Contingency Detection in Modern Power Systems: A Stochastic Hybrid System Method
<|reference_start|>Contingency Detection in Modern Power Systems: A Stochastic Hybrid System Method: This paper introduces a new stochastic hybrid system (SHS) framework for contingency detection in modern power systems (MPS). The framework uses stochastic hybrid system representations in state space models to expand and facilitate capability of contingency detection. In typical microgrids (MGs), buses may contain various synchronous generators, renewable generators, controllable loads, battery systems, regular loads, etc. For development of SHS models in power systems, this paper introduces the concept of dynamic and non-dynamic buses. By converting a physical power grid into a virtual linearized state space model and representing contingencies as random switching of system structures and parameters, this paper formulates the contingency detection problem as a joint estimation problem of discrete event and continuous states in stochastic hybrid systems. This method offers unique advantages, including using common measurement signals on voltage and current synchrophasors to detect different types and locations of contingencies, avoiding expensive local direct fault measurements and detecting certain contingencies that cannot be directly measured. The method employs a small and suitably-designed probing signal to sustain the ability of persistent contingency detection. Joint estimation algorithms are presented with their proven convergence and reliability properties. Examples that use an IEEE 5-bus system demonstrate the main ideas and derivation steps. Simulation case studies on an IEEE 33-bus system are used for detecting transmission line faults and sensor interruptions.<|reference_end|>
@article{yuan2024contingency,
    title={Contingency Detection in Modern Power Systems: A Stochastic Hybrid
  System Method},
    author={Shuo Yuan, Le Yi Wang, George Yin, Masoud H. Nazari},
    journal={arXiv preprint arXiv:2402.01547},
    year={2024},
    doi={10.1016/j.segan.2024.101414},
    archivePrefix={arXiv},
    eprint={2402.01547},
    primaryClass={eess.SY cs.SY}
}