Citation :

Sanju, Kusum Lata Agarwal, Satya Sai Srikant, S Nallusamy, "Simulation and Experimental Validation of DRL-PSO Empowered MPPT Approach for Partially Shaded PV System," International Journal of Electrical and Electronics Engineering, vol. 13, no. 5, pp. 78-94, 2026. Crossref, https://doi.org/10.14445/23488379/IJEEE-V13I5P107

Abstract

Harvesting the maximum available power from a solar Photovoltaic (PV) installation is one of the most critical control challenges, particularly when the environment offers fluctuating irradiance together with partial shading on the array. To deal with this issue, the present manuscript brings forward a novel hybrid Maximum Power Point Tracking (MPPT) scheme in which a Deep Reinforcement Learning (DRL) module, built around the Deep Deterministic Policy Gradient (DDPG) algorithm, has been integrated with Particle Swarm Optimisation (PSO). An embedded artificial-intelligence agent has been further incorporated so that the controller can adapt itself in real time to the prevailing conditions. The actual irradiance and ambient temperature recorded on 20 June 2025 at Ghaziabad city (28.6692° N, 77.4538° E) in northern India have been used as the validation profile. The proposed scheme has been implemented in a LabVIEW environment. Its behaviour has been benchmarked against four well-established techniques, namely Perturb & Observe, Fuzzy Logic, Artificial Neural Network (ANN), and Model Reference Adaptive Control (MRAC), considering parameters such as settling time, voltage and current fluctuations, transient response, and behaviour under shaded operation. The recorded results bring out a tracking efficiency of 99.79 percent against 96.87 percent (P&O), 98.50 percent (Fuzzy), 98.84 percent (ANN), and 99.76 percent (MRAC). The settling interval has been measured at nearly 2.6 ms. At the same time, the voltage and current ripples have been confined to 0.029 V and 0.025 A, with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) values of 0.00187, 0.00191, and 0.22 percent, respectively.

Keywords

AI Agent, Maximum Power Point Tracking, Partial Shading, Deep Reinforcement Learning, PSO Optimization

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