beginning with Figure   2. A drawing of the oscillator pin connection is shown in Figure 2  It is necessary to start with the easiest-to-learn universal 8bit controllers, since the study of more complex 16-and 32-bit series microcontrollers [19]. Experimental modes have been designed in which students can realize a seven-segment display, 2 lines 16 characters Liquid Crystal Display (LCD) (8-bit and 4-bit) displays and a stepper motor application. One common anode indicator was used in the design of the 7 segment display module. In the indicator used, 8 data pins and 2 GND pins are used. The logic1 signal is applied to the data pin to which the segment that is desired to give light is applied and the LED is illuminated. In the LCD display design, 2 lines 16 characters LCD are used. The LCD has a total of 14 pins. The connection of the used LCD to the microcontroller can be made in two ways, 4 bits and 8 bits. The advantage of the 8bit connection is that the data to be written on the screen is sent faster but this is not very clear from the eye. The advantage in 4-bit connection is that 4 pins are used less in the microcontroller and extra hardware can be connected to these pins. A 5-pin unipolar stepper motor is used in the design of stepper motor module. The common end of the stepper motor is connected to ground and the other 4 ends are connected to the +5 volt supply via transistors. The control of the transistors is provided by the signals sent over the microcontroller pins. Those modules are shown in Figures  8(a)

III. DISCUSSION AND FINDINGS
This study tried to simplify by matching the microcontroller equipment schematic displaying to real-life equipment. Thanks to these modules and additional modules, the students are able to practice what they have been taught without cooling off from the educational environment, because there is no disconnect between the simulations and real connections. A large sample area can be created through the study, and it contributes to the learning of the students. With the designed system, the students can realize all the applications in the following areas:  LED on-and-off applications with buttons  LED lamp light brightness intensity control applications  Seven-segment display and LCD control applications  Analog/digital conversion applications with potentiometer  Motor speed control application with potentiometer  Stepper motor control applications  Servo motor control applications With the implementation of training kits, the teaching methodology takes a shift from the classical method to a new paradigm. With this new design set, the student gets the hands-on experience of converting theorical information to a real-life experiment.
Microcontrollers course was created to examine the use of the designed microcontrollers training set and its contribution to learning. midterm and final exams were conducted to determine the learning levels of the participants. The research  covers the 2019 and 2020 periods, respectively. The course was held with the participation of consisting of Biomedical Device and Mechatronics Technology Program students. In total, 72 participants used only ISIS program circuit drawings in 2019 period and 65 participants used ISIS program circuit drawings with microcontrollers training set in 2020 period. Among the participants, those who were in daytime education were named as 1st education and those who were in evening education were named as 2nd education. For this purpose, the exam grades and comparison of the three student groups participating in the microcontroller course are shown in Table  1.
According to Table 1, it is seen that the scores generally increase when looking at both the midterm and final averages of the classes, and at the differences. In particular, the success of the mechatronics students is obvious, increasing by 56% and 10% for the midterm and final exams, respectively. The reason for the success of the students in the mechatronics technology program is higher because the majority of the students of the mechatronics technology come from the fields of electrical electronics and mechatronics in vocational high schools, because they have a better knowledge of basic electrical electronics and some of them have little prior knowledge about microcontrollers. Participants, who have experienced the real-life equipment module, have responded to say that they have learned a great deal about microcontrollers and have collected the relevant information from their applications to solve real engineering problem. As the microcontrollers experiment set is simple and modular, the students stated that their concerns such as using the wrong circuit element, damaging the experiment set and not being able to recognize the circuit elements are eliminated and it is also enjoyable to practice on the experiment set.

A. Limitations
This study is limited with some factors. First, it limited with two consecutive periods. To have the more information about participants' scores, the periods numbers can be increased. Second, the participants also joined the course only from three program; the program number should be increased and be diversified especially technical branch taking account. Additionally, the study was confined to microcontroller reallife equipment.

B. Future work
This study will be diversified by designing experiment modules of various machine controls, temperature sensor applications, humidity sensor applications and serial communication applications. Since the importance of distance education has increased greatly as a result of the pandemic, elaboratory applications should be adopted and developed for effective use in the courses with the applications. With the elaboratory, in schools providing formal education, the opportunity to perform experiments that cannot be done during the lesson will also be provided. Also e-laboratory applications will enable and encourage the students to practice and consolidate their knowledge with ease. In that context, our next target will be to transform our microcontroller training set into e-laboratory application training by increasing the number of experiment modules. So, the students, will be able to perform their laboratory applications from any environment with internet by using their personal computers without coming to the laboratory environment.

IV. CONCLUSION
Although microcontroller-training kits currently are used in various simulation programs and experiment sets, the problem is that the microcontroller experiment sets are not similar to the circuit diagrams that are used by the students in the simulation programs. In this study, microcontroller training sets and modules were designed with a new approach to teach the microcontroller lessons better in engineering-based departments. To make the learning activities more permanent, basic microprocessor training modules compatible with the visuals of the circuit drawings in the ISIS simulation program used in microcontroller training were provided in the designed training set. Because the application set does not require wiring, students do not waste time on circuit installation in their experimental studies. In addition, malfunctions due to incorrect cable connections are prevented. After the users draw the circuits they create in the ISIS simulation program, they can easily create their circuits by attaching the respective modules in the experiment set to the connection points on the main circuit board. The complicated circuit connections have been removed from the center of the set, resulting in a simpler training set. Because the design of the training set is simple,


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Computer Science
Medicine

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Observer-Based Event-Triggered Predictive Control for Networked Control Systems under DoS Attacks

This paper studies the problem of DoS attack defense based on static observer-based event-triggered predictive control in networked control systems (NCSs). First, under the conditions of limited network bandwidth resources and the incomplete observability of the state of the system, we introduce the event-triggered function to provide a discrete event-triggered transmission scheme for the observer. Then, we analyze denial-of-service (DoS) attacks that occur on the network transmission channel. Using the above-mentioned event-triggered scheme, a novel class of predictive control algorithms is designed on the control node to proactively save network bandwidth and compensate for DoS attacks, which ensures the stability of NCSs. Meanwhile, a closed-loop system with an observer-based event-triggered predictive control scheme for analysis is created. Through linear matrix inequality (LMI) and the Lyapunov function method, the design of the controller, observer and event-triggered matrices is established, and the stability of the scheme is analyzed. The results show that the proposed solution can effectively compensate DoS attacks and save network bandwidth resources by combining event-triggered mechanisms. Finally, a smart grid simulation example is employed to verify the feasibility and effectiveness of the scheme’s defense against DoS attacks.

