Protection Loop Error Measurement Based on Factor Analysis and Statistics Technology

doi:10.11930/j.issn.1004-9649.202309022

Abstract

Abstract:

In order to improve the reliability of the protection measurement circuit and ensure the safe operation of the power system, which requires the measurement error of the protection device to be solved in time. An error evaluation method based on factor analysis and statistical techniques is constructed for the protection measurement circuit in this paper. Based on the principle that there is strong linear correlation between the three phase data, the three-phase current obtained by the protection measurement circuit is analyzed and the error starting criterion is established. Based on the results of factor analysis, three classical statistics and control threshold in statistical techniques are used to locate the error source, and the evaluation accuracy of error less than 5% is achieved. The effectiveness and accuracy of this method are proved by simulation results.

Key words: protective measuring loop, error evaluation, factor analysis, statistics

Yawen YI, Chuanbin JIANG, Shiyu GONG, Xiaoyuan QIN, Jingpu ZHAO, Zhenxing LI. Protection Loop Error Measurement Based on Factor Analysis and Statistics Technology[J]. Electric Power, 2023, 56(12): 183-190.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202309022

https://www.electricpower.com.cn/EN/Y2023/V56/I12/183

Figures/Tables 16

Fig.1 Factor analysis algorithm flow

Table 1 Preliminary test of KMO and Bartlett

检测类别	取值范围	因子分析适合情况
λ_kmo	0.00~0.50	不适合
	0.50~0.70	适合
	0.70~1.00	很适合
λ_bar	0.00~0.01	适合
λ_bar	0.01~1.00	不适合

Fig.2 Error assessment process for protective measuring loop

Fig.3 Simulation model of protection measurement loop of a power station

Fig.4 Sample data and 5% measurement error

Table 2 Preliminary test results

KMO检验	Bartlett检验
取样适切性量数	近似卡方	自由度	显著度
0.753	1876.019	3.000	0.000

Table 3 Selection of the number of common factors

三相数据	因子编号	方差解释表
三相数据	因子编号	特征值	方差解释率/%	累积方差贡献率/%
无误差	1	1.501	50.025	50.025
	2	1.499	49.975	100.000
	3	4.178×10^–16	1.393×10^–614	100.000
有误差	1	1.500	50.004	50.004
	2	1.099	36.624	86.629
	3	0.401	13.371	100.000

Fig.5 Q statistical algorithm error evaluation

Fig.6 $T_{\rm H}^2 $ statistical algorithm error evaluation

Fig.7 T2 statistical algorithm error evaluation

Table 4 Sensitivity analysis of different statistical algorithms

统计算法	统计量最大值	优化控制阈值	灵敏度
Q	0.3486	0.3355	1.039
$T_{\rm H}^2 $	1139.0360	1089.1460	1.046
T²	3.1960	2.0020	1.593

Fig.8 GZ error positioning

Fig.9 Test set on normal fluctuation of grid

Fig.10 The T2 statistical algorithm is normal and does not misdiagnose set on normal fluctuation of grid

Fig.11 T2 statistical algorithm error evaluation

Fig.12 GZ error positioning

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