Cultured Bat Algorithm for Optimized MPPT Tracking Under Different Shading Conditions

Amalo, Kulu. Amadu.; Birninkudu, Sabo Ibrahim; Bukata, Bala Boyi; Salawudeen, Ahmed Tijani; Ahmad, Ashraf Adamu

doi:10.1109/icmcecs47690.2020.246985

Cited by 9 publications

(5 citation statements)

References 23 publications

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“…The modified PSO allows for GMPP tracking, as shown in Figs. 16 and 17 of the voltage and power curves. Before looking for local maxima, a sawtooth-like wave structure was observed in the voltage curve, after which the oscillation reduced, and the wave became a linear and steady state.…”

Section: Comparison Of Mppt Methods Resultsmentioning

confidence: 99%

“…Since evolutionary algorithms (EA) and swarm-based algorithms can handle multi-modal objective functions, they are a choice for treating the MPPT as an optimization problem and searching global maxima [10]. Some of them are differential evolution (DE) [11], artificial bee colony [15], modified butterfly optimization algorithm (MBOA) [4], bat search technique [16], modified squirrel search algorithm (ISSA) [17], genetic algorithms (GA) [18], cuckoo search algorithm (CS) [19], Salp swarm optimization (SSO) [20], ant colony optimization (ACO) [21], grey wolf optimization (GWO) [7], modified grasshopper optimization (IGHO) [22], and particle swarm optimization (PSO) method [23]. The listed algorithms differ by tracking time, accuracy, efficiency, and complexity.…”

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confidence: 99%

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Application of a Modified Particle Swarm Optimization for Maximum Power Point Tracking for Solar Photovoltaic Systems

Gedefaye,

Lakeou,

Tadiwos

et al. 2023

JERA

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The maximum power point extraction at any instant of time on photovoltaic (PV) systems has attracted attention. This study introduces a novel DC-DC converter-based power point tracking (PPT) algorithm for solar PV systems. The proposed optimization technique is a modified form of the standard particle swarm optimization (PSO), where the limitations of the standard PSO algorithm, like random number assignment of the acceleration factors and constant weight, are modified. The main goal of the suggested modified particle swarm optimization (MPSO) algorithm is to change the particle weight within a range of values and remove the random number from the acceleration factors. As a result, some of the contributions to this work are: First, when the weight is within some interval values, velocity restriction with a constant number improves. It offers the chance to expedite the search without limitation because of the constantly shifting environmental conditions. Second, the solution shows that the lack of acceleration constants predicts the particle's behavior. Thirdly, the algorithm's input parameters are incredibly minimal. The MATLAB/Simulink simulation of a modeled standalone 2.9 kW solar PV system in shading and non-shading conditions proved the proposed algorithm's performance. Thus, the average efficiency and time tracking of the global maximum power point (GMPP) is 99.45% and 6.285 s, respectively. Generally, the proposed MPPT method is more straightforward and adaptable than perturb and observe (P&O), the cuckoo search algorithm, and standard PSO.

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Section: Comparison Of Mppt Methods Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Application of a Modified Particle Swarm Optimization for Maximum Power Point Tracking for Solar Photovoltaic Systems

Gedefaye,

Lakeou,

Tadiwos

et al. 2023

JERA

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“…The BA was an optimization algorithm of swarm intelligence proposed by Professor Xin-She Yang in 2010 [21][22][23]. The algorithm was based on mimicking the echolocation of micro-bats in nature since the brain and neural system of hearing for a micro-bat could generate profound images of its surroundings by comparing between the pulses of the emitted sounds and the echoes that repetitively appeared.…”

Section: Bamentioning

confidence: 99%

“…In recent years, to solve multiple peak values generated from the P-V curve due to certain modules in a PVMA being shaded, where conventional MPPT methods became invalid, many smart MPPT methods were proposed [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. These methods included the cuckoo search algorithm (CSA) [9][10][11], cat swarm optimization (CSO) [12][13][14], genetic algorithm (GA) [15][16][17], teaching-learning-based optimization (TLBO) [18], grey wolf algorithm [19,20] and bat algorithm (BA) [21][22][23]; however, each algorithm had its unique strengths and constraints. Among them, CSA [9][10][11] was a global search algorithm inspired by bird behavior of food searching.…”

Section: Introductionmentioning

confidence: 99%

“…Although the grey wolf algorithm [19,20] provided merits such as a simple architecture and less parameters required during the procedure of search optimization, the disadvantages included the possibility of eventually being stuck in the local optimum, poor accuracy and slow convergence. The BA [21][22][23] on the other hand was inspired by bats searching for food and was applied in MPPT for a PVMA. The BA had the characteristics of global searching and random searching, where the randomness allowed allocating a new MPP under different environmental conditions, hence the enhancement in fitness level.…”

Section: Introductionmentioning

confidence: 99%

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Global Maximum Power Point Tracking of Photovoltaic Module Arrays Based on an Improved Intelligent Bat Algorithm

Chao,

Bau

2024

Electronics

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In this paper, a method based on an improved intelligent bat algorithm (IIBA) in cooperation with a voltage and current sensor was applied in maximum power point tracking (MPPT) for a photovoltaic module array (PVMA), where the power generation performance of a PVMA was enhanced. Due to the partial shading of the PVMA from climate changes or the surrounding environment, multiple peak values were generated on the power–voltage (P-V) curve, where the conventional MPPT technology could only track the local maximum power point (LMPP), hence the reduction in output power of PVMAs. Therefore, the IIBA-based MPPT was proposed in this paper to solve such issues and to ensure the capability of a PVMA in tracking the global maximum power point (GMPP) and utilization for enhancing the output power of a PVMA. Firstly, the Matlab/Simulink software was used to establish a boost converter model that simulated the actual 4-series–3-parallel PVMA under different shaded conditions, where the P-V curve with 1-peak, 2-peak, 3-peak and 4-peak values were generated. Subsequently, the tracking paces of the conventional bat algorithm (BA) were adjusted according to the gradient of the P-V curve for a PVMA. At the same time, 0.8 times the maximum power point (MPP) voltage Vmp under standard test conditions (STCs) for a PVMA was set as the initial tracking voltage. Lastly, the simulation results proved that under different environmental impacts, the proposed IIBA led to better performances in tracking both dynamic and steady responses.

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Applications of Cultural Algorithms in Engineering

Jalili

2022

Engineering Optimization: Methods and Applications

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Cultured Bat Algorithm for Optimized MPPT Tracking Under Different Shading Conditions

Cited by 9 publications

References 23 publications

Application of a Modified Particle Swarm Optimization for Maximum Power Point Tracking for Solar Photovoltaic Systems

Application of a Modified Particle Swarm Optimization for Maximum Power Point Tracking for Solar Photovoltaic Systems

Global Maximum Power Point Tracking of Photovoltaic Module Arrays Based on an Improved Intelligent Bat Algorithm

Applications of Cultural Algorithms in Engineering

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