Introduction
In recent years, with the development of computer networks and wireless communication technology, the rapid development of network control systems (NCSs) has led to a new round of industry change. With the emergence of 5G technology, more control systems can be combined with networks, and remote closed-loop NCSs can be formed through the network transmission of signals, which has been widely used in actual production [1][2][3]. Due to its wide range of applications, the stability and security of NCSs have attracted much attention in the academic community [4][5][6][7][8].
The combination of networks and control systems greatly improves the flexibility of all connected system devices. In other words, all system equipment can be connected through a wired or wireless network, replacing the original point-to-point control structure [9][10][11]. In [12][13][14] , the authors analyzed the modeling and design problems of NCSs. NCSs mainly exchange control information through the network. Because the network is introduced during the control loop, a series of network problems (limited bandwidth resources, data dropout, etc.) are introduced into the control system, which greatly methods to solve the corresponding problems of event-triggered matrices, observer gain matrices and controller matrices. This work represents a significant expansion of previous results involving predictive control (PC) and the event-trigger mechanism (ET) under a DoS attack. The advantages of the proposed control defense scheme are fourfold: (1) Our method is very different to that in the works of networked PCs [32][33][34] which have used time-triggered communication schemes. This paper adopts event-triggered predictive communication schemes to design a controller. Whether the observer's state measurement information is sent depends on the error between the current observer state and the observer state of the most recently sent information. The event-triggered generator on the controller side greatly reduces the size of the sent predictive control sequences, greatly reduces the occupation of bandwidth resources and can also meet the needs of control performance [35]. (2) Compared with the existing predictive control compensation scheme for DoS attacks [7], another advantage of the OB-ETPC scheme adopted in this paper is the combination of the advantages of PC and ET [27,[36][37][38][39][40][41][42]. With the combination of a model and static observer, it can cope with the problem that state information cannot be obtained directly and can also actively compensate for data packet dropout due to DoS attacks and greatly improve the stability of NCSs under DoS attacks. (3) Compared with the latest DoS attack compensation scheme, the method in [27] only considers DoS attacks from the controller to the actuator side. In real-life scenarios, the attack from the sensor to controller side is often through a network link. In this paper, the novel OB-ETPC solves the problem of DoS attacks on both the sensor-to-controller and controller-to-actuator sides, which is more in line with real-life scenarios. (4) The OB-ETPC is established to actively compensate for DoS attacks in NCSs. The observer gain matrix L and controller gain matrix K are co-designed based on the Lyapunov function method, and related criteria for event-triggered matrices are proposed based on linear matrix inequalities (LMIs). The remainder of this paper is organized as follows. Section 2 deals with the problem descriptions and preliminaries. Section 3 considers OB-ETPC and the stability analysis of NCSs under DoS attacks. Section 4 verifies the feasibility of OB-ETPC under DoS attacks through a simulation example. We draw conclusions in Section 5.
All notations used in this paper are defined in the Table 1. Table 1. All notations in this paper.
Notations Definitions The control vector.
The device output vector.
The state vector of the observer.
The output vector of the observer.
The state vector of the predictive control generator.
The moment at which the predictive control generator that successfully receives the data.

A, B and C
The appropriate dimension matrices of the system. L The gain matrix of the observer. K The feedback gain matrix.
The period of DoS attacks. n ∈ R The number of the DoS attack cycle.
A given positive integer. P and Q The symmetrical positive definite matrices.

Problem Descriptions and Preliminaries
In this paper, we will study an observer-based state feedback networked control system under DoS attacks, as shown in Figure 1. The sensor component and the control component and the control component and the actuator component are connected through the network. Due to the openness and vulnerability of the network, NCSs are vulnerable to DoS attacks [43,44].
It is assumed that the dynamic evolution law of the controlled plant can be described by the following discrete system: where x(t) ∈ R n represents the state vector, u(t) ∈ R m represents the control vector and y(t) ∈ R q represents the device output vector. A, B and C are the appropriate dimension matrices of system (1) and K is the feedback gain matrix (to be solved below). The initial state of system (1) is x(t 0 ) = x(0).

Description of Each Component
(1) Sensor: The high-sensitivity sensor sends the output signal from plant to the observer [45].
(2) Observer: In reality, most systems cannot directly obtain the system's state vector x(t).
Using u(t) = Cx(t) to analyze the problem is restrictive and inaccurate. Therefore, in order to estimate plant state information, the observer is introduced into the NCSs. The full-dimension state observer is , where x(t) ∈ R n is the state vector of the observer, y(t) ∈ R q is the output vector of the observer and L is the gain matrix of the observer. We define δ(t) as the observer state error. Then, and the observer error system can be described by (3) Event Generator 1: Due to the limitation of network bandwidth resources, in order to reduce the transmission of data packets, prevent network congestion and improve the utilization of network bandwidth resources and the performance of NCSs, Event Generator 1 is designed on the sensor side to determine whether data packets need to be transmitted to the controller side [46].
In this paper, we first introduce the event-triggered scheme in Event Generator 1 and assume that the time to trigger the Event Generator 1 is t k (k = 1, 2, ..., ); then, the observer state information which is transmitted at this time is x(t k ). The next trigger moment is where µ > 0 is a given scalar, M is a given positive integer, and Φ is a positive definite weight matrix. According to the above condition (5), the next trigger time is determined by the current observer state x(t k + r) and the observer state x(t k ) at the latest trigger time, µ and Φ. Therefore, for µ > 0 and Φ > 0, if f (t k + r, t k ) ≤ 0, the state data packets at t k + r need not be transmitted.
In other words, the embedded trigger condition of the Event Generator 1 is When the trigger condition (7) is satisfied, the observer's state information and state error are transmitted through the network and released to the controller.

Remark 1.
Reducing network bandwidth consumption through event-driven control (ETC) has been extensively studied in [16,[29][30][31]. Thus, ETC provides a very promising option to solve the bandwidth resource problem of NCSs under DoS attacks.

