Variations of the bacterial foraging algorithm for the extraction of PV module parameters from nameplate data

Awadallah, Mohamed A.

doi:10.1016/j.enconman.2016.01.071

Cited by 122 publications

(41 citation statements)

References 42 publications

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“…In recent years, to deal with the environment pollution, global warming, and increasing energy shortage, many countries have been looked for renewable energy [1]. The research of several renewable sources, such as wind, wave, biomass, and so on, has attracted lots of attention [2].…”

Section: Introductionmentioning

confidence: 99%

Parameters Extraction of Photovoltaic Models Using Triple-Phase Teaching-Learning-Based Optimization

Liao

Chen

2020

IEEE Access

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Parameters extraction of photovoltaic (PV) models is urgently desired for the simulation, control, and evaluation of PV systems. To accurately and reliably extract the parameters of different PV models, a triple-phase teaching-learning-based optimization (TPTLBO) is proposed in this paper. The novelty of TPTLBO lies in: i) teaching-learning-based optimization introduces a buffer phase and adopts a centroid strategy to update the position of intermediate learners, which further strengthens the exploration and exploitation; ii) the learners can select different phases and employ different learning strategies based on their knowledge level; iii) a dynamic control parameter replaces the original random parameter rand to enhance the search ability of algorithm. The parameters extraction performance of TPTLBO is verified through the single diode model, the double diode model, and three PV models. Experimental results demonstrate that TPTLBO achieves better performance in terms of accuracy and reliability compared to state-of-the-art algorithms.

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Section: Introductionmentioning

confidence: 99%

Parameters Extraction of Photovoltaic Models Using Triple-Phase Teaching-Learning-Based Optimization

Liao

Chen

2020

IEEE Access

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“…Regarding the popular metaheuristic algorithms, simulated annealing algorithm [12], genetic algorithm [13,14], particle swarm optimization algorithm [15,16], differential evolution algorithm [17][18][19][20], pattern search [21], artificial bee colony algorithm [22] are widely used for the SCPIP. In addition to these well-known heuristic algorithms, there exist several papers in the literature which consider more recent approaches, such as bacterial foraging algorithm [23,24], teaching-learning-based optimization algorithm [25][26][27], biogeography-based optimization algorithm [28], chaos optimization algorithm [29], artificial fish swarm algorithm [30], bird mating optimizer approach [31], artificial immune system [32], evolutionary algorithm [1], cat swarm optimization algorithm [33], moth-flame optimization algorithm [5], JAYA optimization algorithm [34,35], chaotic whale optimization algorithm [36], imperialist competitive algorithm [37], bee pollinator flower pollination algorithm [38], shuffled complex evolution algorithm [39], memetic algorithm [40], interior search algorithm [41], collaborative swarm intelligence approach [42], and cuckoo search algorithm [43]. On the other hand, it has been proven by No-Free-Lunch theorem [44] that none of these algorithms is able to solve all type of optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Electromagnetic Field Optimization Algorithm for the Solar Cell Parameter Identification Problem

Küçükoğlu

2019

International Journal of Photoenergy

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Solar cell parameter identification problem (SCPIP) is one of the most studied optimization problems in the field of renewable energy since accurate estimation of model parameters plays an important role to increase their efficiency. The SCPIP is aimed at optimizing the performance of solar cells by estimating the best parameter values of the solar cells that produce an accurate approximation between the current vs. voltage (I−V) measurements. To solve the SCPIP efficiently, this paper introduces an adaptive variant of the electromagnetic field optimization (EFO) algorithm, named adaptive EFO (AEFO). The EFO simulates the attraction-repulsion mechanism between particles of electromagnets having different polarities. The main idea behind the EFO is to guide electromagnetic particles towards global optimum by the attraction-repulsion forces and the golden ratio. Distinct from the EFO, the AEFO searches the solution space with an adaptive search procedure. In the adaptive search strategy, the selection probability of a better solution is increased adaptively whereas the selection probability of worse solutions is reduced throughout the search progress. By employing the adaptive strategy, the AEFO is able to maintain the balance between exploration and exploitation more efficiently. Further, new boundary control and randomization procedures for the candidate electromagnets are presented. To identify the performance of the proposed algorithm, two different benchmark problems are taken into account in the computational studies. First, the AEFO is performed on global optimization benchmark functions and compared to the EFO. The efficiency of the AEFO is identified by statistical significance tests. Then, the AEFO is implemented on a well-known SCPIP benchmark problem set formed as a result of real-life physical experiments based on single- and double-diode models. To validate the performance of the AEFO on the SCPIP, extensive experiments are carried out, where the AEFO is tested against the original EFO, AEFO variants, and novel metaheuristic algorithms. Results of the computational studies reveal that the AEFO exhibits superior performance and outperforms other competitor algorithms.

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“…Biologically-inspired optimisation algorithms are exploited to estimate PV module parameters via minimising certain objective functions (Ye et al, 2009;Sandrolini et al, 2010;Zagrouba et al, 2010;Krishnakumar et al, 2013;da Costa et al, 2010;Yeh et al, 2017;Oliva et al, 2017;Awadallah and Venkatesh, 2016;Awadallah, 2016;Chin et al, 2017). The targeted performance is obtained either through measurements (Ye et al, 2009;Sandrolini et al, 2010;Zagrouba et al, 2010) or through datasheet information (Krishnakumar et al, 2013;da Costa et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, variations of algorithm parameters impact the convergence behaviour such that experimental numerical runs may be required before the best combination is reached. In Awadallah (2016), various combinations of BF algorithm parameters are experimented before the best fit for PV parameter estimation problems is realised. On the other hand, analytical and optimisation methods could be combined yielding a hybrid parameter estimation routine which can improve convergence behaviour and modelling accuracy (Chin et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Optimisation-based parameter estimation of photovoltaic modules

Awadallah¹,

Venkatesh²

2018

IJIED

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This paper presents a methodology for parameter estimation of photovoltaic (PV) modules based on global search and optimisation algorithms. A nonlinear optimisation problem is formulated to search the best set of equivalent circuit parameters which minimises a certain objective function. The objective function measures the discrepancy between computed and targeted performance. The proposed technique is flexible on the source of the targeted performance, which could be datasheet information or experimental measurements. The method is also flexible on the nature and number of sought parameters according to the equivalent circuit representation and available information. The nonlinear optimisation problem is solved using three different global search routines, namely, genetic algorithms (GA), particle swarm optimisation (PSO) and bacterial foraging (BF). Effectiveness of the proposed methodology is shown through three case studies on different PV modules of various ratings and manufacturers. The extracted parameters could accurately simulate the performance of PV modules under normal operation as well as low irradiance and partial shading conditions.

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Variations of the bacterial foraging algorithm for the extraction of PV module parameters from nameplate data

Cited by 122 publications

References 42 publications

Parameters Extraction of Photovoltaic Models Using Triple-Phase Teaching-Learning-Based Optimization

Parameters Extraction of Photovoltaic Models Using Triple-Phase Teaching-Learning-Based Optimization

Adaptive Electromagnetic Field Optimization Algorithm for the Solar Cell Parameter Identification Problem

Optimisation-based parameter estimation of photovoltaic modules

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