Improved particle swarm optimization based MPPT for PV systems under Partial Shading Conditions

Avila, Eduardo; Pozo, Nataly; Pozo, Marcelo; Salazar, Gabriel; Dominguez, Xavier

doi:10.1109/spec.2017.8333634

Cited by 14 publications

(8 citation statements)

References 16 publications

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“…The performance of the PV system under partial shading conditions (PSC) has been discussed and modeled in [19]. Shading on the PV system generates multiple peaks on the P-d curve of the PV array as explained above and is shown in Fig.…”

Section: Modeling Of Pv Systemmentioning

confidence: 99%

A Novel Bat Algorithm Strategy for Maximum Power Point Tracker of Photovoltaic Energy Systems Under Dynamic Partial Shading

2020

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Power versus voltage curves of partial shading photovoltaic (PV) systems contain several local peaks (LPs) and one global peak (GP). Most conventional maximum power point tracker (MPPT) techniques may not follow the GP under partial shading conditions (PSC). The use of metaheuristic techniques such as the bat algorithm (BA) and particle swarm optimization (PSO) can overcome these obstacles. All problems inherent in the using of BA as MPPT of PV systems has been discussed and solved in this paper. The first problem is the random initial values of bats that may cause premature convergence. Therefore, the initial values of bats were modified to be close to the anticipated positions of peaks to reduce the convergence time and improve the chance of capturing the GP. The second problem occurs when shading pattern changes the value and position of the GP which is not configurable because all bats are concentrated at the previous GP; this can be resolved by BA re-initialization. The the third problem is the GP memorized in the execution of the BA code forces the PV system to work at the duty ratio of the highest GP ever seen, which may not be the real GP. This problem is solved by updating the memorized GP. This paper also proposes a new criterion for selecting the optimal swarm size against number of peaks to reduce the convergence time and improve the chance of capturing the GP. To the authors' knowledge, most of these problems inherent in the BA have hitherto not been addressed in the literature. The simulation and experimental results obtained from the proposed modified BA (MBA) with re-initialization have been compared to the PSO and grey wolf optimization (GWO) techniques which show the superiority of using MBA strategy in the MPPT of partial shading PV systems. INDEX TERMS Bat algorithm, boost converter, dynamic global peak, maximum power point tracker, partial shading conditions, PV system.

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Section: Modeling Of Pv Systemmentioning

confidence: 99%

A Novel Bat Algorithm Strategy for Maximum Power Point Tracker of Photovoltaic Energy Systems Under Dynamic Partial Shading

2020

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“…PSO has been integrated with fuzzy logic controller (FLC) for this purpose, where the MPPT starts the search with PSO, and when it captures the GP it transfers the control to FLC [29]. Other researchers used the same idea but hybridized the PSO with the P&O technique [30][31][32][33][34]. The PSO has also been hybridized with the gravitational search algorithm (GSA) for the same purpose [35].…”

Section: Hybridizing the Pso With Other Mppt Techniquementioning

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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“…The duration and intensity of sunlight that strikes the surface of the PV panel directly control the numerical value of the output current (Avila et al, 2017) (Subha and Himavathi, 2014). This explains the reason for the popularity of solar energy as a form of renewable energy in countries which are mostly tropical.…”

Section: Introductionmentioning

confidence: 99%

Implementation of an MPPT technique of a solar module with supervised machine learning

et al. 2022

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Automated calibration of a maximum power point tracking (MPPT) algorithm for the photovoltaic (PV) system is pivotal for harnessing the maximum possible energy from solar power. However, most existing calibration methods of such an MPPT system are cumbersome and vary greatly with the environmental condition. Hence, an automated pipeline capable of performing suitable adjustments is highly desirable. We proposed a method using supervised machine learning (ML) in a solar PV system for MPPT analysis. For this purpose, an overall schematic diagram of a PV system is designed and simulated to create a dataset in MATLAB/Simulink. Thus, by analyzing the output characteristics of a solar cell, an improved MPPT algorithm on the basis of a neural network (NN) method is put forward to track the maximum power point (MPP) of solar cell modules. Moreover, we implemented the algorithm in a hardware setup and verified the theoretical result with the empirical data. Typically, the performance accuracy of the NN models is around 97∼98%. But our proposed model shows an even higher efficiency (99.8% approximately) without adding to any extra computational cost.

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Improved particle swarm optimization based MPPT for PV systems under Partial Shading Conditions

Cited by 14 publications

References 16 publications

A Novel Bat Algorithm Strategy for Maximum Power Point Tracker of Photovoltaic Energy Systems Under Dynamic Partial Shading

A Novel Bat Algorithm Strategy for Maximum Power Point Tracker of Photovoltaic Energy Systems Under Dynamic Partial Shading

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

Implementation of an MPPT technique of a solar module with supervised machine learning

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