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Cyber-Physical Power System (CPPS) refers to a system in which the elements of the internet and the physical power system communicate and work together. With the use of modern communication and information technology, grid monitoring and control have improved. However, the components of a cyber system are extremely vulnerable to cyberattacks via cyber connections due to inadequate cyber security measures. Therefore, an adaptive defence strategy is required for the analysis and mitigation of the coordinated attack. The conventional approach of using an offline controller requires tuning for changes in the operating conditions of the system, which is inappropriate for the modern CPPS. To counter the coordinated attack, a framework that integrates STATCOM based Adaptive Model Predictive Controller with RPME and time delay compensator is proposed. This paper addresses attack impact, detection, and mitigation methods in CPPS. In both time domain and frequency domain simulations the case studies are conducted for three distinct situations namely physical attack, cyberattack, and coordinated attack. Convolutional Neural Network (CNN), Support Vector Machine (SVM), Random Forest (RF), and K Nearest Neighbour (KNN) are four data-driven methods used for the detection of anomalies in PMU measurement data. Simulation studies show that CNN performs better in anomaly detection than other classifiers based on assessed performance metrics. For coordinated attack mitigation the proposed STATCOM based Adaptive Model Predictive Controller with RPME quickly recovers the system than the STATCOM based conventional lead-lag controller. The efficacy of the proposed strategy is validated on the WSCC 3 machine 9 bus system. |
