Reconstruction Method Based on ST-SSIM for Missing Data in Power System

doi:10.12204/j.issn.1000-7229.2022.07.012

Abstract

Abstract:

Power system data may miss during acquisition, measurement, transmission and storage, which threatens the security of power grid. Since traditional missing data reconstruction methods only consider the data distribution and ignore the spatio-temporal characteristics, a power system missing data reconstruction model called ST-SSIM (spatio-temporal seq2seq imputation model) is proposed in this paper. ST-SSIM has an encoder-decoder structure. The encoder is composed of a spatio-temporal information extraction unit which is constructed by graph convolution layer and long short-term memory cell. The decoder is composed of long short-term memory cell and full connection layer. The input of the proposed model includes power system timeseries and adjacency matrix, so ST-SSIM can realize the automatic learning of complex time-space relationship of data. In experiment, compared the proposed method with the existing methods in power grids of different scales, ST-SSIM has the highest reconstruction accuracy, which proves that ST-SSIM can effectively learn the spatio-temporal characteristics of power system data. By discussing the relationship between reconstruction error and the number of missing nodes and time span, it is verified that the reconstruction effect of the proposed model is stable.

Key words: power system, missing data reconstruction, spatio-temporal characteristics, graph convolution network, long short-term memory cell

CLC Number:

TM715

SONG Tiewei, SHI Weifeng, BI Zong. Reconstruction Method Based on ST-SSIM for Missing Data in Power System[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(7): 103-112.

