Parameters identification of PV solar cells and modules using flexible particle swarm optimization algorithm

Ebrahimi, Shayan; Salahshour, Esmaeil; Malekzadeh, Milad; Gordillo, Francisco

doi:10.1016/j.energy.2019.04.218

Cited by 167 publications

(57 citation statements)

References 42 publications

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“…In which x = sin ( t∕10) . In order to have a quantitative comparison, consider the criterion Fitness = ∫ 10 0 � ∑ i e 2 i � dt [43][44][45][46][47][48][49][50][51][52]. All the controller parameters are the same as those presented in simulation 1.…”

Section: Simulation 2: Comparison Between Different Uncertainty Estimmentioning

confidence: 99%

Design of an adaptive dynamic sliding mode control approach for robotic systems via uncertainty estimators with exponential convergence rate

2020

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In this work, an adaptive dynamic sliding mode control approach is proposed for robotic systems via uncertainty estimators with exponential convergence rate. The uncertainties are estimated using various uncertainty estimators such as the Fourier series expansion, Legendre polynomials and adaptive fuzzy systems. Also, for each uncertainty estimator, the approximation error is compensated. The adaptation laws are derived using a stability analysis. Moreover, the asymptotic convergence of the tracking error and the boundedness of all closed-loop signals are guaranteed. The novelty of this paper is proposing a positive exponential function for the convergence rate of the adaptation rules to prevent from initial high voltages originated from large initial tracking errors. Another novelty of this paper is presenting a robust control term for the truncation error that improves the accuracy of the control system. Analysis of simulations reveals the effectiveness of the proposed method in terms of fast disturbance rejection and negligible tracking error.

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Section: Simulation 2: Comparison Between Different Uncertainty Estimmentioning

confidence: 99%

Design of an adaptive dynamic sliding mode control approach for robotic systems via uncertainty estimators with exponential convergence rate

2020

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“…Other methods used data mining techniques like support vector machine [21][22][23][24], K-nearest neighbor as in [25][26][27]. In addition, there are some optimization techniques such as genetic algorithm and particle swarm in were used to predict and improve the solar system depending on environmental factors such as the temperature, wind, and cloud [28][29][30][31].…”

Section: Fig 3 Pv and Wind Energy Project Distributionmentioning

confidence: 99%

An Efficient Intelligent Power Detection Method for Photovoltaic System

Mansour

Abdallah

Obeidat

2020

International Journal of Circuits, Systems and Signal Processing

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Jordan has experienced a significant increase in both peak load and annual electricity demand within the last decade due to the growth of the economy and population. Photovoltaic (PV) system is one of the most popular renewable energy source in Jordan. PV system is highly nonlinear with unpredictable behavior since it is always subject to many external factors such as severe weather conditions, irradiance level, sheds, temperature, etc. This makes it difficult to maintain maximum power production around its operation ranges. In this paper, an intelligent technique is used to predict and identify the working ability of the PV system under different weather factors in Tafila Technical University (TTU) in Jordan. It helps in optimizing power productions for different operation points. The PV system in Tafila with size 1 MWp PV generated 5.4 GWh since 2017. It saves about € 1.5 million in three years. A real power data from the PV system and a weather data from world weather online site of TTU location are used in this study. Decision tree technique is employed to identify the relation between the output power and weather factors. The results show that the system accuracy is 82.01% during the training phase and 93.425 % on the validation set.