Remark 2.
The data packet which is transmitted from the observer to the controller includes x(t k ) and δ(t k ).
Remark 3. M is the upper limit of the trigger time interval given by us to prevent long-term non-triggering from affecting the stability of the system.
(4) Predictive control generator: Combined with the model-based event-triggered predictive control (MB-ETPC) system, the plant's predictive model is introduced on the control side. The predictive model is used to actively compensate a DoS attack and generate corresponding predictive control sequences. Then, Event Generator 2 is introduced at the control side, which is used to reduce the sending size of the predictive control sequences and further reduce the occupation of bandwidth resources. The predictive control sequences that trigger Event Generator 2 are packaged into a single data packet and sent to the actuator side through the network. (5) Buffer: The buffers are used to store the incoming data packets. (6) Zero-order holder (ZOH): The ZOH is used to choose a suitable control signal with a hold event interval of Ω = [t s i , t s i+1 ). t s i is the moment that the predictive control generator successfully receives the data. (7) Actuator: The function of the actuator is to receive the control signal from the ZOH and control the plant.
In order to facilitate the analysis, we make the following assumptions regarding the above OB-ETPC system: Assumption 1. System (1) performs isochronous sampling. The sampling time is h, and all data packets are time-stamped.

Assumption 2.
The sensor is time-driven, and the predictive controller and actuator are event-driven. Assumption 3. This paper does not consider the time delay of the system and the delay of the transmission process.

Assumption 4. Assume that (A, B) is completely controllable and (A, C) is completely observable.
Assumption 5. x(t 0 ) and δ(t 0 ) are successfully sent at the initial moment t 0 from the observer to the controller.

DoS Attack Description
Denial-of-service (DoS) attacks are simple and effective attacks against a server. The purpose of DoS attacks is to allow the attacked host and server to deny normal users access and disrupt the normal operation of the system. Internet users cannot reach the attacked server and host, causing the server to fail [47]. DoS attacks occur on the sensor-to-controller and controller-to-actuator communication channels. Under DoS attacks, the network control systems will become unstable due to the lack of feedback measurement signals and control signals. Several typical examples of defenses against DoS attacks on modern NCSs are as follows: the United States specifically established the "National Infrastructure Protection Plan" in 2006 and the "Control System Security Plan (CSSP)" in 2010 to incorporate the protection of related national infrastructure control systems into national strategic plans, the European Union released the "European Program for Critical Infrastructure Protection (EPCIP)" in 2013 and the Ministry of Industry and Information Technology (MIIT) in China issued the "Notice on Strengthening the Information Security Management of Industrial Control Systems" in September 2011 [48].
Due to DoS attacks affecting the communication channel, at this time, the observer state data packets released to the control end by Event Generator 1 and the system control sequence packets sent to the actuator side will suffer data dropout due to the DoS attacks. The information transmission under DoS attacks is shown in Figure 2. This paper considers periodic DoS attacks of a variable duration, which is a more general approach. According to [49], the model of DoS attacks is described as follows: where n ∈ R represents the number of the DoS attack cycle, n∈R [nT, nT + T o f f ) represents the time interval without DoS attacks and n∈R [nT + T o f f , nT + T) represents the time interval of DoS attacks; see Figure 3. The main aim of this paper is to use the idea of predictive control to actively compensate for the loss of triggered data packets due to DoS attacks. Based on the received observer state signal x(t s i ), the prediction controller not only needs to calculate the current control signal u(t s i ) but also to perform continuous control serial prediction based on the current observer state signal. The generated data packets U t s i are stored in Buffer 2. The ZOH chooses the suitable control signal to control the plant, which plays an active role in compensating for data packet loss due to DoS attacks.

Remark 4.
In the process of DoS attack compensation, the ZOH adopts a time-driven mechanism. The ZOH continuously sends the u(t s i ) of Buffer 2 to the executor until a split-second before t s i+1 . If the packet in Buffer 2 is not updated before t s i+1 , the ZOH will send the data u( t s i+1 |t s i ) of the latest packet in Buffer 2 to the actuator continuously, and so on, until the packet of Buffer 2 is updated. If the packet in Buffer 2 is updated, the new packet is used to perform the above compensation mechanism.
Remark 5. At present, several design methods for network NCSs schemes have been reported in [16][17][18] (except for a few works [19][20][21][22]24,25] ). Most of the above design schemes do not consider the security of network NCSs but only discuss the design methods of NCSs schemes. Therefore, exploring NCSs compensation under DoS attack is particularly important to solve the security problem of NCSs. Assumption 6. DoS attacks lead to a data packet dropout rate of 100% . Assumption 7. This paper considers periodic DoS attacks, and the period of DoS attacks is T. Because the duration of DoS attacks is variable, we define a real number T min

Assumption 8.
The time interval between two adjacent DoS attacks is greater than M.

OB-ETPC of NCSs under DoS Attacks
Most of the currently researched DoS attack compensation predictive controllers directly compensate for data packet loss by predicting the dynamic evolution of NCSs under a predictive event-driven mechanism. In order to prevent DoS attacks, this paper uses a new predictive controller which combines ET and PC. We assume that the predictive model of the plant is known. The OB-ETPC actively compensates for DoS attacks to ensure system stability and ultimately achieve defense against DoS attacks. Therefore, the structure of the OB-ETPC controller design is shown in Figure 4.
Due to the existence of DoS attacks, the observer's state information { x(t k )} ∞ k=1 triggered by Event Generator 1 cannot be completely transmitted because of data dropout. Thus, we introduce to present the observer's state information which is successfully received at times

Remark 6.
Compared with the latest DoS attack compensation scheme [27], when network communication is introduced from the sensor to controller, the proposed OB-ETPC solves the DoS attack problem from the sensor to controller and controller to actuator.

