Twin-delayed deep deterministic policy gradient for enhanced power optimization in solar PV-integrated DFIG wind energy systems.

Abstract

The electrical power systems are facing rising challenges of stability and control with increasing share of intermittent renewable energy power sources. This work presents application of Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm in single unified controller for multi-objective control of DFIG-Solar PV system connected to power grid. The commonly used Proportional-Integral (PI) controllers are not suitable to address nonlinearities of single controller based hybrid DFIG and solar PV systems. At times, the latest reinforcement learning-based controllers like DDPG can be erratic and aggressive due to overestimation of the actor's control action. These aggressive actions, which cause overshoot and oscillation, can be overcome by adopting the TD3 algorithm. The TD3 algorithm provides improved learning capabilities and performance by mitigating overestimation by using dual critic networks. A single TD3-based controller is implemented to simultaneously control the Rotor Side Converter (RSC), Grid Side Converter (GSC) and solar PV system integrated at the DC link. OPAL-RT real-time hardware-in-the-loop (HIL) simulation results demonstrate that the TD3 controller achieves a 10.3% reduction in power overshoot, 8% improvement in DC link voltage regulation, 15.3% faster response time, and 16.9% faster settling time compared to conventional PI control, and also outperforms the DDPG-based controller across all metrics.