Optimization of Photovoltaic MPPT System Efficiency Based on Combined Algorithm

Nian-chun, Wang; Jin, Ming; Wang, Zheng; Cheng, Ming

doi:10.23919/icems.2018.8549191

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Among these, P&O and IC algorithms are more prevalent. Second category algorithms use soft computing or artificial-intelligence (AI) based techniques namely: fuzzy logic controller (FLC) based MPPT (Saravanan and Ramesh Babu, 2016), artificial neural network (ANN) MPPT (Kottas et al, 2006), adaptive neuro-fuzzy inference system (ANFIS) based MPPT (Ben Salah and Ouali, 2011;Amara et al, 2018), particle swarm optimization (PSO) based MPPT (Algarín et al, 2017;Aouchiche et al, 2018;Wang et al, 2018) technique and evolutionary algorithms (EA) based MPPT. These intelligent techniques are suited for efficient operation in more dynamic environments to produce output with lesser steadystate oscillations, but they are more sophisticated as compared to conventional techniques requiring more resources for effective realization.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of MPPT Control Techniques to Enhance Solar Energy Utilization and Convergence Time Under Varying Meteorological Conditions and Loads

Singh¹,

Singh²,

Verma

et al. 2022

Front. Energy Res.

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The electrical energy generated from solar energy photovoltaic (PV) technology is intermittent, varying, and irregular. With PV technology’s limited energy conversion efficiency, it is imperative to extract the maximum of converted energy. The zero slopes of the power versus voltage curve are utilized to determine the maximum power point. Conventional algorithms provide lower convergence time along with low power oscillations. This paper proposes an adaptive perturb and observe (A-P&O) maximum power point tracking (MPPT) technique for the energy conversion system. The primary objectives of the proposed technique are to obtain a more robust, better tracking capability, improved efficiency, and fast response lesser oscillations under steady-state with a simple structure to implement. Maximum power point (MPP) tracking under varying meteorological conditions and load variations is still a challenge. The proposed P&O technique has been tested under realistic meteorological variations and load variations. The comparative evaluation of the proposed adaptive-step size A-P&O MPPT technique and other conventional techniques such as perturb and observe (P&O), incremental conductance (IC), modified P&O and fuzzy logic control (FLC) have also been performed. The performance of the proposed control technique is evaluated using a MATLAB/Simulink environment. The obtained results confirm that the proposed control technique is superior in performance as compared to the other four conventional techniques.

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