Remark 7.
Based on Remark 2 and the existence of DoS attacks, we define {δ(t s i )} ∞ i=1 to present the observer's state error which is successfully received at time Remark 8. Based on Assumption 7, Assumption 8 and the event-triggering condition (5), the times t s i+1 and t s i of two successful transmissions satisfy the following relationship: t s i+1 − t s i ≤ 2 × M + p.
The predictive model system is Next, we will explain in detail the use of OB-ETPC to prevent DoS attacks and actively compensate for state data packet loss due to DoS attacks. Due to the impact of DoS attacks, the moment of the successfully received from observer is t s i . As long as the observer's state x(t s i ) is successfully received by the predictive controller, it will be predicted by the predictive model system (9). The predictive model will perform prediction to obtain the corresponding predictive control sequences and actively compensate for the DoS attack. The closed-loop state prediction at the future trigger moment is as follow: . . .
where δ(t s i + j|t s i ) = (A − LC) j δ(t s i ) and δ( t s i+m + j|t s i ) = (A − LC) j δ( t s i+m |t s i ).
In order to reduce the size of the predictive control sequences that need to be sent, Event Generator 2 is introduced into the predictive control generator. t s i+1 is the first predictive event-triggered moment: and t s i+m+1 = t s i+m + min{r s i+m , M}, m ∈ {1, 2, . . . , l i } , where µ and Φ are given in condition (5). Therefore, the predicted moment of transmission is T s i = t s i+1 , t s i+2 , . . . , t s i+l i .

Remark 9. Based on Remarks 3, 8 and Assumptions
Then, l i predictive event-triggered states are packed into X(t s i ).
In order to fully respond to DoS attacks, the controller generates l i predictive control sequences based on the predictive event-triggered states X(t s i ).
According to the corresponding predictive controller law u(t) = Kx(t), the controller's predictive control can be obtained: u( t s i+1 |t s i ) = K x( t s i+1 |t s i ), (22) . . . (23) u( t s i+l i |t s i ) = K x( t s i+l i |t s i ).
Then control sequences are generated as: The generated l i predictive control signals are stored in Buffer 2 for the ZOH to select a suitable control input signal. Then, the ZOH sends the selected control input signal to the actuator to complete the defense against the DoS attack.
Compared with other PC approaches [32] without Event Generator 2, all predicted control sequences will be packed, and the predicted control sequences to be sent are U t s i = [u(t s i ), u(t s i + 1|t s i ), u(t s i + 2|t s i ), . . . , u(t s i + 2 × M + p|t s i )]. Obviously, after Event Generator 2 is added, the size of the predictive control sequences to be transmitted is greatly reduced, and the occupation of bandwidth resources is reduced.

The Closed-Loop System
Lemma 1. Comparing the observer system (2) and predictive model system (9), it is necessary to provide the proof for the following relationships.
x( t s i+h ) = x(t s i+h ), According to Remark 7 and Lemma 1, the closed-loop system with DoS attack compensation under event-triggering condition (5) is expressed as Comparing the observer system (32) and predictive model system (33) in the closed-loop system, and to facilitate system analysis and controller design, for t ∈ [t s i , t s i+1 ) ∩ [ t s i+m , t s i+m+1 ), we define e s i (t) = x(t) − x(t s i+m ). According to e s i and δ(t), the above closed-loop system (31)- (37) can be written as Remark 10. Note that t s i+1 = t s i + t s i+1 − t s i ≤ t s i + 2 × M + p < t s i+l i +1 , and so the compensation selected from Buffer 2 by the ZOH is used to compensate for DoS attacks when t ∈ [t s i , t s i+1 ).
Remark 11. Comparing event-triggered conditions (5) and (20), they are found to be consistent. Based on Remark 9, no event is triggered when t ∈ [t s i , t s i+1 ) ∩ [ t s i+m , t s i+m+1 ) [41]. Thus, based on event-triggering condition (5) and e s i , the following inequality needs to be followed:

Stability Analysis
In this subsection, the design method of the state feedback controller gain matrix K, observer gain matrix L and triggering parameter Φ in condition (5) will be given.

Proof.
To connect x(t) and δ(t), we choose an appropriate Lyapunov function as where P and Q are symmetric positive definite matrices. When t ∈ [t s i , t s i+1 ) ∩ [ t s i+m , t s i+m+1 ), calculating the difference of V(x(t), δ(t)) along the system (38)- (41) and taking the inequalities in condition (42) into account yields that By using Schur's complement, if the following inequality is satisfied, ∆V(x(t), δ(t)) < 0 can be concluded.
However, the above inequality is not in the form of LMI. To reduce it to a linear matrix inequality, we perform pre and post-multiplying (44) with diag{ P −1 , P −1 , I, P −1 , P −1 , I }.
The above matrix inequality is transformed into the following linear matrix inequality form: We define P P −1 , Φ P −1 ΦP −1 , X KP −1 , G QL. According to the Lyapunov stability theory, we find that if the LMI in (43) holds, then the closed-loop system (38)-(41) is asymptotically stable. This completes the proof.

Remark 12.
The main purpose of this paper is to apply the OB-ETPC to compensate for DoS attacks. Unlike the work in [50], this paper introduces two event-triggered generators that compensate for the DoS attack while saving a large amount of bandwidth resources.

Simulation Example
In this section, we apply observer-based event-triggered predictive control to the smart grid example with a four-bus model of the distribution test feeders under DoS attacks [50]. The relevant parameters of the system can be found in [50,51]. The sampling time of the system is h = 0.02s, the DoS attack cycle is T = 2s and the trigger parameter is µ = 0.08.
Other details of the system's parameters can be found in [50]. Then, the matrix C is chosen as C = 1.0000 2.0000 0.0000 0.5000 .
According to Theorem 1, using MATLAB to solve the corresponding linear matrix inequality (LMI), the corresponding controller gain matrix K, observer gain matrix L and event-triggering matrix Φ are obtained as follows: The effectiveness of OB-ETPC in the defense of DoS attacks is demonstrated by comparing the experimental results of three simulation cases under DoS attacks with different durations and two other mainstream DoS attack compensation schemes based on time-triggered predictive control (TTPC) [52][53][54] and event-triggered control (ETC) [27,48,49]. Assume that the plant initial state is

