Abstract
Renewable Energy Resources (RERs) are widely used on the concern of global environment protection. Solar energy systems play an important role in the generation of electrical energy, remarkably minimize the utilization of nonrenewable fuel sources. Solar energy can be extracted and transformed into electrical energy via solar photovoltaic process. Several traditional, soft computing, heuristic, and meta-heuristic maximum power point tracking (MPPT) techniques have been developed to extract Maximum Energy Point (MEP) from the solar photovoltaic modules under different atmospheric conditions. In this manuscript, the combination of reinforcement learning algorithm (RLA) and deep learning algorithm (DLA) called deep Reinforcement Learning Algorithm based MPPT (DRLAMPPT) is proposed under partial shading conditions (PSC) of the solar system. DRLAMPPT can deal with continuous state spaces, in contrast to RL it can be operated only with discrete action state spaces. In this proposed DRLAMPPT, deep deterministic policy gradient (DDPG) solves the problem of continuous state spaces are involved to reach the GMEP in photovoltaic systems especially under PSC. In DRLAMPPT, the representative's strategy is parameterized by an artificial neural network (ANN), which uses sensory information as input and directly sends out control signals. This work develops a 2 kW solar photovoltaic power plant comprises of a photovoltaic array, DC/DC step-up converter, 3-Φ Pulse Width Modulated Voltage Source Inverter (PWM-VSI) integrated with conventional power grid using Constant Current Controller (CCC Effectiveness of the proposed DRLAMPPT with CCC can be validated through an experimental setup and with MATLAB. Simulation and tested at different input conditions of solar irradiance. Experimental results prove that, in comparison to existing MPPTs, suggested DRLAMPPT not only attains the best efficiency and also adopts the change in environmental conditions of the photovoltaic system at a much faster rate and able to reach the GMEP within 0.8 s under PSC. Experimental and simulation results also prove that suggested CCC with LC filter makes the inverter output voltage and the grid voltage are in phase at the lower value of THD i.e. 1.1% and 0.98% respectively.