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“…Number of cells in parallel [61] 7.7301E − 04 NA NA NA LI [62] 1.0548E − 03 NA NA NA TSLLS [63] 7.7301E − 04 NA NA NA Tong and Pora [49] 1.5051E − 03 NA NA NA Tayyan [64] 2.9117E − 03 NA NA NA MABC [65] 9.862E − 04 NA NA NA ABSO [66] 9.9124E − 04 NA NA NA BBO-M [67] 9.8634E − 04 NA NA NA GGHS [68] 9.9078E − 04 NA NA NA CARO [69] 9.8665E − 04 NA NA NA SOS [24] 9.8609E − 04 1.1982E − 03 1.0245E − 5.2184E − 05 MSSO [70] 9.8607E − 04 NA NA NA CWOA [21] 9.8604E − 04 NA NA 1.0216E − 08 CSO [71] 9.8602E − 04 NA 9.8602E − 5.4941E − 09 MADE [72] 9.8602E − 04 9.8602E − 04 9.8602E − 2.74E − 15 EO-Jaya [73] 9.8603E − 04 NA NA NA ILCOA [74] 9.8602E − 04 NA NA NA FPSO [75] 9.8602E − 04 NA NA 2.0142E − 08 PGJAYA [76] 9.8602E − 04 9.8602E − 04 9.8602E − 1.4485E − 09 OBWOA [77] 9.8602E − 04 NA NA NA ABC-TRR [78] 9.8602E − 04 9.8602E − 04 9.8602E − 6.15E − 17 NM-MPSO [79] 9.8602E − 04 NA NA NA SDO 9.8602E − 04 9.8616E − 04 9.8603E − 2.5141E − 08 DDM MABC [65] 9.8276E − 04 NA NA NA ABSO [66] 9.8344E − 04 NA NA NA BBO-M [67] 9.8272E − 04 NA NA NA IGHS [68] 9.8635E − 04 NA NA NA CARO [69] 9.8260E − 04 NA NA NA SOS [24] 9.8518E − 04 1.3498E − 03 1.0627E − 9.6141E − 05 MSSO [70] 9.8281E − 04 NA NA NA CWOA [21] 9.8279E − 04 NA NA 1.1333E − 07 CSO [71] 9.8252E − 04 NA 9.9619E − 3.4681E − 05 MADE [72] 9.8261E − 04 9.8786E − 04 9.8608E − 8.02E − 05 EO-Jaya [73] 9.8262E − 04 NA NA NA ILCOA [74] 9.8257E − 04 NA NA NA FPSO [75] 9.8253E − 04 NA NA 3.1469E − 08 PGJAYA…”

Section: Nomenclature I Dmentioning

confidence: 99%

Application of Supply‐Demand‐Based Optimization for Parameter Extraction of Solar Photovoltaic Models

et al. 2019

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Modeling solar photovoltaic (PV) systems accurately is based on optimal values of unknown model parameters of PV cells and modules. In recent years, the use of metaheuristics for parameter extraction of PV models gains more and more attentions thanks to their efficacy in solving highly nonlinear multimodal optimization problems. This work addresses a novel application of supply-demand-based optimization (SDO) to extract accurate and reliable parameters for PV models. SDO is a very young and efficient metaheuristic inspired by the supply and demand mechanism in economics. Its exploration and exploitation are balanced well by incorporating different dynamic modes of the cobweb model organically. To validate the feasibility and effectiveness of SDO, four PV models with diverse characteristics including RTC France silicon solar cell, PVM 752 GaAs thin film cell, STM6-40/36 monocrystalline module, and STP6-120/36 polycrystalline module are employed. The experimental results comparing with ten state-of-the-art algorithms demonstrate that SDO performs better or highly competitively in terms of accuracy, robustness, and convergence. In addition, the sensitivity of SDO to variation of population size is empirically investigated. The results indicate that SDO with a relatively small population size can extract accurate and reliable parameters for PV models.

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Parameters identification of PV solar cells and modules using flexible particle swarm optimization algorithm

Cited by 167 publications

References 42 publications

Design of an adaptive dynamic sliding mode control approach for robotic systems via uncertainty estimators with exponential convergence rate

Design of an adaptive dynamic sliding mode control approach for robotic systems via uncertainty estimators with exponential convergence rate

An Efficient Intelligent Power Detection Method for Photovoltaic System

Application of Supply‐Demand‐Based Optimization for Parameter Extraction of Solar Photovoltaic Models

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