Case 1.
In this case, based on the controller gain matrix K and observer gain matrix L obtained by the above OB-ETPC and based on TTPC [52][53][54], the state responses and event intervals of the system with DoS attack are shown in Figure 5 and Figure 6, respectively. As shown in Figures 5 and 6, we can clearly see that both OB-ETPC-based or TTPC-based methods are able to make the system state stable when encountering DoS attacks. In this case, based on the OB-ETPC and the TTPC, all packet losses due to DoS attacks (p = 1 s or p = 1.5 s) are fully compensated. From Table 2 we can see that, based on OB-ETPC, there are 154 triggered moments within 500 sampling times, and the average triggered time interval is 0.0649 s. Based on the TTPC, the system has 500 triggered moments when compensating for DoS attacks, and the average triggered time interval is 0.02 s. Moreover, it is also clear from the data in the Table 2 that the amount of data packets required to stabilize the OB-ETPC-based system is 114, while the amount of data packets required to stabilize the TTPC-based system is up to 380 when encountering weak DoS attacks. When encountering strong DoS attacks, the OB-ETPC-based system requires 54 data packets for stability, while the TTPC-based system requires 130 data packets for stability. Thus, the event-triggered mechanism not only does not degrade the performance of the system, but also greatly reduces the network bandwidth resource consumption. Case 2. In this case, we assume that p = 1 s is used. Based on the controller gain matrix K and observer gain matrix L obtained by the above OB-ETPC and based on the ETC [27,48,49], the state responses and event intervals of the system are shown in Figure 7 and Figure 8, respectively.  As shown in Figures 7 and 8, the system can be seen to experience a weak DoS attack. From Table 2 we can see that, based on OB-ETPC, there are 154 triggered moments and the average triggered time interval is 0.0649 s. In this case, based on ETC, there are 143 triggered moments and the average triggered time interval is 0.0699 s. Because the simulation case is performed in the upper bound of the weak DoS attack, according to Assumption 7, the ETC-based method can defend against DoS attacks with an arbitrary duration in the weak attack duration range and remain stable after a period of time.
Case 3. In this case, we assume that p = 1.5 s is used. Based on the controller gain matrix K and observer gain matrix L obtained by the above OB-ETPC and based on the ETC [27,48,49], the state responses and event intervals of the system are shown in Figure 9 and Figure 10, respectively.  As shown in Figures 9 and 10, the system can be seen to experience strong DoS attacks. From Table 2, we can see that, based on OB-ETPC, there are 154 triggered moments and the average triggered time interval is 0.0649 s. In this case, based on ETC, there are 117 triggered moments and the average triggered time interval is 0.0855 s. In this case, the ETC-based method cannot defend against strong DoS attacks and the system loses stability. However, since OB-ETPC can fully compensate for DoS attacks, the system remains stable after encountering strong DoS attacks.
Remark 13. The data numbers represent the amount of data successfully transmitted to the actuator.
Compared with TTPC [52][53][54], the above results verify the effectiveness of our proposed OB-ETPC in reducing bandwidth resource consumption. Furthermore, compared to ETC [27,48,49], the above results verify the feasibility of our proposed OB-ETPC approach to defend against DoS attacks. In summary, the OB-ETPC approach proposed in this paper can make up for the deficiency of different types of NCS compensation schemes under DoS attacks. In the case that the system state cannot be completely measured, OB-ETPC can ensure the stability of NCSs and reduce the occupancy of bandwidth resources, which cannot be achieved by other methods at present.

Conclusions
This paper studies the problem of event-triggered control based on a static observer in networked control systems (NCSs) under DoS attacks. The OB-ETPC is a new method to solve the problem of DoS attacks. The results show that the introduction of an observer and predictive model in the system has a significant effect on the defense against DoS attacks. The establishment of an event-triggered scheme greatly reduces the size of the predictive control sequence compensation packet. In addition, a ZOH is constructed in the actuator node to actively compensate for data packet loss due to DoS attacks. The OB-ETPC is an active compensation method for NCSs under DoS attacks that combines event-triggered conditions, robust controller-feedback gain and observer gain. The practical application example shows that this method can not only actively compensate for DoS attacks but can also reduce the bandwidth occupancy while maintaining the stability of the NCSs.
In future research, it would be beneficial to extend the OB-ETPC to the defense against DoS attack of distributed NCSs. In distributed NCSs, it is necessary to consider the impact of network-induced delay, data packet dropout and DoS attacks on the closed-loop system. In addition, the OB-ETPC approach would also be of great significance for solving noise problems [55].


s2fieldsofstudy:
Computer Science
Engineering

text:
ELMAN-RECURRENT NEURAL NETWORK FOR LOAD SHEDDING OPTIMIZATION

Load shedding plays a key part in the avoidance of the power system outage. The frequency and voltage fluidity leads to the spread of a power system into sub-systems and leads to the outage as well as the severe breakdown of the system utility. In recent years, Neural networks have been very victorious in several signal processing and control applications. Recurrent Neural networks are capable of handling complex and non-linear problems. This paper provides an algorithm for load shedding using ELMAN Recurrent Neural Networks (RNN). Elman has proposed a partially RNN, where the feedforward connections are modifiable and the recurrent connections are fixed. The research is implemented in MATLAB and the performance is tested with a 6 bus system. The results are compared with the Genetic Algorithm (GA), Combining Genetic Algorithm with Feed Forward Neural Network (hybrid) and RNN. The proposed method is capable of assigning load releases needed and more efficient than other methods.

