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Maximum power extraction from solar PV systems using intelligent based soft computing strategies: A critical review
Abhinav Saxena1, Rajat Kumar2, Mohammad Amir3,4
1Department of Electrical Engineering, JSS Academy of Technical Education, Noida, 201301, India.
Heliyon
|February 5, 2024
Summary
This review compares solar photovoltaic (PV) maximum power point tracking (MPPT) techniques. Artificial neural network (ANN) excels in power extraction, while incremental conductance (IC) minimizes deviations, and particle swarm optimization (PSO) reduces losses.
Area of Science:
- Renewable Energy Systems
- Power Electronics
- Control Systems
Background:
- Solar photovoltaic (PV) arrays require efficient maximum power point tracking (MPPT) to meet energy demands.
- Variable environmental conditions like shading and irradiance impact PV array power output.
- Optimizing power extraction is crucial for the economic viability and performance of solar energy systems.
Purpose of the Study:
- To conduct a comprehensive review and comparative analysis of various MPPT techniques for solar PV cells.
- To evaluate the performance of different MPPT methods under varying environmental conditions.
- To identify the strengths and weaknesses of each technique for practical implementation in smart energy systems.
Main Methods:
- Literature review and comparative analysis of prominent MPPT algorithms.
- Inclusion of techniques such as Perturb and Observe (P&O), Fuzzy Logic Control (FLC), Incremental Conductance (IC), Ripple Correction Control (RCC), Artificial Neural Network (ANN), Particle Swarm Optimization (PSO), Lyapunov Control Scheme (LCS), and Fisher Discrimination Dictionary Learning (FDDL).
- Performance evaluation considering factors like power extraction efficiency, deviation from maximum power point, total harmonic distortion (THD), and switching losses.
Main Results:
- Artificial Neural Network (ANN) demonstrated superior maximum power extraction across different irradiation levels.
- Incremental Conductance (IC) achieved the least deviation from the maximum power point.
- Fisher Discrimination Dictionary Learning (FDDL) was most effective in minimizing Total Harmonic Distortion (THD), while Particle Swarm Optimization (PSO) resulted in the lowest switching losses.
Conclusions:
- Each MPPT technique possesses unique advantages, making them suitable for specific applications within smart energy systems.
- The choice of MPPT method should be based on the specific parameters and requirements of the solar energy system.
- This review serves as a valuable resource for researchers selecting appropriate soft computing methods for solar PV power optimization.
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