AI-based global MPPT for partial shaded grid connected PV plant via MFO approach

Aouchiche, N.; Ait-Cheikh, Mohamed Salah; Becherif, Mohamed; Ebrahim, Mohamed A.

doi:10.1016/j.solener.2018.06.109

Cited by 85 publications

(25 citation statements)

References 53 publications

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“…It tracks GMPP accurately and converges faster [59]. Moth-flame optimization (MFO) is another new meta-heuristic optimization based on the convergence of moth behavior towards the light source [60].…”

Section: F Famentioning

confidence: 99%

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

Yap

Sarimuthu

Lim

2020

Journal of Modern Power Systems and Clean Energy

175

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In the last decade, artificial intelligence (AI) techniques have been extensively used for maximum power point tracking (MPPT) in the solar power system. This is because conventional MPPT techniques are incapable of tracking the global maximum power point (GMPP) under partial shading condition (PSC). The output curve of the power versus voltage for a solar panel has only one GMPP and multiple local maximum power points (MPPs). The integration of AI in MPPT is crucial to guarantee the tracking of GMPP while increasing the overall efficiency and performance of MPPT. The selection of AI-based MPPT techniques is complicated because each technique has its own merits and demerits. In general, all of the AI-based MPPT techniques exhibit fast convergence speed, less steady-state oscillation and high efficiency, compared with the conventional MPPT techniques. However, the AI-based MPPT techniques are computationally intensive and costly to realize. Overall, the hybrid MPPT is favorable in terms of the balance between performance and complexity, and it combines the advantages of conventional and AI-based MPPT techniques. In this paper, a detailed comparison of classification and performance between 6 major AI-based MPPT techniques have been made based on the review and MATLAB/Simulink simulation results. The merits, open issues and technical implementations of AI-based MPPT techniques are evaluated. We intend to provide new insights into the choice of optimal AI-based MPPT techniques. Index Terms-Maximum power point tracking (MPPT), artificial intelligence (AI), fuzzy logic control (FLC), artificial neural network (ANN), genetic algorithm (GA), swarm intelligence (SI), machine learning (ML).

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Section: F Famentioning

confidence: 99%

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

Yap

Sarimuthu

Lim

2020

Journal of Modern Power Systems and Clean Energy

175

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“…Metaheuristic techniques have the ability to track the GP in uniformly distributed irradiance as well as in case of PSCs. Many metaheuristic techniques have been used as MPPT, including particle swarm optimization (PSO) [4], bat algorithm (BA) [5], cuckoo search (CS) [6], grey wolf optimization (GWO) [7], crow search algorithm (CSA) [8], ant colony optimization (ACO) [9], colony of flashing fireflies [10], artificial bee colony (ABC) [11], gravitational search algorithm (GSA) [12], Cauchy and Gaussian sine cosine optimization [13], moth-flame optimization (MFO) [14], etc. Some of these techniques have been integrated with one of the conventional techniques to gain the benefits of both of these techniques.…”

Section: Introductionmentioning

confidence: 99%

Performance Improvement of PV Systems’ Maximum Power Point Tracker Based on a Scanning PSO Particle Strategy

2020

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Partial shading of PV systems generates many peaks in the P–V curve. These peaks have one global peak (GP), the remaining being local peaks (LPs). Metaheuristic techniques such as PSO have proven superiority in capturing the GP and avoiding entrapment in an LP in comparison to conventional techniques. In case of partial shading conditions (PSC), the GP may change its position and value in the P–V curve and the PSO is unable to capture the GP unless they reinitialize. Reinitialization of PSO particles spends a long time for convergence; and it may cause premature convergence. This paper proposes a novel strategy for scanning the new position of the GP in case of PSC changes without a need for reinitialization. The proposed strategy sends a particle to the anticipated places of peaks to search for any peak with power greater than the current GP and when it locates this new GP it will move the PSO particles directly to the new GP. This strategy reduced the reinitialization time by 650% as compared to the time required for the random reinitialization of the conventional PSO technique. Moreover; this proposed strategy completely avoids the premature convergence associated with conventional PSO techniques.

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“…[7]. Recently, many optimization algorithms such as salp swarm inspired algorithm (SSIA) [8], moth-flame optimization techniques [9], sine cosine algorithm (SCA) [10], ant lion optimizer (ALO) [11], grey wolf optimizer (GWO) [12] and dragonfly algorithm (DA) [13] have been used to reach the optimal solution with sufficient convergence performance. So, the hybrid theory can be used as a possible solution for achieving that harmony.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Implementation of Self-Adaptive Salp Swarm Optimization-Based Microgrid Droop Control

et al. 2020

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This paper presents a new version of the salp swarm inspired algorithm (SSIA) for the optimal design of the microgrid droop controller. The new version of SSIA is originated from the hybridizing of SSIA with the updating features of the particle swarm optimization (PSO). The development of SSIA is achieved by applying referential integrity between leaders and followers' candidates via employing both position and velocity update property of PSO. The hybrid SSIA-PSO also has a self-adaptive mechanism to avoid the necessity of refining the algorithm parameters for each optimization problem. Twenty-three benchmark test systems are tested to validate the superiority of the improved SSIA over the original PSO and SSIA. The proposed SSIA-PSO based control strategy is experimentally tested in a real-time environment. The control platform's performance is experimentally tested by using the Texas Instruments Launchpad TMS320F28379D. The developed real-time hardware-in-the-loop setup is a real investigation for implementing the suggested SSIA-PSO based control strategy with a low-cost control platform. The attained results prove the efficacy of the hybrid SSIA-PSO algorithm over the presented techniques. The introduced hybrid SSIA-PSO is employed to tackle the real microgrid droop control uncertainties such as inaccuracy in controller gains, deterioration of system parameters, multi-sources energy sharing challenge and system dynamics.

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AI-based global MPPT for partial shaded grid connected PV plant via MFO approach

Cited by 85 publications

References 53 publications

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

Performance Improvement of PV Systems’ Maximum Power Point Tracker Based on a Scanning PSO Particle Strategy

Real-Time Implementation of Self-Adaptive Salp Swarm Optimization-Based Microgrid Droop Control

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