Photovoltaic System MPPT Evaluation Using Classical, Meta-Heuristics, and Reinforcement Learning-Based Controllers: A Comparative Study

Abstract

Maximum power point tracking (MPPT) entails constraining photovoltaic (PV) modules to operate under a specified power condition. It has previously been shown that some meta-heuristic techniques often suffer from steady-state oscillations around maximum points and experience difficulty in adapting to environmental variations, such as irradiation and/or temperature. To address the aforementioned limitation, this work proposed an adaptable reinforcement learning (RL) technique based on a novel deep deterministic policy gradient (DDPG) agent and a reward function. The actor–network top layer uses a sigmoid activation function and the critic–network contains bottleneck layers with non-uniform nodal distributions as well as exponential linear unit (ELU) activation functions in some of the layers. The RL based on DDPG method was compared with Particle Swarm Optimization (PSO) and Perturb-and-Observe (P&O) in order to determine the optimal duty-cycle command needed for controlling the PV modules MPPT. All the investigated systems were implemented in MATLAB/Simulink. The results show that the proposed RL technique based on DDPG agent yielded superior tracking efficiency than all the other approaches. However, as the step change in irradiation at a constant temperature increases, the RL technique based on DDPG agent shows a decrease in tracking efficiency.

Improvement of Perturb and Observe Based on Reinforcement Learning for Maximum Power Point Tracking Under Fast Changing Condition

“…In contrast to conventional methods, intelligent control methods and evolutionary algorithms such as fuzzy logic controllers (FLC) [2], artificial neural networks (ANN) [3], genetic algorithms (GA) [4], ant colony optimization (ACO) [5], and particle swarm optimization (PSO) [6] were reported. The algorithm requires specific information about PV to track the maximum power point more accurately than conventional methods, but this method is more difficult to implement into the system and requires more duration time [7]. Many studies have been carried out by combining the two methods, either by combining conventional methods with intelligent control methods such as the FLC algorithm with P&O [8] that applied to the permanent magnet synchronous motor (PMSM).…”