A Review on Investigation of PV Solar Panel Surface Defects and MPPT
Techniques

Monicka, S. Gayathri; Manimegalai, D.; Karthikeyan, M.

doi:10.2174/2352096515666220620093933

Cited by 2 publications

(1 citation statement)

References 89 publications

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“…In the actual operation of the photovoltaic power generation system, uneven illumination and sudden change in illumination intensity will lead to the multi-peak phenomenon in the output power curve of a photovoltaic array. [1][2] Traditional MPPT algorithms, such as constant voltage method, perturbation and observation method(P&O) and incremental conductivity method(INC), are simple and easy to implement, but they are greatly influenced by the environment, so it is difficult to ensure the maximum power generation efficiency of the system. [3][4][5][6] The use of intelligent algorithms makes up for the shortcomings of traditional algorithms to a great extent, including particle swarm optimization algorithm(PSO), genetic algorithm(GA), artificial neural network algorithm(ANN), grey wolf optimization algorithm(GWO),glowworm swarm optimization algorithm(GSO),fuzzy logic control algorithm(FLC), etc.…”

Section: Introductionmentioning

confidence: 99%

Multi-peak MPPT Control Based on Variable Step Disturbance Observation Method and Butterfly Optimization Algorithm

2023

jeis

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Photovoltaic power generation has attracted more and more attention in the field of new energy applications, and the maximum power point tracking technology is the critical link of the photovoltaic power generation system. In the case of partial shading, the output power curve of the photovoltaic array presents a multi-peak phenomenon, and the traditional MPPT algorithm is easy to fall into the optimal local solution when tracking the maximum power, while the traditional butterfly algorithm has slow convergence and low optimization accuracy in the tracking process. In order to reduce the loss of output power of photovoltaic system, an MPPT control method combining butterfly algorithm with adaptive inertia weight and variable step size disturbance observation method is proposed. In the butterfly algorithm, the population randomly generates the initial solution, and the crossover mutation operation is carried out on the population. The inertia weight is constantly updated with the increase of iteration times, which can reduce the oscillation amplitude in the tracking process, increase the robustness of the algorithm search, and achieve the purpose of global search. Then, the perturbation observation method with variable step size is used to accelerate the convergence speed and accuracy. The simulation results show that compared with the traditional perturbation observation method and butterfly optimization algorithm, the proposed algorithm can find the maximum power point stably, quickly, and accurately in the case of sudden illumination change, which significantly improves the performance of MPPT.

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