Single Sensor-Based MPPT of Partially Shaded PV System for Battery Charging by Using Cauchy and Gaussian Sine Cosine Optimization

Kumar, Nishant; Hussain, Ikhlaq; Singh, Bhim; Panigrahi, Bijaya Ketan

doi:10.1109/tec.2017.2669518

Cited by 140 publications

(52 citation statements)

References 29 publications

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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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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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“…11 Such diodes have no influence under normal conditions. These algorithms comprise flower pollination algorithm, 16 teaching-learningbased optimization, 17 Cuckoo search (CS), 18 moth-flame optimization, 19 mine blast optimization, 20 particle swarm optimization (PSO), 21 differential evolution, 22 Jaya algorithm, 23 grey wolf optimizer, 24 sine cosine algorithm (SCA), 25 and shuffled frog leap algorithm. This issue causes power versus voltage graph of the PVS during shadowing condition that contain some local MPPs and single global point.…”

Section: Introductionmentioning

confidence: 99%

“…The main purpose of these techniques is to improve the efficacy of a PVS under shading. These algorithms comprise flower pollination algorithm, 16 teaching-learningbased optimization, 17 Cuckoo search (CS), 18 moth-flame optimization, 19 mine blast optimization, 20 particle swarm optimization (PSO), 21 differential evolution, 22 Jaya algorithm, 23 grey wolf optimizer, 24 sine cosine algorithm (SCA), 25 and shuffled frog leap algorithm. 26 Most of global MPP tracking methods in the literature require both current and voltage sensors at the array side.…”

Section: Introductionmentioning

confidence: 99%

An efficient single‐sensor global maximum power point tracking method for partially shaded photovoltaic battery chargers

Rezk

2019

Int J Energy Res

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SUMMARY Solar energy and other renewables like geothermal, biomass, and wind energy can minimize the release of the CO2 and other harmful gases produced in case of fossil fuel. Low efficiency is the main drawback of the solar photovoltaic system specifically under partial shadowing condition (PSC). Commonly, with uniform solar radiation distribution, the power‐voltage graph has single maximum power point (MPP). The single MPP can be definitely extracted by any traditional tracker like perturb and observe as an example. However, during PSC, the situation is completely different since the power‐voltage curve has many MPPs (ie, multiple local points and single global point). The conventional MPP tracking methods cannot discriminate among local peaks and global peak; consequently, they can be easily trapped on the first local peak. Therefore, smart MPPTs based on modern optimization are required to track the global MPP. Most of MPPT tracking methods in the literature require both voltage and current sensors, and sometimes the control system needs an additional solar irradiance sensor and/or temperature sensor, which increase the system cost. In this paper, for the first time, a simple single‐sensor–based global MPP tracking method for partially shaded photovoltaic battery chargers is proposed. A deterministic particle swarm optimizer is utilized to extract the global MPP. Several patterns of PSC are considered to test and evaluate the proposed strategy. The obtained results confirm the efficacy of a single‐sensor–based global MPP tracking method to catch the global MPP accurately. Considering this research reduces the number of sensors, cost, and difficulty and consequently increases the power density of the MPP tracking methods under partial shadowing conditions.

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“…Several global tracking techniques based on modern optimization are proposed in order to extract the global MPP under shading condition. These techniques include Flower Pollination Algorithm [29], Teaching-Learning-Based Optimization [30], Cuckoo Search [31], Moth-Flame Optimization [32], Mine Blast Optimization [33], Particle Swarm Optimization [34,35], Differential Evolution [36], Jaya Algorithm [37], Grey Wolf Optimizer [38,39], Sine Cosine Algorithm [40] and shuffled frog leap algorithm [41]. A modified PSO based MPPT algorithm for a PV system operating under PSC, has been proposed by [35].…”

Section: Introductionmentioning

confidence: 99%

Numerical Performance Evaluation of Solar Photovoltaic Water Pumping System under Partial Shading Condition using Modern Optimization

et al. 2019

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Renewable energy is an attractive solution for water pumping systems particularly in isolated regions where the utility grid is unavailable. An attempt is made to improve the performance of solar photovoltaic water pumping system (SPVWPS) under partial shading condition. Under this condition, the power versus voltage curve has more than one maximum power point (MPP), which makes the tracking of global MPP not an easy task. Two MPP tracking (MPPT) strategies are proposed and compared for tracking MPP of SPVWPS under shading condition. The first method is based on the classical perturb and observe (P&O) and the other method is based on a Salp Swarm Algorithm (SSA). Based on extensive MATLAB simulation, it is found that the SSA method can provide higher photovoltaic (PV) generated power than the P&O method under shading condition. Consequently, the pump flowrate is increased. But, under normal distribution of solar radiation, both MPPT techniques can extract the maximum power but SSA is considered a time-consuming approach. Moreover, SSA is compared with particle swarm optimization (PSO) and genetic algorithm (GA). The obtained results ensure the superiority of SSA compared with PSO and GA. SSA has high successful rate of reaching true global MPP.

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Single Sensor-Based MPPT of Partially Shaded PV System for Battery Charging by Using Cauchy and Gaussian Sine Cosine Optimization

Cited by 140 publications

References 29 publications

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

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

An efficient single‐sensor global maximum power point tracking method for partially shaded photovoltaic battery chargers

Numerical Performance Evaluation of Solar Photovoltaic Water Pumping System under Partial Shading Condition using Modern Optimization

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