Voltage Trajectory Prediction of Photovoltaic Power Station Based on CNN-GRU

doi:10.11930/j.issn.1004-9649.202112009

Abstract

Abstract: The output randomness of photovoltaic power stations can easily cause large voltage fluctuations at grid-connection points. Advance regulation through trend prediction is an effective way to improve voltage stability. To improve the accuracy of voltage trend prediction, this paper proposes a voltage trajectory prediction method based on convolutional neural network (CNN) and gated recurrent unit (GRU). Specifically, a time series is constructed by extracting voltage data from the acquisition unit. Then, the autocorrelation coefficient of the voltage time series and its maximal information coefficient (MIC) relative to external variables are calculated, and the correlations of the voltage time series with external variables in timing are analyzed. Finally, the high-level features of input data are extracted through the CNN network and input into the GRU network to complete voltage trajectory prediction. The measured data of a photovoltaic power station are utilized for verification. The results show that compared with GRU, long short-term memory (LSTM), CNN-LSTM, and support vector regression (SVR) models, the proposed model has higher prediction accuracy.

Key words: photovoltaic power station, voltage trajectory prediction, maximal information coefficient, convolutional neural networks, gated recurrent unit

FENG Yuqi, LI Hui, LI Lijuan, ZHOU Yanbo, TAN Mao, PENG Hanmei. Voltage Trajectory Prediction of Photovoltaic Power Station Based on CNN-GRU[J]. Electric Power, 2022, 55(7): 163-171.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202112009

https://www.electricpower.com.cn/EN/Y2022/V55/I7/163

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