引用本文: | 宋雨露,樊艳芳,刘牧阳,等.基于SC-DNN和多源数据融合的新能源电力系统状态估计方法[J].电力系统保护与控制,2023,51(9):177-187.[点击复制] |
SONG Yulu,FAN Yanfang,LIU Muyang,et al.State estimation method of a new energy power system based on SC-DNN andmulti-source data fusion[J].Power System Protection and Control,2023,51(9):177-187[点击复制] |
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摘要: |
大规模新能源并网重塑了电力系统的控制运行特性,现有的电力系统状态估计方法面临新能源波动数据识别困难、估计精度低、估计速度慢等问题。为改善现有方法的不足,提出了一种基于残差连接(skip connection, SC)-深度神经网络(deep neural network, DNN)和多源数据融合的新能源电力系统状态估计方法。首先采用基于双向长短期神经网络(bidirectional long short-term memory, BILSTM)预测的改进插值法进行多源数据融合。然后利用联合时空交叉机制和BILSTM网络的数据辨识技术替代传统的量测量突变检测法,以便更好地处理新能源波动数据。最后根据原始量测数据集建立基于SC-DNN的状态估计模型,把残差模块的拟合优势和神经网络的速度优势结合起来,从而实现状态估计精度和速度的提高。基于IEEE39节点系统和新疆某地区实网的算例分析表明,相比于传统方法,所提方法能在更准确地分辨新源波动数据与不良数据的同时提高状态估计的精度和速度。 |
关键词: 状态估计 深度神经网络 时空交叉机制 多源数据融合 残差连接 |
DOI:10.19783/j.cnki.pspc.221165 |
投稿时间:2022-03-31修订日期:2022-08-25 |
基金项目:国家自然科学基金项目资助(51767023);2022天山英才培养计划(2022TSYCLJ0019) |
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State estimation method of a new energy power system based on SC-DNN andmulti-source data fusion |
SONG Yulu1,FAN Yanfang1,LIU Muyang1,BAI Xueyan1,ZHANG Xinyu2 |
(1. School of Electrical Engineering, Xinjiang University, Urumqi 830047, China; 2. Electric Power Research
Institute of State Grid Xinjiang Electric Power Co., Ltd., Urumqi 830011, China) |
Abstract: |
Large-scale new energy grid connection has reshaped the control and operational characteristics of the power system. The existing power system state estimation methods face problems such as difficulties in identifying new energy fluctuation data, low estimation accuracy, and slow estimation speed. To help overcome the shortcomings of existing methods, a new energy power system state estimation method based on a skip connection (SC)-deep neural network (DNN) and multi-source data fusion is proposed. First, an improved interpolation method based on bidirectional long short-term memory (BILSTM) prediction is used for multi-source data fusion. Then a joint spatiotemporal crossover mechanism and data identification technology of the BILSTM network are used to replace the traditional measurement mutation detection method, thereby better handling new energy fluctuation data. Finally, a state estimation model based on SC-DNN is established according to the original measurement data set, and the fitting advantage of the residual module and the speed advantage of the neural network are combined, so as to improve the accuracy and speed of state estimation. An example analysis based on the IEEE39 bus system and a real network in an area of ??Xinjiang shows that, compared with the traditional method, the proposed method can more accurately distinguish new source fluctuation data and bad data, and at the same time improve the accuracy and speed of state estimation. |
Key words: state estimation deep neural network spatiotemporal intersection mechanism multi-source data fusion skip connection |