Machine Learning Algorithms for Secure and Reliable Electric Grid Operations and Control

Abstract

This dissertation develops Machine Learning (ML) algorithms for secure and reliable electric grid operations and control by addressing three related problems. The first part studies real-time event identification using synchrophasor measurements, physics-based modal decomposition, and interpretable classifiers to distinguish generation loss from load loss events. Targeted adversarial attacks are developed to evaluate robustness under both white box and gray box settings, showing that learned event identification models are susceptible to adversarial attacks and that simpler models such as logistic regression are generally more vulnerable than gradient boosting. The second part builds on this vulnerability analysis and focuses on enhancing the security of ML event identification models in a white box adversarial setting. Two mitigation strategies are developed: robust classification through iterative adversarial retraining, and a dual-classifier architecture that combines event classification with attack detection. Numerical results on the synthetic South Carolina 500-bus system show that while robust retraining provides only modest improvement, the dual classifier approach is highly effective, reducing successful undetected attacks to under 0.1%. The third part addresses reliable grid control through a forecast-integrated rolling-horizon Model Predictive Control (MPC) framework for net-demand balancing using Distributed Energy Resource Aggregators (DERAs). Each DERA is modeled as a generalized battery with state-of-charge, power, and ramping constraints, while Linear Regression (LR) and Long Short-TermMemory (LSTM) forecasting models are integrated with MPC to generate real-time allocation policies. Using high-resolution California Independent System Operator (CAISO) net-demand data, results show close tracking of net-demand and reveal clear tradeoffs among forecast horizon, update frequency, and control performance, with LSTM generally benefiting longer time-shifts and LR remaining competitive for shorter update intervals. These three parts show that effective ML for power systems must be accurate, physically grounded, cyber-resilient, and compatible with real-time operational constraints.