Experimental Parameter Extraction in the Single-Diode Photovoltaic Model via a Reduced-Space Search

Angulo, Alejandro; Carrasco, Miguel; Mancilla–David, Fernando; Street, Alexandre; Cárdenas, Roberto

doi:10.1109/tie.2016.2615590

Cited by 92 publications

(58 citation statements)

References 27 publications

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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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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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“…of photovoltaic power generation. But it gets the problem of low accuracy and robustness generally [12]- [14]. Statistical method and artificial intelligence method are popular at present, mainly using data-driven to extract features from historical data to predict the trend of photovoltaic power generation [15]- [19].…”

Section: Introductionmentioning

confidence: 99%

Ultra-Short-Term Photovoltaic Power Prediction Based on Self-Attention Mechanism and Multi-Task Learning

Sun

2020

IEEE Access

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Due to the volatility and randomness of the photovoltaic power generation, it is difficult for traditional models to predict it accurately. To solve the problem, we established a model based on the selfattention mechanism and multi-task learning to predict the ultra-short-term photovoltaic power generation. First, we selected the data with the optimal timing length and input the data into the Encoder-Decoder network based on the self-attention. The validity of features extracted by the encoder was checked by the decoder. Then, we added a restriction to the middle layer of the Encoder-Decoder network to prevent the autoencoder from copying the input to the output mechanically. This condition is used to predict the photovoltaic power generation, so a multi-task learning model was established. Finally, to take full advantage of the features that are efficiently expressed and allow our main task, the prediction task, to learn some unique features autonomously, we proposed a step-by-step training method and have validated the effectiveness of this view in experiments. Through experimental contrast, it is found that compared with the Encoder-Decoder network based on CNN and LSTM, the performance of the proposed method has been increased by 14.82% and 8.09% respectively. The RMSE and MAE of the Encoder-Decoder model based on the self-attention mechanism using step-by-step training are 0.071 and 0.040 respectively. INDEX TERMS Energy, PV power prediction, self-attention mechanism, multi-task learning, autoencoder.

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“…First a modeling of a PV selected, the PV contains a flow source, a diode, a parallel resistance, and a series resistance [24]. The voltage curve and current obtained from the characteristic circuit indicate low output power [25,26]. For an ideal PV cell, we have the Equation 1…”

Section: Introductionmentioning

confidence: 99%

Extracting Technical Specifications of a Solar Panel Type to Design a 10 MW Hybrid Power Plant

Hajipour

Hasheminejad

Haghgou

2019

IJE

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Experimental Parameter Extraction in the Single-Diode Photovoltaic Model via a Reduced-Space Search

Cited by 92 publications

References 27 publications

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

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

Ultra-Short-Term Photovoltaic Power Prediction Based on Self-Attention Mechanism and Multi-Task Learning

Extracting Technical Specifications of a Solar Panel Type to Design a 10 MW Hybrid Power Plant

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