Enhanced grid stability of DFIG-based wind systems through intelligent reactive power coordination using machine learning-based control

This paper presents an intelligent coordination strategy for enhancing the dynamic performance of doubly-fed induction generator (DFIG) -based wind energy systems through real-time reactive power optimization using Twin Delayed Deep Deterministic Policy Gradient (TD3) reinforcement learning. The proposed Reinforcement Learning Coordinated Transient Controller (RL-CTC) coordinates reactive power sharing between DFIG wind farms and Static Synchronous Compensator (STATCOM) devices, eliminating the need for explicit system models or fixed control parameters. The approach integrates artificial neural network (ANN) -based wind farm placement (with Bus 5 identified as optimal), rotor angle stability-based power system stabilizer (PSS) selection, and voltage stability-based STATCOM sizing. Comprehensive validation on the IEEE 14-bus test system demonstrates a 74. 3% reduction in Sum of Maximum Rotor Angle Deviations (SMRAD), 50. 0% improvement in voltage regulation, 57. 1% reduction in total harmonic distortion, and 32. 1% faster settling times compared to conventional methods. The system maintains frequency deviations within ±0. 25 Hz and achieves full compliance with international grid codes, including Zero, Low, and High Voltage Ride-Through (ZVRT, LVRT, HVRT) requirements. Economic analysis indicates annual operational savings of 945k, representing a 40. 6% improvement over existing control methods. The results confirm the proposed strategy’s effectiveness in improving grid stability, voltage support, and operational efficiency in power systems with high renewable energy penetration.