Rapid and accurate modeling of PV modules based on extreme learning machine and large datasets of I-V curves

Zhicong Chen, Hui Yu, Linlu Luo, Lijun Wu, Qiao Zheng, Zhenhui Wu, Shuying Cheng and Peijie Lin

Applied Energy, 2021, vol. 292, issue C, No S0306261921004098

Abstract: Efficient and accurate photovoltaic (PV) modeling plays an important role in optimal evaluation and operation of PV power systems. Using current–voltage (I-V) curves measured at different operating conditions, a novel extreme learning machine (ELM) based modeling method is proposed for characterizing the electrical behavior of PV modules, which features high training speed and generalization performance. Firstly, a voltage-current grid based method is used to resample each raw measured I-V curve for reducing data redundancy of I-V curves, a slope change based detection method is proposed to exclude the abnormal I-V curves for improving the data quality, and an irradiance-temperature grid based method is applied to downsample the dataset. Secondly, a single hidden-layer feedforward neural network (SLFN) is proposed as the model structure, which is then trained by the ELM learning algorithm. Particularly, the configuration of the ELM is optimized by cross-validation. Finally, the proposed ELM based PV modeling method is verified and tested on the preprocessed large datasets of I-V curves of six PV modules from the National Renewable Energy Laboratory. Moreover, in order to verify the advantage, the ELM based PV modeling is further compared with some commonly used machine learning based methods. Experimental results demonstrate that the proposed ELM based PV modeling method features fast training, high accuracy and generalization performance. The comparison results further indicate that the ELM based model is greatly superior in terms of the training time, and is slightly better than other algorithms in terms of the model accuracy and generalization performance.

Keywords: PV modules; PV modeling; I-V characteristics; Extreme learning machine; Machine learning (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (8)

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DOI: 10.1016/j.apenergy.2021.116929

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