Figures/Tables 14

Fig.1 Power system data tensor diagram

Fig.2 Structure of GCN

Fig.3 Structure of LSTM cell

Fig.4 Structure of ST unit

Fig.5 Structure of ST-SSIM

Fig.6 IEEE 39-node standard case

Table 1 Parameters of ST-SSIM

Fig.7 Reconstruct results of missing data

Table A1 Parameters of ST-SSIM

Table A2 Parameters of LSTM

Table A3 Parameters of ST-SSIM

Fig.8 Reconstruction error comparison of different models

Table 2 Reconstruction error of each model

Fig.9 MAE of reconstruction results

References 22

[1]	张素香, 赵丙镇, 王风雨, 等. 海量数据下的电力负荷短期预测[J]. 中国电机工程学报, 2015, 35(1): 37-42.
	ZHANG Suxiang, ZHAO Bingzhen, WANG Fengyu, et al. Short-term power load forecasting based on Big data[J]. Proceedings of the CSEE, 2015, 35(1): 37-42.
[2]	王德文, 孙志伟. 电力用户侧大数据分析与并行负荷预测[J]. 中国电机工程学报, 2015, 35(3):527-537.
	WANG Dewen, SUN Zhiwei. Big data analysis and parallel load forecasting of electric power user side[J]. Proceedings of the CSEE, 2015, 35(3): 527-537.
[3]	KIM D I, CHUN T Y, YOON S H, et al. Wavelet-based event detection method using PMU data[J]. IEEE Transactions on Smart Grid, 2017, 8(3): 1154-1162. doi: 10.1109/TSG.2015.2478421 URL
[4]	王守相, 陈海文, 潘志新, 等. 采用改进生成式对抗网络的电力系统量测缺失数据重建方法[J]. 中国电机工程学报, 2019, 39(1): 56-64, 320.
	WANG Shouxiang, CHEN Haiwen, PAN Zhixin, et al. A reconstruction method for missing data in power system measurement using an improved generative adversarial network[J]. Proceedings of the CSEE, 2019, 39(1): 56-64, 320.
[5]	韩升科, 胡飞虎, 陈之腾, 等. 基于GCN-LSTM的日前市场边际电价预测[J]. 中国电机工程学报, 2022, 42(9): 3276-3286.
	HAN Shengke, HU Feihu, CHEN Zhiteng, et al. Day ahead market marginal price forecasting based on GCN-LSTM[J]. Proceedings of the CSEE, 2022, 42(9): 3276-3286.
[6]	ZHU X H, SHEN M. Based on the ARIMA model with grey theory for short term load forecasting model[C]// 2012 International Conference on Systems and Informatics (ICSAI2012). Yantai, China: IEEE, 2012: 564-567.
[7]	郭蕴颖, 丁云峰. 基于CNN和LSTM联合预测并修正的电量缺失数据预测[J]. 计算机系统应用, 2020, 29(8): 192-198.
	GUO Yunying, DING Yunfeng. Prediction and correction of power loss data based on CNN and LSTM[J]. Computer Systems & Applications, 2020, 29(8): 192-198.
[8]	MIRANDA V, KRSTULOVIC J, KEKO H, et al. Reconstructing missing data in state estimation with autoencoders[J]. IEEE Transactions on Power Systems, 2012, 27(2): 604-611. doi: 10.1109/TPWRS.2011.2174810 URL
[9]	辜超, 白德盟, 王晶, 等. LSTM在输变电设备缺失值填补中的应用[J]. 电测与仪表, 2019, 56(5): 63-69, 142.
	GU Chao, BAI Demeng, WANG Jing, et al. Application of LSTM in filling missing value of power transmission and transformation equipment[J]. Electrical Measurement & Instrumentation, 2019, 56(5): 63-69, 142.
[10]	王子馨, 胡俊杰, 刘宝柱. 基于长短期记忆网络的电力系统量测缺失数据恢复方法[J]. 电力建设, 2021, 42(5): 1-8.
	WANG Zixin, HU Junjie, LIU Baozhu. Recovery method for missing measurement data of power systems based on long short-term memory networks[J]. Electric Power Construction, 2021, 42(5): 1-8.
[11]	WU Z Z, HUANG M X, ZHAO A P, et al. Traffic prediction based on GCN-LSTM model[J]. Journal of Physics: Conference Series, 2021, 1972(1): 012107. doi: 10.1088/1742-6596/1972/1/012107 URL
[12]	LI Z S, XIONG G, CHEN Y Y, et al. A hybrid deep learning approach with GCN and LSTM for traffic flow prediction[C]// 2019 IEEE Intelligent Transportation Systems Conference. Auckland, New Zealand: IEEE, 2019: 1929-1933.
[13]	王渝红, 沈靖, 曾琦, 等. 基于谱图论和图卷积神经网络的直流电网节点电压估计研究[J]. 电网技术, 2022, 46(2): 521-532.
	WANG Yuhong, SHEN Jing, ZENG Qi, et al. Voltage estimation for DC grid nodes based on spectral theory and graph convolutional neural network[J]. Power System Technology, 2022, 46(2): 521-532.
[14]	廖文龙, 于贇, 王煜森, 等. 基于图卷积网络的配电网无功优化[J]. 电网技术, 2021, 45(6): 2150-2160.
	LIAO Wenlong, YU Yun, WANG Yusen, et al. Reactive power optimization of distribution network based on graph convolutional network[J]. Power System Technology, 2021, 45(6): 2150-2160.
[15]	ZHOU J, CUI G Q, HU S D, et al. Graph neural networks: A review of methods and applications[J]. AI Open, 2020, 1: 57-81. doi: 10.1016/j.aiopen.2021.01.001 URL
[16]	XU D, RUAN C, KORPEOGLU E, et al. Inductive representation learning on temporal graphs[J]. 2020.
[17]	SCHLICHTKRULL M, KIPF T N, BLOEM P, et al. Modeling relational data with graph convolutional networks[C]// The Semantic Web, 2018.
[18]	武昭旭, 杨岸, 祝龙记. 基于循环神经网络的电能质量扰动识别[J]. 电力系统保护与控制, 2020, 48(18): 88-94.
	WU Zhaoxu, YANG An, ZHU Longji. Power quality disturbance recognition based on a recurrent neural network[J]. Power System Protection and Control, 2020, 48(18): 88-94.
[19]	曹梦舟, 张艳. 基于卷积-长短期记忆网络的电能质量扰动分类[J]. 电力系统保护与控制, 2020, 48(2): 86-92.
	CAO Mengzhou, ZHANG Yan. Classification for power quality disturbances based on CNN-LSTM network[J]. Power System Protection and Control, 2020, 48(2): 86-92.
[20]	王俊, 李霞, 周昔东, 等. 基于VMD和LSTM的超短期风速预测[J]. 电力系统保护与控制, 2020, 48(11): 45-52.
	WANG Jun, LI Xia, ZHOU Xidong, et al. Ultra-short-term wind speed prediction based on VMD-LSTM[J]. Power System Protection and Control, 2020, 48(11): 45-52.
[21]	TODERICI G, O'MALLEY S M, HWANG S J, et al. Variable rate image compression with recurrent neural networks[J]. Computer Science, 2015.
[22]	李应求, 安勃, 李恒通. 基于NARX及混沌支持向量机的短期风速预测[J]. 电力系统保护与控制, 2019, 47(23): 65-73.
	LI Yingqiu, AN Bo, LI Hengtong. Short-term wind speed prediction based on NARX and chaos-support vector machine[J]. Power System Protection and Control, 2019, 47(23): 65-73.