Bad Data Identification and Correction Based on Load Clustering by FCM Algorithm

doi:10.11930/j.issn.1004-9649.2013.10.29.5

Abstract

Abstract: In order to identify and correct the bad load data, the load profiles are clustered by using simulated annealing genetic algorithm optimized fuzzy C-means algorithm(FCM). Based on the threshold of differential coefficient which is determined by the comparison of test load profiles with its typical load profile, the bad data whose differential coefficient is greater than the threshold value is identified. A numerical case study demonstrates that this method overcomes the impact of bad data in the statistical historical data,and as a result improves the operability and practicality of bad data identification.A new bad data correction method is presented, which takes all the measurement points load information into consideration. Compared with the correction method which considers only the load information of two points before and behind the bad data measurement point, this method improves the accuracy and effectiveness of the bad data correction.

Key words: bad data, identification and correction, load curve clustering, fuzzy C-means algorithm

CLC Number:

TM744

LIU Hui-zhou, ZHOU Kai-le, HU Xiao-jian. Bad Data Identification and Correction Based on Load Clustering by FCM Algorithm[J]. Electric Power, 2013, 46(10): 29-34.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.2013.10.29.5

https://www.electricpower.com.cn/EN/Y2013/V46/I10/29

References

[1] 卢志刚,程慧琳,冯磊,等. 基于证据融合理论的多不良数据辨识[J]. 电网技术,2012,36（1）：123-128.
LU Zhi-gang, CHENG Hui-lin, FENG Lei, et al . Multi bad data identification based on evidence fusion theory[J]. Power System Technology, 2012, 36(1): 123-128.
[2] 吴军基,杨伟,葛成,等. 基于GSA的肘形判据用于电力系统不良数据辨识[J]. 中国电机工程学报,2006,26（22）：23-28.
WU Jun-ji, YANG Wei, GE Cheng, et al . Application of GSA- based elbow judgment on bad-data detection of power system [J]. Proceedings of the CSEE, 2006, 26(22): 23-28.
[3] 张国辉,王宾,潘贞存,等. 配电系统电压跌落状态估计中的不良数据辨识[J]. 电网技术,2010,34（8）：69-73.
ZHANG Guo-hui, WANG Bin, PAN Zhen-cun, et al . Bad data identification in voltage sag state estimation of distribution system[J]. Power System Technology, 2010, 34(8): 69-73.
[4] 康仁. 基于小波分析的母线负荷预测不良数据检测[J]. 中国电力,2011,44（8）： 5-8.
KANG Ren. Bad data identification based on wavelet in bus load forecasting [J]. Electric Power, 2011, 44(8): 5-8.
[5] 张晓星,程其云,周湶,等. 基于数据挖掘的电力负荷脏数据动态智能清洗[J]. 电力系统自动化,2005,29（8）：60-64.
ZHANG Xiao-xing, CHENG Qi-yun, ZHOU Quan, et al . Dynamic intelligent cleaning for dirty electric load data based on data mining[J]. Automation of Electric Power Systems, 2005, 29(8):60-64.
[6] DEPURU S S S R, WANG L F, DEVABHAKTUNI V. Support vector machine based data classification for detection of electricity theft [C]// Power Systems Conference and Exposition (PSCE), 2011, 3: 1-8.
[7] 李钊年. 电力系统状态估计中的不良数据辨识[J]. 青海大学学报：自然科学版,2001,19（1）：49-51.
LI Zhao-nian. Identification of bad data of electric power system state estimation [J]. Journal of Qinghai University: Nature Science Edition, 2001, 19(1): 49-51.
[8] 刘莉,翟登辉,姜新丽. 电力系统不良数据检测与辨识方法的现状与发展[J]. 电力系统保护与控制,2010,38（5）：143-147, 152.
LIU Li, ZHAI Deng-hui, JIANG Xin-li. Current situation and development of the methods on bad-data detection and identification of power system [J]. Power System Protection and Control, 2010, 38(5): 143-147, 152.
[9] 李培强,李欣然,罗安. 基于数理统计和模糊聚类的变电站负荷特性判别比较[J]. 中国电力,2011,44（1）：15-18.
LI Pei-qiang, LI Xin-ran, LUO An. Load characteristics discrimination analysis based on mathematical statistics and fuzzy clustering [J]. Electric Power, 2011, 44(1): 15-18.
[10] 刘莉,王刚,翟登辉. k-means聚类算法在负荷曲线分类中的应用[J]. 电力系统保护与控制,2011,39（23）：65-68,73.
LIU Li, WANG Gang, ZHAI Deng-hui. Application of k-means clustering algorithm in load curve classification [J]. Power System Protection and Control, 2011, 39(23): 65-68, 73.
[11] 蒋德珑,王克文,王祥东. 基于模糊等价矩阵聚类分析的不良数据辨识[J]. 电力系统保护与控制,2011,39（21）：1-6,11.
JIANG De-long, WANG Ke-wen, WANG Xiang-dong. Clustering method of fuzzy equivalence matrix to bad-data detection and identification [J]. Power System Protection and Control, 2011, 39(21): 1-6, 11.
[12] 李欣然,姜学皎,钱军,等. 基于用户日负荷曲线的用电行业分类与综合方法[J]. 电力系统自动化,2010,34（10）：56-61.
LI Xin-ran, JIANG Xue-jiao, QIAN Jun, et al . A classifying and synthesizing method of power consumer industry based on the daily load profile[J]. Automation of Electric Power Systems, 2010, 34(10): 56-61.
[13] RASANEN T, VOUKANTSIS D, NISKA H, et al . Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data[J]. Applied Energy, 2010, 87(11): 3538-3545.
[14] 曾博,张建华,丁蓝,等. 改进自适应模糊C均值算法在负荷特性分类的应用[J]. 电力系统自动化,2011,35（12）：42-46.
ZENG Bo, ZHANG Jian-hua, DING Lan, et al . An improved adaptive fuzzy c-means algorithm for load characteristics classification [J]. Automation of Electric Power Systems, 2011, 35(12): 42-46.
[15] GAVRILAS M, IVANOV O, GAVRILAS G. Load profiling with fuzzy self-organizing algorithms [C]// 9th Symposium on Neural Network Application in Electrical Engineering (NEUREL), Belgrade, Serbia, Sept. 25-27, 2008.
[16] CHICCO G, NOBERTO R, PIGLIONE F. Comparisons among clustering techniques for electricity customer classification[J].IEEE Transaction on Power Systems, 2006, 21(2): 933-940.
[17] 白莉媛,胡声艳,刘素华. 一种基于模拟退火和遗传算法的模糊聚类算法[J]. 计算机工程与应用,2005,36（9）：56-57.
BAI Li-yuan, HU Sheng-yan, LIU Su-hua. A fuzzy clustering algorithm based on simulated annealing and genetic algorithm [J].Computer Engineering and Applications, 2005, 36(9): 56-57.
[18] XIE X L, BENI G. A validity measure for fuzzy clustering [J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 1991, 13(8): 841-847.
[19] 张忠华. 电力系统负荷分类研究[D]. 天津：天津大学,2007.
ZHANG Zhong-hua. Study on load classification in power system [D]. Tianjin: Tianjin University, 2007.