INTRODUCTION
Power systems are complex multicomponent dynamic systems in which the system characteristics fluctuate with varying loads and varying generation schedules [1]. Large disorders can cause system and voltage instability. Frequency instability is like a decrease in drastic frequency can cause the system to experience total blackouts. One of the strategies to anticipate possibilities of the drastic decrease in frequency is released some of the burden borne by the system. After some loads are released, loads are borne by plants that are still operating will decreases, and frequency will be able to return to normal condition immediately after a balance occurs between generation and loading. Release the load must be carried out immediately at the time of frequency the system began to decline dramatically. If there is a disturbance in the system cause the available power cannot serving loads, for example, caused by existence generating unit that trips, so to prevent collapse occurs, the system needs to do load Shedding Maintaining the power system frequency within the permissible limits is an important control task, which in normal conditions is carried out using load frequency control [2]. However, when a sudden power deficit occurs as a result of outage of large generating units or islanding of some parts of power system, even if the existing generating units have enough spinning reserve to supply the demand, their response is not rapid enough to stop the frequency excursion and prevent the operation of generating units' protective relays. The condition may lead to an outage of some of the generating units. Consequently, the outage of a generating unit can worsen the situation and decrease the frequency to a lower level; therefore, the relays of other generating units might trip and lead to power system blackout [3] [4].
The process of releasing several loads with a degrade priority is to maintain the stability and reliability of the existing system. Load shedding schemes are needed to maintain power system stability. It is a common habit for electric companies to run loads shedding procedure with adjusting under-frequency relays to decide the predestinated load in various shedding tread when the frequency crashes fall from setting values. The transient stability explores all the probability the problem of the external equipment power system has to be carried out to obtain an expeditious load shedding procedure Some of the latest methods have been applied to the issue of load release with the hope of obtaining efficient load release. The situation is to maintain a steady state of the power system. A computationally easy algorithm has been progressed. Arya et al. have a novel method based on the sensitivities of the minimum eigenvalue of load flow Jacobian [5]. Arya et al. have differential evolution (DE methods for load shedding stress stability considerations [6]. Yan Xu et al. have a reserved approach using Parallel-Differential Evolution (P-DE) to optimal load shedding for avoiding voltage collapse [7]. Hamid and Musirin have a fuzzy logic method as a mastermind that is recommended an algorithm to uncover the suitable load buses for the goal of load shedding reckoning multi-contingencies [8]. Hong [11].
The recurrent neural network method has been applied to various problems in recent years. The technique can be split into two primary categories: full and partially one. Recurrent neural networks were humbly identified in the late 1980s to learn character sequences. Various studies have developed this method.
This study presents a recurrent neural network called an Elman network. The Elman network is called a simple recurrent network (SRN) because it is similar to a fully connected network, but the number and complexity of interconnections are lower than in an RNN [12] [13]. The advantage of RNN is a Neural Network with a feedback facility to its neurons and other neurons. The information flow from input has direction plural (multidirectional). The RNN output does not only depend on the current input but also depends on the input conditions for the past. This condition is intended to accommodate past events included in the computational process and is important for a quite complicated problem and the response of the NN output is related to time variation (time-varying). RNN has a sensitivity to time with memory conditions past This research will present optimization load shedding using the Recurrent Neural Network (RNN). The results compared with GA and Hybrid methods from previous studies by [14]. The hybrid method by [14] is a combined Genetic Algorithm (GA) and Neural Network (NN). The neural network by [14] was used Feed Forward Neural Network (FFNN).

METHOD
An Elman RNN is a network with an initial configuration based on a regular feedforward neural network. As is well-known, in an FFNN, the information moves in only one direction, forward, from the input nodes, through the hidden nodes, and to the output nodes without cycles or loops. The main difference between the FFNN and the Elman network, because the latter has a layer called the context layer. The neurons in the context layer, called context neurons, hold a copy of the outputs that are given by the neurons of the hidden layer to the output layer. It means that in the following computing step, information that was given as an output by the hidden layer is used as new input information for this layer.
The condition is intended to accommodate past events included in the computing process. This is important for fairly complex problems, and NN output responses are related to time-varying so that NN has a time sensitivity with past conditions memory. The Structure of Recurrent Neural Network shown in Figure 1. The j and k signs present from j and k neurons, each neuron from the input corresponds to weight Wji. The Layers 1 output matches the Wji. Whereas Oj is related to Wkj. Layer 1 The error function is defined: Which xk(t) is output system and n is output neuron. Weight Wji and weight Wkj can be adjusted by using the steepest descent algorithm.
ή1is learning rate from RNN. Gradient error E (t) of weight Wji and weight Wkj are:  This study adjusted quantitative methods with historical data to estimate control values. Processing and analyzing data are taken from time to time. Data is taken and analyzed in sequence. The step of this research is explained in Figure 2.

Related Work
A previous study conducted by [13], the load shedding tested with 6 bus systems was obtained using a GA and Hybrid method. The result can be seen in Table 1, Table 2 and Table  3.
The performance of the proposed hybrid method is compared with the base case and GA results. The performance comparison of six bus systems, the minimum eigenvalue and the sensitivity of eigenvalues are compared.

RESULTS AND DISCUSSION
The bus and line data of 6 bus systems are referred to in [9]. The system consists of 2 generator buses and four load buses. The buses 3, 4, 5 and 6 are load buses. Data research was used to conduct NN training. Table 4 is the parameter used using RNN. Figure 3 is RNN training using data six bus system  The neural network training has five hidden layers, and the Hyperbolic Tangent Sigmoid function transfer. The learning rate is used 0.1 with 0.2 momentum. Figure 3 shows the results that completed in 24 iterations of the 1000 epoch limit. The training performance results are 0.000996 with a 0.124 gradient.
The proposed RNN algorithm has stable voltage stability and a minimum load shedding value. Table 5 shows the eigenvalue sensitivity ratio of each method. The Sensitivity eigenvalue can be seen in Figure 4. Next, the results are measured using Mean absolute percentage error (MAPE). The MAPE is a measure of how accurate a forecast system. Table 6 shows a comparison of calculations from MAPE and is illustrated in Figure 5.     Table 7 shows calculations of bus voltage, real power, and load values. The results on the 6 bus system show that the RNN algorithm is superior to voltage stability and the lowest load shedding value. Figure 6 shows a comparison before and after load shedding. Generally, the proposed method has a better average than other methods.

CONCLUSION
The performance of the proposed method is measured and applied using 6 bus systems. The proposed RNN method is compared to GA and the Hybrid Method that has been done before that is used as a combination of backpropagations neural network and GA. Data obtained the average error value of load shedding using the RNN method is 1.76%. The result is better than other methods. There is 10.55% using the hybrid method and 11.99% using the GA method.
The average error value of the voltage obtained using the RNN method is 0.47%. This result is better than other methods. There are 3,609% using the hybrid method and 1,751% using the GA method. A comparison in this study shows that the proposed RNN has superiority at minimum voltage deviations and load shedding.


