Enhancing Power System Stability: A Machine Learning Approach with SVM, Random Forest and Deep Learning Utilizing Wide Area Measurements

As the scale and complexity of modern power systems continue to expand, the importance of system stability has become increasingly critical. To ensure the stable operation of the power system, this paper proposes a power system stability analysis and optimal control strategy based on the Wide Area Measurement System (WAMS). Initially, a novel stability evaluation method is established using WAMS data and machine learning techniques, enabling effective handling of complex power system data and providing accurate stability assessments. Following this, an optimal control strategy is designed based on the evaluation method, aimed at real-time adjustments of power resource allocation to enhance system stability and responsiveness. Simulation analyses confirm the effectiveness and reliability of the proposed approach, indicating that the optimized control strategy significantly improves the system's performance under disturbances. This paper presents an innovative method for the stable operation of integrated power systems, contributing to the enhancement of power supply quality within the grid and ensuring the safe and efficient operation of the overall power system.