Two fast metaheuristic-based MPPT techniques for partially shaded photovoltaic system

Moghassemi, Ali; Ebrahimi, Shayan; Padmanaban, Sanjeevikumar; Mitolo, Massimo; Holm‐Nielsen, Jens Bo

doi:10.1016/j.ijepes.2021.107567

Cited by 41 publications

(20 citation statements)

References 23 publications

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“…For example, premature convergence can be overcome using a chaotic strategy assigning the individual position of the bat algorithm [24], [25]. The complex traditional MPPT techniques show poor performance in locating the global MPP under partial shading conditions [26], [27], [28]. Recently, there have been efforts to combine traditional MPPT methods with meta-heuristic algorithms to track global MPP under partial shading effectively [29].…”

Section: Introductionmentioning

confidence: 99%

A Novel Improved Grey Wolf Algorithm Based Global Maximum Power Point Tracker Method Considering Partial Shading

Gundogdu,

Demirci,

Tercan

et al. 2024

IEEE Access

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Considering photovoltaic systems' sustainability and environmental friendliness, they have been widely used due to ease of installation as their cost reduces and their efficiency is improved. Analytical maximum power point tracking methods for photovoltaic system work effectively under uniform weather conditions. However, they may fall into local maximum power points due to partial shading conditions. Although numerous meta-heuristic methods can overcome these challenges, they can still be improved regarding the convergence time to the global maximum power point. This paper suggests an improved grey wolf optimization method to track global maximum power points, enhancing the convergence process and efficiency under various weather conditions. The proposed method has been verified experimentally under dynamic and real weather conditions, consisting of uniform and non-uniform weather conditions. The method provides better dynamic tracking speed and efficiency up to 82% and 1.4% compared to the basic grey wolf optimization. According to the daily performance evaluation, the IGWO reduces the runtime by up to 76% and improves energy harvesting up to 2.3% compared to basic grey wolf optimization. The obtained results validate the superiority of the method compared under partial shading conditions in terms of tracking time and accuracy.INDEX TERMS Heuristic algorithms, improved grey wolf optimization, maximum power point tracking, partial shading, photovoltaic.

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Section: Introductionmentioning

confidence: 99%

A Novel Improved Grey Wolf Algorithm Based Global Maximum Power Point Tracker Method Considering Partial Shading

Gundogdu,

Demirci,

Tercan

et al. 2024

IEEE Access

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“…Al-Wesabi et al [10] proposed the CSA-PID to find GMPP quickly. Moghassemi et al [11] used the advantages of the Whale Optimization Algorithm (WOA), such as fast convergence and no power fluctuations, to implement MPPT under PSC with fewer and less computationally demanding algorithms parameters adjusted. The Gravitational Search Algorithm (GSA) [12] is a new swarm intelligence algorithm with global solid search capability and fast convergence.…”

Section: Introductionmentioning

confidence: 99%

Research on the MPPT of Photovoltaic Power Generation Based on the CSA-INC Algorithm

Hou¹,

Wang²

2023

Energy Engineering

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The existing Maximum Power Point Tracking (MPPT) method has low tracking efficiency and poor stability. It is easy to fall into the Local Maximum Power Point (LMPP) in Partial Shading Condition (PSC), resulting in the degradation of output power quality and efficiency. It was found that various bio-inspired MPPT based optimization algorithms employ different mechanisms, and their performance in tracking the Global Maximum Power Point (GMPP) varies. Thus, a Cuckoo search algorithm (CSA) combined with the Incremental conductance Algorithm (INC) is proposed (CSA-INC) is put forward for the MPPT method of photovoltaic power generation. The method can improve the tracking speed by more than 52% compared with the traditional Cuckoo Search Algorithm (CSA), and the results of the study using this algorithm are compared with the popular Particle Swarm Optimization (PSO) and the Gravitational Search Algorithm (GSA). CSA-INC has an average tracking efficiency of 99.99% and an average tracking time of 0.19 s when tracking the GMPP, which improves PV power generation's efficiency and power quality.

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“…Photovoltaic energy is gaining popularity over other sources of RE such as geothermal, wind, biomass, fuel cells, and hydropower due to its universal availability, low emissions, and low operating cost [ 2 ]. Partial shading caused by clouds and obscuring objects such as buildings or trees is a common problem in PV systems [ 3 ]. Shaded PV modules operate in reverse bias and act as a load that decreases the performance of the system [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

An improved grasshopper-based MPPT approach to reduce tracking time and startup oscillations in photovoltaic system under partial shading conditions

Wasim,

Amjad,

Abbasi

et al. 2023

PLoS ONE

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Global maximum power point (GMPP) tracking under shading conditions with low tracking time and reduced startup oscillations is one of the challenging tasks in photovoltaic (PV) systems. To cope with this challenge, an improved grasshopper optimization algorithm (IGOA) is proposed in this work to track the GMPP under partial shading conditions (PSC). The performance of the proposed approach is compared with well-known swarm intelligence techniques (SITs) such as gray wolf optimization (GWO), cuckoo search algorithm (CSA), salp swarm algorithm (SSA), improved SSA based on PSO (ISSAPSO), and GOA in terms of tracking time, settling time, failure rate, and startup oscillations. For a fair comparison, the PV system is analysed under uniform irradiance and three PSCs having four to six peaks in the power-voltage characteristic curves and using three to six search agents for each SIT. For this purpose, a PV system containing six solar panels has been built using MATLAB/SIMULINK software, and statistical analysis is performed in detail. The results show that the IGOA tracks the GMPP in 0.07 s and settles the output in 0.12 s which is 25% to 96% faster than its counterparts. Moreover, IGOA proves its consistency with a minimal tracking failure rate of 0% for four to six search agents with negligible startup oscillations. This work is expected to be helpful to PV system installers in obtaining maximum benefits from the installed system.

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Two fast metaheuristic-based MPPT techniques for partially shaded photovoltaic system

Cited by 41 publications

References 23 publications

A Novel Improved Grey Wolf Algorithm Based Global Maximum Power Point Tracker Method Considering Partial Shading

A Novel Improved Grey Wolf Algorithm Based Global Maximum Power Point Tracker Method Considering Partial Shading

Research on the MPPT of Photovoltaic Power Generation Based on the CSA-INC Algorithm

An improved grasshopper-based MPPT approach to reduce tracking time and startup oscillations in photovoltaic system under partial shading conditions

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