HYBRID PSOGSA TECHNIQUE FOR SOLVING DYNAMIC ECONOMIC EMISSION DISPATCH PROBLEM

Article history: Received: 18.9.2018. Received in revised form: 6.5.2019. Accepted: 14.5.2019. In this paper, a new hybrid population-based algorithm is proposed with the combining of particle swarm optimization (PSO) and gravitational search algorithm (GSA) techniques. The main idea is to integrate the ability of exploration in PSO with the ability of exploration in the GSA to synthesize both algorithms’ strength. The new algorithm is implemented to the dynamic economic emission dispatch (DEED) problem to minimize both fuel cost and emission simultaneously under a set of constraints. To demonstrate the efficiency of the proposed algorithm, a 5-unit test system is used. The results show the effectiveness and superiority of the proposed method when compared to the results of other optimization algorithms reported in the literature.

Introduction
The fundamental objective of dynamic economic dispatch (DED) problem of electric power generation is to schedule the committed generating unit outputs in order to meet the predicted load demand with minimum operating cost, while satisfying all system inequality and equality constraints [1,2]. Therefore, the DED problem is a highly constrained large-scale nonlinear optimization problem. The valve-point effect introduces ripples in the heat-rate curves and makes the objective function non-convex, discontinuous, and with multiple minima [3][4][5]. The fuel cost function with valve point loadings in the generating units is the accurate model of the DED problem [6,7]. Nowadays, strategically utilizing available resources and achieving electricity at cheap rates without sacrificing the social benefits is of major significance. The environmental pollution plays a major role as it had a major threat on the human society. Hence, it became compulsory to deliver electricity at a minimum cost as well as to maintain minimum level of emissions. The lowest emissions are considered as one of the objectives with combined economic and emission dispatch problems, along with the cost economy. Atmospheric pollution due to release of gases such as nitrogen oxides (NOX), carbon dioxide (CO2), and sulphur oxides (SOX) into atmosphere by fossil-fuel based electric power stations affects not only humans, but also other forms of life such as birds, animals, plants and fish, while causing global warming too [8][9][10][11]. Generating units may have certain prohibited operating zones (POZs) due to faults in the machines themselves or instability concerns or the valve point effect. Hence, considering the effect of valve-points and POZs in generators' cost function makes the economic dispatch a non-convex and non-smooth optimization problem [12]. The dispatching of emission is a short-term option where the emission, in addition to fuel cost objective, is to be optimized. Thus, the DEED problem can be handled as a multi-objective optimization problem and requires only small modification to include emission. Hence, the DEED problem can be converted to a single objective problem by linear combination of various objectives using different weights. The important characteristic of the weighted sum method is that different pareto-optimal solutions could be obtained by varying the weights [13]. In [14][15][16] the static economic dispatch problem with prohibited operating zones has been solved. A number of reported works has considered the prohibited operating zones in the DED problem [17][18][19][20], however, the emission has not considered in these papers. PSO is an evolutionary computation technique which is proposed by Kennedy and Eberhart [21,22]. The main difficulty for classic PSO is its sensitivity to the choice of parameters, and they also premature convergence, which might occur when the particle and group best solutions are trapped into local minimums during the search process. One of the recently improved heuristic algorithms is the GSA based on the Newton's law of gravity and mass interactions. The GSA has been verified as a high quality performance in solving different optimization problems in the literature [23]. The same objective for them is to find the best solution (global optimum) among all possible inputs. To overcome these problem, a heuristic algorithm should be equipped with two major characteristics to ensure finding global optimum. These two main characteristics are exploration and exploitation [24]. The aim of this paper proposes a hybrid PSO-GSA for solving the DEED problem with valve-point effects and prohibited operating zones. The PSO is used to find a near global solution, and the GSA is used as a local search to determine the optimal solution at the final.

Problem formulation
The objective of the DEED problem is to find the optimal schedule of output powers of online generating units with predicted power demands over a certain period of time to meet the power demand at minimum -both operating costs and emissions simultaneously. The objective function of the DEED problem can be formulated as following: where FT is the total operating cost over the whole dispatch period, T is the number of hours in the time horizon, N is the total number of generating units, w1 is weighting factor for economic objective such that its value should be within the range 0 and 1, and w2 is the weighting factor for emission objective which is given by w2 = (1 -w1), and hi is the price penalty factor. Fi,t(Pi,t) and Ei,t(Pi,t) are the generation cost and the amount of emission for unit i at time interval t , and Pi,t is the real power output of generating unit i at time period t. The valve-point effects are taken into consideration in the DEED problem by superimposing the basic quadratic fuel-cost characteristics with the rectified sinusoidal component as follows [12]: where the constants ai, bi, and ci represent generator cost coefficients and ei and fi represent valve-point effect coefficients of the i-th generating unit. Utilization of thermal power plant that consumes fossil fuel is with release of high amounts of NOX, therefore they are strongly requested by the environmental protection agency to reduce their emissions. The NOX emission of the thermal power station having N generating units at interval t in the scheduling horizon is represented by the sum of quadratic and exponential functions of power generation of each unit. The emission due to i-th thermal generating unit can be expressed as where αi ,βi , γi , ηi and δi are emission coefficients of the i-th generating unit. The minimization of the fuel cost and emission are subjected to the following equality and inequality constraints.

Power balance constraint
The total generated real power should be the same as total load demand plus the total line loss.
where PD,t and PL,t are the demand and transmission loss in MW at time interval t, respectively. The transmission loss PL,t can be expressed by using B matrix technique [1] and is defined by (5) as, where Bij is the ij-th element of the loss coefficient square matrix of size N.

Generation limits
The real power output of each generator should lie between minimum and maximum limits.

