A Novel Machine Learning-Based Framework for Optimal and Secure Operation of Static VAR Compensators in EAFs

Abstract

A static VAR compensator (SVC) is a critical component for reactive power compensation in electric arc furnaces (EAFs) that is used to relieve the flicker impacts and maintain the voltage level. A weak voltage profile can not only reduce the power-quality services, but can also result in system instability in severe cases. The cybersecurity of EAFs is becoming a significant concern due to their cyber-physical structure. The reliance of SVC controllers on reactive power measurement and network communications has resulted in a cyber-vulnerability point for unauthorized access to the EAF, which can affect its normal operation. This paper addresses concerns about cyber attacks on EAFs, which can cause network communication issues in measurement data for SVCs. Three significant and different types of cyber attacks that are launched on SVC controllers—a replay attack, delay attack, and false data injection attack (FDIA)—were simulated and investigated. In order to stop the activities of cyber attacks, a secured anomaly detection model (ADM) based on a prediction interval is proposed. The proposed model is dependent on a support vector regression and a new smooth cost function for constructing the optimal and symmetrical intervals. A modified algorithm based on teaching–learning-based optimization was developed to adapt the ADM’s parameters during training. The simulation’s outcomes on a genuine dataset showed the strong capability of the proposed model against cyber attacks in EAFs.

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