A series capacitive reactance compensator (SCRC), using a voltage source converter to inject a controllable voltage in quadrature with the line current of a power network, is capable of rapidly providing a specified and controllable magnitude of capacitive reactance compensation independent of the line current. Moreover, with a suitably designed external controller, the SCRC can also be used to damp low-frequency power oscillations in a power network. Conventionally, linear control techniques are used to design the SCRC external controller around a specific operating point, where the nonlinear system equations are linearized. However, at other operating points its performance degrades. Nonlinear adaptive neuro-controllers offer an attractive approach to overcome this degradation problem. In this paper, an indirect adaptive external neuro-controller (INDAEC) using two radial basis function neural networks (RBFNNs) is proposed to improve the damping performance of an SCRC connected to a power network. This nonlinear INDAEC needs no mathematical model of the SCRC or the power network. It provides the SCRC with improved damping performance over a wide range of system operating conditions. This is shown by results on a single machine infinite bus power system, as well as a multimachine power system
['Wei Qiao', 'Ronald G. Harley']
Indirect Adaptive External Neuro-Control for a Series Capacitive Reactance Compensator Based on a Voltage Source PWM Converter in Damping Power Oscillations