基于CNN-GRU的光伏电站电压轨迹预测

doi:10.11930/j.issn.1004-9649.202112009

中国电力 ›› 2022, Vol. 55 ›› Issue (7): 163-171.DOI: 10.11930/j.issn.1004-9649.202112009

基于CNN-GRU的光伏电站电压轨迹预测

冯裕祺¹, 李辉^1,2, 李利娟¹, 周彦博¹, 谭貌¹, 彭寒梅¹

1. 湘潭大学自动化与电子信息学院，湖南湘潭 411105;
2. 湘潭大学多能协同控制技术湖南省工程研究中心，湖南湘潭 411105

收稿日期:2021-12-09 修回日期:2022-04-27 出版日期:2022-07-28 发布日期:2022-07-20
作者简介:冯裕祺（1996—），男，硕士研究生，从事微电网电能质量态势感知研究，E-mail：1252988056@qq.com;李辉（1974—），男，通信作者，博士，硕士生导师，从事并网逆变器控制技术、微电网电能质量态势感知与态势利导研究，E-mail：lihui7402@126.com;李利娟（1980—），女，博士，教授，博士生导师，从事智能电网研究，E-mail：lilijuan204@126.com
基金资助:
国家自然科学基金资助项目（高比例并网风电分钟级波动影响下的电力系统脆弱性分析及性能优化，52077189）；湖南省自然科学基金资助项目（极端事件下含微网配电系统的弹性评估及弹性提升运行方法研究，2020JJ4580）。

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

FENG Yuqi¹, LI Hui^1,2, LI Lijuan¹, ZHOU Yanbo¹, TAN Mao¹, PENG Hanmei¹

1. School of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China;
2. Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, Xiangtan University, Xiangtan 411105, China

Received:2021-12-09 Revised:2022-04-27 Online:2022-07-28 Published:2022-07-20
Supported by:
This work is supported by National Natural Science Foundation of China (Power System Vulnerability Analysis and Performance Optimization Under the Minute-level Fluctuation Influence of High Proportion Grid-connection Wind Power, No.52077189), Natural Science Foundation of Hunan Province (Research on Resilience Evaluation and Resilience Enhancement Operation Method of Distribution System with Microgrids Under Extreme Events, No.2020JJ4580).

摘要/Abstract

摘要： 光伏电站出力随机性易引发并网点电压大幅度波动，通过趋势预测提前调控是提高电压稳定性的有效途径。为了提升电压趋势预测精度，提出一种基于卷积神经网络（convolutional neural networks，CNN）和门控循环单元（gated recurrent unit，GRU）的电压轨迹预测方法。首先，通过采集单元提取电压数据构建时间序列；然后，计算电压时间序列的自相关系数及其与外部变量间的最大信息系数（maximal information coefficient，MIC），分析电压时间序列与外部变量在时序上的关联性；再通过CNN网络提取输入数据的高层特征；最后输入至GRU网络完成电压轨迹预测。通过某地光伏电站实测数据进行验证，结果表明：本文模型与GRU、长短期记忆网络（long short-term memory，LSTM）、CNN-LSTM、支持向量回归（support vector regression，SVR）等模型相比预测准确度更高。

关键词: 光伏电站, 电压轨迹预测, 最大信息系数, 卷积神经网络, 门控循环单元

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

冯裕祺, 李辉, 李利娟, 周彦博, 谭貌, 彭寒梅. 基于CNN-GRU的光伏电站电压轨迹预测[J]. 中国电力, 2022, 55(7): 163-171.

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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基于CNN-GRU的光伏电站电压轨迹预测

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

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基于CNN-GRU的光伏电站电压轨迹预测

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

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