Maximum power point tracking of photovoltaic system using ant colony and particle swam optimization algorithms

Nivetha, V.; Gowri, G. Vijaya

doi:10.1109/ecs.2015.7125054

Cited by 9 publications

(7 citation statements)

References 3 publications

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“…Multiple studies [37][38][39][40][41][42][43][44] focus on the use of the ACO method in tracking the Global Maximum Power Point, utilizing a variety of MPPT techniques. The Ant Colony Optimization method is based on the phenomenon of ants' behavior in following the shortest path towards their colony [42].…”

Section: Ant Colony Optimization Methods (Aco)mentioning

confidence: 99%

Artificial Intelligence and Bio-Inspired Soft Computing-Based Maximum Power Plant Tracking for a Solar Photovoltaic System under Non-Uniform Solar Irradiance Shading Conditions—A Review

et al. 2021

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Substantial progress in solar photovoltaic (SPV) dissemination in grid-connected and standalone power generation systems has been witnessed during the last two decades. However, weather intermittency has a non-linear characteristic impact on solar photovoltaic output, which can cause considerable loss in the system’s overall output. To overcome these inevitable losses and optimize the SPV output, maximum power point tracking (MPPT) is mounted in the middle of the power electronics converters and SPV to achieve the maximum output with better precision from the SPV system under intermittent weather conditions. As MPPT is considered an essential part of the SPV system, up to now, many researchers have developed numerous MPPT techniques, each with unique features. A Google Scholar survey from 2015–2021 was performed to scrutinize the number of published review papers in this area. An online search established that on different MPPT techniques, overall, 100 review articles were published; out of these 100, seven reviews on conventional MPPT techniques under shading or partial shading and only four under non-uniform solar irradiance are published. Unfortunately, no dedicated review article has explicitly focused on soft computing MPPT (SC-MPPT) techniques. Therefore, a comprehensive review of articles on SC-MPPT techniques is desirable, in which almost all the familiar SC-MPPT techniques have to be summarized in one piece. This review article concentrates explicitly on soft computing-based MPPT techniques under non-uniform irradiance conditions along with their operating principles, block/flow diagram. It will not only be helpful for academics and researchers to provide a future direction in SC-MPPT optimization research, but also help the field engineers to select the appropriate SC-MPPT for SPV according to system design and environmental conditions.

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Section: Ant Colony Optimization Methods (Aco)mentioning

confidence: 99%

Artificial Intelligence and Bio-Inspired Soft Computing-Based Maximum Power Plant Tracking for a Solar Photovoltaic System under Non-Uniform Solar Irradiance Shading Conditions—A Review

et al. 2021

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“…The ACO algorithm performs excellently for partially shaded PV modules with improved system performance [56,57]. In reference [58], an ACO-PSO-based MPPT technique is given for a partially shaded PV system. The proposed hybrid algorithm is implemented with an inter-leaved boost converter, which improves the output power and provides a constant voltage to the load.…”

Section: Aco In Mpptmentioning

confidence: 99%

Nature-Inspired MPPT Algorithms for Partially Shaded PV Systems: A Comparative Study

et al. 2019

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PV generating sources are one of the most promising power generation systems in today’s power scenario. The inherent potential barrier that PV possesses with respect to irradiation and temperature is its nonlinear power output characteristics. An intelligent power tracking scheme, e.g., maximum power point tracking (MPPT), is mandatorily employed to increase the power delivery of a PV system. The MPPT schemes experiences severe setbacks when the PV is even shaded partially as PV exhibits multiple power peaks. Therefore, the search mechanism gets deceived and gets stuck with the local maxima. Hence, a rational search mechanism should be developed, which will find the global maxima for a partially shaded PV. The conventional techniques like fractional open circuit voltage (FOCV), hill climbing (HC) method, perturb and observe (P&O), etc., even in their modified versions, are not competent enough to track the global MPP (GMPP). Nature-inspired and bio-inspired MPPT techniques have been proposed by the researchers to optimize the power output of a PV system during partially shaded conditions (PSCs). This paper reviews, compares, and analyzes them. This article renders firsthand information to those in the field of research, who seek interest in the performance enhancement of PV system during inhomogeneous irradiation. Each algorithm has its own advantages and disadvantages in terms of convergence speed, coding complexity, hardware compatibility, stability, etc. Overall, the authors have presented the logic of each global search MPPT algorithms and its comparisons, and also have reviewed the performance enhancement of these techniques when these algorithms are hybridized.

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“…In recent years, metaheuristic algorithms have been considered, primarily designed to solve complex problems with multiple variables and obtain the most optimal possible values [ 23 ]. Algorithms such as Ant Colony (ACO) [ 24 , 25 ], Cuckoo Search (CS) [ 26 , 27 ], Firefly Algorithm (FF) [ 28 , 29 ], Whale Optimization (WO) [ 30 , 31 ], Differential Evolution (DE) [ 32 ], Gray Wolf (GWO) [ 33 , 34 ] and Particle Swarm (PSO) [ 35 , 36 ]. These were proposed as they can detect the Global Maximum Power Point (GMPP) by combining possible solutions or using random variables.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review

et al. 2021

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The use of photovoltaic systems for clean electrical energy has increased. However, due to their low efficiency, researchers have looked for ways to increase their effectiveness and improve their efficiency. The Maximum Power Point Tracking (MPPT) inverters allow us to maximize the extraction of as much energy as possible from PV panels, and they require algorithms to extract the Maximum Power Point (MPP). Several intelligent algorithms show acceptable performance; however, few consider using Artificial Neural Networks (ANN). These have the advantage of giving a fast and accurate tracking of the MPP. The controller effectiveness depends on the algorithm used in the hidden layer and how well the neural network has been trained. Articles over the last six years were studied. A review of different papers, reports, and other documents using ANN for MPPT control is presented. The algorithms are based on ANN or in a hybrid combination with FL or a metaheuristic algorithm. ANN MPPT algorithms deliver an average performance of 98% in uniform conditions, exhibit a faster convergence speed, and have fewer oscillations around the MPP, according to this research.

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Maximum power point tracking of photovoltaic system using ant colony and particle swam optimization algorithms

Cited by 9 publications

References 3 publications

Artificial Intelligence and Bio-Inspired Soft Computing-Based Maximum Power Plant Tracking for a Solar Photovoltaic System under Non-Uniform Solar Irradiance Shading Conditions—A Review

Artificial Intelligence and Bio-Inspired Soft Computing-Based Maximum Power Plant Tracking for a Solar Photovoltaic System under Non-Uniform Solar Irradiance Shading Conditions—A Review

Nature-Inspired MPPT Algorithms for Partially Shaded PV Systems: A Comparative Study

Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review

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