Harnessing artificial neural networks for accurate PV system parameters determination: radiation, temperature, and MPPT

TL;DR

This paper introduces a new method using artificial neural networks to efficiently determine PV system parameters and track maximum power points with high accuracy and low cost.

Contribution

A novel two-stage MPPT strategy using artificial neural networks for estimating PV parameters and tracking MPP with high efficiency and minimal fluctuations.

Findings

The proposed ANN-based method achieves 99.99% tracking efficiency with a fast settling time of 0.007 seconds.

The method outperforms existing MPPT techniques like FLC, P&O, FIC, and VIC in terms of efficiency and response time.

The system was validated using simulations and real-world data from Hurghada, Egypt.

Abstract

Photovoltaic (PV) systems are increasingly significant in modern electrical energy applications. Extracting the maximum power from PV modules with high efficiency requires measuring temperature (T) and irradiance (G), which often demands sensors that increase the overall system cost. Furthermore, tracking the PV maximum power point (MPP) under varying T and G presents a considerable challenge. Conventional MPPT techniques require a long time to reach the MPP and can exhibit fluctuations during operation. To address these challenges, this work proposes a novel two-stage maximum power point tracking (MPPT) strategy. In the first stage, T and G are estimated using an artificial neural network (ANN) based on the measured PV open-circuit voltage and short-circuit current, thereby reducing system cost. The first proposed stage is compared with Newton Raphson and Open circuit voltage methods (VOC) in terms of T and G errors. In the second stage, the MPP is determined directly by ANN under varying T and G, minimizing tracking time and fluctuations. This stage is compared with Fuzzy logic control (FLC), Perturb and observe (P&O), Fixed increment conductance (FIC) and Variable increment conductance (VIC) in terms of efficiency, time capture (TC), and steady-state error. Simulation results demonstrate high tracking efficiency (99.99%), fast settling time (0.007 s), and low voltage/current ripples (0.018/0.12). Comparison with FLC (99.1%, 0.0275s), P&O (98.7%, 0.0322s), FIC (98.78%, 0.0517s), and VIC (98.81%, 0.0342s) confirms the best performance of the proposed method. The proposed ANN-based method is applied to simulate the system for three case studies. In the first case, predefined data are utilized, while in the second case, real T and G data from Hurghada, Egypt are employed. Third case is an experimental setup established to validate the performance of the proposed ANN strategy. The result of the proposed system was evaluated using MATLAB/Simulink.

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