Comprehensive learning Jaya algorithm for parameter extraction of photovoltaic models

Zhang, Yiying; Ma, Maode; Jin, Zhigang

doi:10.1016/j.energy.2020.118644

Cited by 65 publications

(22 citation statements)

References 40 publications

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“…Traditional approaches such as Incremental Conductance (IC) [2], Hill Climbing (HC), Perturb and Obserb (P&O) [3], etc., is incapable of tracking MPP under Partial Shading Conditions (PSC). Many metaheuristic strategies, including Particle Swarm Optimization (PSO) [4], Jaya algorithm [5], Whale Optimization Algorithm (WOA) [6], Grey Wolf Optimization (GWO) [7], and Jelly Search Algorithm (JSA) [8][9][10][11], have been utilised to overcome the challenges produced by PSC. To address this problem, a hybrid Improved Jelly Fish Algorithm integrated Perturb and Obserb (IJFA-PO) method was proposed, which combines the benefits of both traditional and innovative methods.…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: A Novel Approach for Mitigating Power Quality Issues in a PV Integrated Microgrid System Using an Improved Jelly Fish Algorithm

Suman¹,

Chatterjee²,

Mohanty³

2021

Preprint

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A two-step methodology was used to address the power quality concerns for the PV integrated microgrid system. In the first step, partial shading was included to deal with the real-time issues. The Improved Jelly Fish Algorithm integrated Perturb and Obserb (IJFA-PO) has been proposed to track the Global Maximum Power Point (GMPP). In the second step, the main unit powered by a DC-AC converter is synchronised with the grid. To cope with the wide voltage variation, an auxiliary unit is connected to the main unit in the opposite phase. This study evaluates various switching approaches to determine the optimal solution for achieving the stated goals. It was found that the IJFA-based SHE in 120 օ conduction gives improved results. A novel series compensation technique has been employed to further eliminate harmonics before grid integration. The proposed IJFA has been used to determine the switching angles for the SHEPWM converter. The objective function and novel series compensation work together to determine the harmonics that should be decreased and kept to a minimum. Three switching angles' performance was equivalent to that of nine switching angles. As a result, higher-order harmonics can be minimised with fewer switches, and switching losses can be lowered without compromising efficiency. The THD of the proposed system was 1.32 percent, which is very much within the tolerable limit of IEEE 1547, IEC, and CIGRE WG 36-05 standards. In terms of efficiency, metaheuristics, and convergence, the proposed system outperformed the existing ones. The model was developed in MATLAB/Simulink 2016b. To verify the simulation results, an experimental prototype of grid synchronised PV capacity of 260W was tested under various loading conditions. The present model is reliable and features a simple controller that provides more convenient and adequate performance for practical reasons.

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Section: Introductionmentioning

confidence: 99%

WITHDRAWN: A Novel Approach for Mitigating Power Quality Issues in a PV Integrated Microgrid System Using an Improved Jelly Fish Algorithm

Suman¹,

Chatterjee²,

Mohanty³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Table 2 presents some of them, classified on family and on whether they are simple or hybrid [8]. The families of the algorithms presented are genetic algorithms (GA) [31][32][33][34], differential evolution (DE) [35][36][37][38], particle swarm optimization (PSO) [39][40][41][42][43], discretization [3,44,45], artificial bee colony (ABC) [46,47], shuffled complex evolution [48,49], simulated annealing (SA) [50,51], flower pollination algorithm (FPA) [52,53], harmony search (HS) [54], JAYA algorithm [55][56][57], teaching-learning-based optimization algorithm [58,59], whale optimization algorithm [60,61], and backtracking search algorithm [62,63]. Additionally, the diode model is shown, computational time and the iteration number when these are given.…”

Section: Analytical and Metaheuristic Methodsmentioning

confidence: 99%

Analytical versus Metaheuristic Methods to Extract the Photovoltaic Cells and Panel Parameters

Cotfas

Oproiu

et al. 2021

International Journal of Photoenergy

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The parameters of the photovoltaic cells and panels are very important to forecast the power generated. There are a lot of methods to extract the parameters using analytical, metaheuristic, and hybrid algorithms. The comparison between the widely used analytical method and some of the best metaheuristic algorithms from the algorithm families is made for datasets from the specialized literature, using the following statistical tests: absolute error, root mean square error, and the coefficient of determination. The equivalent circuit and mathematical model considered is the single diode model. The result comparison shows that the metaheuristic algorithms have the best performance in almost all cases, and only for the genetic algorithm, there are poorer results for one chosen photovoltaic cell. The parameters of the photovoltaic cells and panels and also the current-voltage characteristic for real outdoor weather conditions are forecasted using the parameters calculated with the best method: one for analytical—the five-parameter analytical method—and one for the metaheuristic algorithms—hybrid successive discretization algorithm. Additionally, the genetic algorithm is used. The forecast current-voltage characteristic is compared with the one measured in real sunlight conditions, and the best results are obtained in the case of a hybrid successive discretization algorithm. The maximum power forecast using the calculated parameters with the five-parameter method is the best, and the error in comparison with the measured ones is 0.48%.

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“…Zhang et al. propose a new comprehensive learning Jaya algorithm CLJAYA with an improved global search ability [42]. Artificial ecosystem‐based optimiser algorithm (AEO) uses the following mechanisms: producer‐to balance between exploration and exploitation phases, consumer‐to reinforce the exploration phase, and decomposer‐to promote exploitation phase.…”

Section: Introductionmentioning

confidence: 99%

Hybrid successive discretisation algorithm used to calculate parameters of the photovoltaic cells and panels for existing datasets

Cotfas

Deaconu

Cotfas

2021

IET Renewable Power Gen

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This paper presents a new hybrid successive discretisation algorithm, used to calculate the parameters of the photovoltaic cells and panels, by the one diode model and the two diode model. Nine known datasets from the specialised literature were used to validate the new algorithm and then it was firstly applied for two new datasets. For the first time only one algorithm is applied to extract the parameters, in both cases-one and two diode models. The new datasets are for commercial monocrystalline silicon and amorphous silicon photovoltaic cells. The main test used to prove the performance of HDSA is the root mean square error. Other four tests were used for comparison: the mean absolute error, the mean bias error, t-statistic, and the coefficient of determination. The hybrid successive discretisation algorithm proved its accuracy and reliability for parameter extraction of different types of photovoltaic cells and panels for all datasets used. Comparing the hybrid successive discretisation algorithm with the best algorithms from the specialised literature shows an improvement of the root mean square error by up to10.4% for the one diode model and by up to 7.5% for the two diode model, respectively.

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Comprehensive learning Jaya algorithm for parameter extraction of photovoltaic models

Cited by 65 publications

References 40 publications

WITHDRAWN: A Novel Approach for Mitigating Power Quality Issues in a PV Integrated Microgrid System Using an Improved Jelly Fish Algorithm

WITHDRAWN: A Novel Approach for Mitigating Power Quality Issues in a PV Integrated Microgrid System Using an Improved Jelly Fish Algorithm

Analytical versus Metaheuristic Methods to Extract the Photovoltaic Cells and Panel Parameters

Hybrid successive discretisation algorithm used to calculate parameters of the photovoltaic cells and panels for existing datasets

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