Ramp rate limits
The ramp-up and ramp-down constraints can be written as (7) and (8), respectively.
where Pi,t and Pi,t-1 are the present and previous real power outputs, respectively. URi and DRi are the ramp-up and ramp-down limits of unit i (in units of MW/time period). To consider the ramp rate limits and real power output limits constraint at the same times, therefore, equations (6), (7) and (8) can be rewritten as follows: ,min , 1 ,

Prohibited operating zones
The prohibited operating zones are the range of real power output of a generator where the operation causes undue vibration of the turbine shaft bearing caused by opening or closing of the steam valve. The prohibited operating zones of the unit can be described as follows: ,min , 3 Meta-heuristic optimization

Overview of the PSO
The particle swarm optimization (PSO) algorithm is introduced by Kennedy and Eberhart based on the social behavior metaphor. In the PSO, a potential solution for a problem is considered as a bird without quality and volume, which is called a particle, flying through a D-dimensional space by adjusting the position in search space according to its own experience and its neighbors. In the PSO, the i-th particle is represented by its position vector xi in the D-dimensional space and its velocity vector vi. In each time step t, the particles calculate their new velocity, then update their position according to equations (11) and (12) respectively.
where t i v is velocity of particle i at iteration t, w is inertia factor, c1 and c2 are accelerating factor, r1 and r2 are positive random number between 0 and 1, pbesti is the best position of particle i, gbest is the best position of the group, wmax and wmin are maximum and minimum of inertia factor, Itermax is maximum iteration, n is number of particles. The PSO begin with randomly placing the particles in a problem space. In each iteration, the velocities of particles are calculated using (11). After defining the ________________________________________________________________________________________________________________________ velocities, position of masses can be calculated as (12). The process of changing particles' position will continue until the stop criteria is reached.

Gravitational search algorithm
The Gravitational Search Algorithm (GSA) is a novel heuristic optimization technique which has been proposed by E. Rashedi et al in 2009 [23]. The basic physical theory based on which GSA is inspired by is the Newton's theory. This algorithm, which is based on the Newtonian physical law of gravity and law of motion, has great potential to be a breakthrough optimization method. In the GSA, consider a system with N agent (mass) in which position of the i-th mass is defined as follows: where d i x is position of the i-th mass in the d-th dimension and n is dimension of the search space. At the specific time t a gravitational force from mass j acts on mass i, and is defined as follows: where G(t) is the gravitational constant at time t, Mi(t) and Mj(t) are the masses of the objects i and j, and ε is a small constant, and Rij(t) is the Euclidean distance between the two objects i and j objects described as follows: The masses of the agents are calculated as following by comparison of fitness: where fiti(t) represents the fitness value of the agent i at time t, best(t) is maximum fitness values of all agents and worst(t) is the minimum fitness.
Randomly initialized gravitational constant G(t) is decreased according to the time as follows: where α and G0 are descending cooefficient and initial value respectively, t is current iteration, and T is maximum number of iterations.
The total force that acts on agent i in the dimension d is described as follows: where randj is a random number interval [0, 1]. According to the law of motion, the acceleration of at is given as follows: Then, the searching strategy can be described by the next velocity and next position of an agent. The next velocity function is the sum of the current velocity and its current acceleration. The current acceleration is described as the initial acceleration calculated from (21). The initial position is calculated from (14) and the initial speed is determined by producing a zero matrix, which has a dim x N dimension (dim: dimension of problem, N: number of agents). Also, the next position function is the sum of the current position and the next velocity of that agent. These functions are shown as follows: were randi is a random number interval [0, 1], () While solving an optimization problem with the GSA at the beginning of the algorithm, every agent is located at a certain point of the search space, which represents a solution to the problem at every unit of time. Next, according to (22) and (23), masses are evaluated, and their next positions are calculated. Then, gravitational constant G, masses M, and acceleration α are calculated through (17)- (19) and (21) and updated at every time cycle. The search process is stopped after a certain amount of time.

The hybrid PSOGSA
The hybrid PSOGSA approach is an integrated approach between PSO and GSA which combines the ability of social thinking (gbest) in PSO with the local search capability of GSA. In order to combine these algorithms, the updated velocity of agent i can be calculated as follows [24]: where Vi(t) is the velocity of agent i at iteration t, cj is a weighting factor, w is a weighting function, rand is a random number between 0 and 1, ai(t) is the acceleration of agent i at iteration t, and gbest is the best solution so far. The updating position of the particles at each iteration is as follows: In the hybrid PSOGSA, at the beginning of the algorithm, all agents are randomly initialized. Each mass (agent) is considered as a candidate solution. After initialization, gravitational force, gravitational constant, and resultant forces among the agents are calculated using (15), (19), and (20) respectively. After that, the acceleration of particles is defined as (21) and updated at every time cycle. After calculating the accelerations and with updating the best solution so far, the velocities of all agents can be calculated using (24). Finally, the positions of the agents are defined as (25). The search process is stopped after a certain amount of time.

The results and discussion
The feasibility of the proposed method is demonstrated on a 5-unit test system for the given scheduled time duration which is divided into 24 intervals. The 5-unit test system data with nonsmooth fuel cost and emission function, B-loss coefficients, and the load demand for 24 intervals are taken from [19,25,26] and are given in Tables 1, 2, and 3, respectively. The algorithms were executed in MATLAB R2015a on a PC with 3.07 GHz CPU and 8-GB RAM. The PSO-GSA parameters used for the simulation are adopted as following: c1 = 0.5, c2 = 1.5, w = rand[0, 1], α = 20 and G0 = 100. The population size N and maximum iteration number T are set to 30 and 100, respectively, for all case studies. Tables 4, 5, and 6, respectively show the optimal solutions of the dynamic economic dispatch (DED, w1=1, w2=0), dynamic economic emission dispatch (DEED, w1=0.5, w2=0.5) and pure dynamic emission dispatch (PDED, w1=0, w2=1).  Table 2. B-loss coefficients (5-unit Table 5 shows that the cost is 45,702.6001 $ which is more than 42,853.3394 $ (in case of DED) and less than 51,953.9046 $ (in case of PDED), and emission is 18,267.1788 lb which is less 22,087.8872 lb (in case of DED) and more than 17,852.9791 lb (in case of PDED). Table 7 shows that, the efficiency of the proposed method compare to other methods for DEED problem at different weighting factors. It appears that both fuel cost and emission are less than the other methods reported in the literature. The table also shows the running time for each step of the process.

Conclusion
In this paper, a new hybrid PSOGSA technique has been applied to solve the non-convex DEED problem of generating units considering the valve-point effects, prohibited operation zones, ramp rate limits and transmission loss. The proposed technique has provided the best solution in the 5-unit test systems and better solution than the previous studies reported in the literature. The simulation results show the high performance of the PSOSGA algorithm on minimizing fuel cost and reduced emission. The analyses of the results are very promising since the main objectives of the proposed technique were achieved. Future studies will focus on multiobjective economic emissions power dispatch considering renewable energy.