Non-intrusive Power Load Identification Model Based on FPCA and KELM

doi:10.12204/j.issn.1000-7229.2021.02.011

Abstract

Abstract:

Non-intrusive power load monitoring and identification is the key technology to realize customer-side intelligent sensing in the ubiquitous power Internet of things. Aiming at the problems of high feature redundancy, low identification accuracy and low calculation efficiency in the existing identification model, a non-intrusive power load identification model based on Fisher principal component analysis and kernel extreme learning machine is proposed. Firstly, the steady-state characteristics such as current, power and harmonic contents are selected as the original input variables, and the Fisher principal component analysis (FPCA), which combines Fisher score and principal component analysis, is used to eliminate the invalid features with poor separability and to eliminate the correlation among the effective features at the same time. Then, radial basis function is introduced to build the network structure, and genetic algorithm (GA) is used to optimize the model parameters such as penalty coefficient, so as to build the kernel extreme learning machine(KELM) classification model for load identification. Finally, the open TIPDM load data set is used for example analysis. The simulation results show that the proposed model has better identification accuracy and calculation efficiency than the traditional load identification models, and it can effectively identify common household loads.

Key words: non-intrusive load identification, Fisher score, principal component analysis, genetic algorithm(GA), kernel extreme learning machine(KELM)

CLC Number:

TM73

TONG Ruining, LI Peng, LANG Xun, SHEN Xin, CAO Min. Non-intrusive Power Load Identification Model Based on FPCA and KELM[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(2): 85-92.

Figures/Tables 9

Fig.1 Contents of the 2nd to 25th harmonic currents of some electrical appliances

Fig.2 Flow chart of non-intrusive power load identification based on FPCA and KELM

Table 1 Parameters of GA

Fig.3 Network structure of FPCA-GA-KELM

Table 2 Fisher scores of 17 steady-state characteristics

Table 3 Eigenvalues of the covariance matrix of 8 variables

Table 4 Classification accuracy of the test samples under different characteristics

Table 5 Test classification accuracy of four different identification models

Fig.4 Time-consuming of the model calculation increasing with the number of samples

