Optimizing Power Control in Generation Units: LSTM-Based Machine Learning for Enhanced Stability in Virtual Synchronous Generators

The integration of inverter-based generation units, such as photovoltaic systems, wind turbines, and vehicle-to-grid (V2G) technologies, has introduced new challenges in maintaining power and frequency stability in modern power systems. Virtual Synchronous Generators (VSGs) have emerged as a promising solution to enhance system stability; however, existing control methods often lack the robustness and flexibility needed to address deliberate and unplanned outages effectively. This paper presents a novel approach for optimizing power control in generation units using a Long Short-Term Memory (LSTM)-based machine learning method. The proposed LSTM-based controller provides a fast and real-time response, ensuring robust and flexible performance under varying operational conditions. Unlike traditional controllers, the proposed method effectively handles nonlinearities and uncertainties associated with inverter-based units. Additionally, it effectively balances technical and economic aspects of power system operation by minimizing oscillations and optimizing resource utilization. The proposed approach is benchmarked against conventional control methods through a detailed simulation-based comparative analysis against a linear Model Predictive Control strategy under identical operating conditions. Simulation results indicate that the proposed controller reduces frequency deviations by up to 66.7%, voltage deviations by 62.5%, and total operational cost by approximately 11.3%, while achieving nearly 90% faster dynamic response, validating its effectiveness for modern power systems.