DDPG algorithm for power optimization and control of solar PV-integrated DFIG wind energy systems

In modern power systems, the integration of multiple renewable energy sources pose significant challenges for system control and optimization. This paper presents a novel approach using Deep Deterministic Policy Gradient (DDPG) algorithm for controlling a solar PV-integrated Doubly Fed Induction Generator (DFIG) wind energy system. Unlike conventional PI controllers, the proposed DDPG-based control strategy provides an adaptive learning capabilities and improved dynamic performance. The control system is implemented on both Rotor Side Converter (RSC) and Grid Side Converter (GSC) with solar PV integration at the DC link. Simulation results demonstrate superior performance in terms of power quality, dynamic response, and system stability compared to conventional PI control methods. The proposed system achieves an overall improvement in annual energy production, better control stability, and enhanced fault ride-through capability.