References 21

[1]	CORBETT J, WARDLE K, CHEN C. Toward a sustainable modern electricity grid:The effects of smart metering and program investments on demand-side management performance in the US electricity sector 2009-2012[J]. IEEE Transactions on Engineering Management, 2018,65(2):252-263.
[2]	王继业, 季知祥, 史梦洁, 等. 智能配用电大数据需求分析与应用研究[J]. 中国电机工程学报, 2015,35(8):1829-1836.
	WANG Jiye, JI Zhixiang, SHI Mengjie, et al. Scenario analysis and application research on big data in smart power distribution and consumption systems[J]. Proceedings of the CSEE, 2015,35(8):1829-1836.
[3]	王守相, 葛磊蛟, 王凯. 智能配电系统的内涵及其关键技术[J]. 电力自动化设备, 2016,36(6):1-6.
	WANG Shouxiang, GE Leijiao, WANG Kai. Main contents and key technologies of smart distribution system[J]. Electric Power Automation Equipment, 2016,36(6):1-6.
[4]	江帆, 杨洪耕. 基于选择性贝叶斯分类的非侵入式负荷识别方法[J]. 电力建设, 2019,40(2):94-99.
	JIANG Fan, YANG Honggeng. Non-intrusive load identification method based on selective Bayesian classification[J]. Electric Power Construction, 2019,40(2):94-99.
[5]	GUZEL T, USTUNEL E. Principal components null space analysis based non-intrusive load monitoring[C]∥ IEEE Electrical Power and Energy Conference. Ontario: IEEE, 2015: 420-423.
[6]	LIU B, LUAN W P, YU Y X. Dynamic time warping based non-intrusive load transient identification[J]. Applied Energy, 2017,195:634-645.
[7]	何恒靖, 王灏, 肖勇, 等. 基于Bi-LSTM算法的非侵入式负荷监测模型[J]. 南方电网技术, 2019,13(2):20-26.
	HE Hengjing, WANG Hao, XIAO Yong, et al. Non-intrusive load monitoring model based on Bi-LSTM algorithm[J]. Southern Power System Technology, 2019,13(2):20-26.
[8]	祁兵, 程媛, 武昕. 基于Fisher 有监督判别的非侵入式居民负荷辨识方法[J]. 电网技术, 2016,40(8):2484-2491.
	QI Bing, CHENG Yuan, WU Xin. Non-intrusive household appliance load identification method based on Fisher supervised discriminant[J]. Power System Technology, 2016,40(8):2484-2491.
[9]	宋旭帆, 周明, 涂京, 等. 基于k-NN结合核Fisher判别的非侵入式负荷监测方法[J]. 电力系统自动化, 2018,42(6):73-80.
	SONG Xufan, ZHOU Ming, TU Jing, et al. Non-intrusive load monitoring method based on k-NN and kernel Fisher discriminant[J]. Automation of Electric Power Systems, 2018,42(6):73-80.
[10]	DUFOURL, GENOUD D, JARA A, et al. A non-intrusive model to predict the exible energy in a residential building[C]∥ Wireless Communications and Networking Conference Workshops(WCNCW).IEEE, 2015: 69-74.
[11]	李如意, 王晓换, 胡美璇, 等. RPROP神经网络在非侵入式负荷分解中的应用[J]. 电力系统保护与控制, 2016,44(7):55-61.
	LI Ruyi, WANG Xiaohuan, HU Meixuan, et al. Application of RPROP neural network in nonintrusive load decomposition[J]. Power System Protection and Control, 2016,44(7):55-61.
[12]	江帆, 杨洪耕. 基于广义回归神经网络的非侵入式负荷识别方法[J]. 电测与仪表, 2020,57(3):1-6, 18.
	JIANG Fan, YANG Honggeng. Non-intrusive load identification method based on generalized regression neural network[J]. Electrical Measurement & Instrumentation, 2020,57(3):1-6, 18.
[13]	程祥, 李林芝, 吴浩, 等. 非侵入式负荷监测与分解研究综述[J]. 电网技术, 2016,40(10):3108-3117.
	CHENG Xiang, LI Linzhi, WU Hao, et al. A survey of the research on non-intrusive load monitoring and disaggregation[J]. Power System Technology, 2016,40(10):3108-3117.
[14]	LIANG J, NG S K, KENDALL G, et al. Load signature study,part I:Basic concept, structure, and methodology[J]. IEEE Transactions on Power Delivery, 2010,25(2):551-560.
[15]	李郅琴, 杜建强, 聂斌, 等. 特征选择方法综述[J]. 计算机工程与应用, 2019,55(24):10-19.
	LI Zhiqin, DU Jianqiang, NIE Bin, et al. Summary of feature selection methods[J]. Computer Engineering and Application, 2019,55(24):10-19.
[16]	贺川双, 杜修明, 严英杰, 等. 基于数据挖掘和主成分分析的电力设备状态评价[J]. 高压电器, 2017,53(12):34-41.
	HE Chuanshuang, DU Xiuming, YAN Yingjie, et al. State evaluation of power equipment based on data mining and principal component analysis[J]. High Voltage Apparatus, 2017,53(12):34-41.
[17]	孙朝江, 汤健, 魏忠军, 等. 基于选择性更新的在线核极限学习机建模[J]. 控制工程, 2013,20(4):659-662.
	SUN Chaojiang, TANG Jian, WEI Zhongjun, et al. Modeling of online nuclear limit learning machine based on selective update[J]. Control Engineering, 2013,20(4):659-662.
[18]	周明, 宋旭帆, 涂京, 等. 基于非侵入式负荷监测的居民用电行为分析[J]. 电网技术, 2018,42(10):3268-3276.
	ZHOU Ming, SONG Xufan, TU Jing, et al. Residential electricity consumption behavior analysis based on non-intrusive load monitoring[J]. Power System Technology, 2018,42(10):3268-3276.
[19]	徐青山, 娄藕蝶, 郑爱霞, 等. 基于近邻传播聚类和遗传优化的非侵入式负荷分解方法[J]. 电工技术学报, 2018,33(16):3868-3878.
	XU Qingshan, LOU Oudie, ZHENG Aixia, et al. Non-intrusive load decomposition method based on near neighbor propagation clustering and genetic optimization[J]. Transactions of China Electrotechnical Society, 2018,33(16):3868-3878.
[20]	DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multi-objective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002,6(2):182-197.
[21]	Dreis. Topic a of the sixth Teddy cup data mining challenge: Power data mining based on non-invasive load monitoring and decomposition [EB/OL]. [2018-11-01]. http://www.tipdm.org/bdrace/index.html.