Abstract

A PV system’s operation highly depends on weather conditions. In case of varying irradiances or load changes, there is a power mismatch between various modules of the PV array. This power mismatch causes instability in the output of the PV system and deteriorates the overall system efficiency. To overcome instability and lower efficiency problems, and to extract maximum power from the PV system, various maximum power point tracking (MPPT) techniques are employed. The success of these techniques depends on the identification of the actual operating conditions of the system. This article proposes a hybrid maximum power point tracking (MPPT) technique that is capable of efficiently differentiating between uniform irradiance, non-uniform irradiance, and load variations on the PV system. Based on the identified operating conditions, the proposed method uses modified perturb and observe (Modified P&O) to cope with uniform irradiance variations and chimp optimization algorithms (ChOA) for non-uniform conditions to track the oscillation free maximum power-point. The proposed method is implemented and verified using a 4 × 3 PV array model in MATLAB Simulink software. Different cases of uniformly changing irradiance and non-uniformly changing irradiance are applied to test the performance of the proposed hybrid technique. The load varying conditions are performed by applying a variable load resistor. The authenticity of the proposed hybrid technique is critically evaluated against the well-known and most widely used optimization techniques of modified perturb and observe (Modified P&O), particle swarm optimization (PSO), flower pollination algorithm (FPA), and grey wolf optimization (GWO). The results demonstrate the superiority of the proposed technique in oscillation-free tracking of global maximum power point (GMPP) in a minimum tracking time of 0.4 s and 0.15 s, and steady-state MPPT efficiency of 96.92% and 99.54% under uniform and non-uniform irradiance conditions, respectively.

Highlights

  • Most conventional energy resources rely on fossil fuels, which are costly, diminishing, and pollute the atmosphere

  • The results demonstrate the superiority of firefly algorithm (FA) over particle swarm optimization (PSO) and P&O in terms of tracking speed and efficiency

  • The main research gap in the existing literature is the problems and limitations associated with global maximum power point tracking (GMPPT) techniques while dealing with uniform irradiance change, partial shading, and load variations

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Summary

Introduction

Most conventional energy resources rely on fossil fuels, which are costly, diminishing, and pollute the atmosphere. Despite having efficient performance under uniform irradiance change, the proposed technique fails to track the MPP during partial shading cases. Even though metaheuristic techniques have outstanding performance in partial shading conditions, most of these methods have some limitations (i.e., complexity, increased tuning parameters, slow convergence speed, and large tracking and steady-state oscillations). The main research gap in the existing literature is the problems and limitations associated with global maximum power point tracking (GMPPT) techniques while dealing with uniform irradiance change, partial shading, and load variations. Based on the identified operating conditions, the proposed hybrid technique uses modified P&O for oscillation-free MPP tracking under uniform irradiance change. Advantages of fewer tuning parameters, high convergence speed, and negligible tracking oscillations, is employed to extract global maximum power point (GMPP) in the case of nonuniform irradiance and load variations.

PV System Modelling
Classification of Uniform and Non-Uniform Irradiance Variation
Identification of Load Variations
Combined Effect of Load and Irradiance Variations
Proposed Hybrid GMPPT Methodology
Identification of Operating Conditions
Oscillation-Free Tracking of MPP Using Modified Perturb and Observe under UIC
Results and Discussions
Uniform Irradiance Conditions
Partial Shading Conditions
Load-Varying Conditions
Conclusions
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