基于因子分析与统计学技术的保护测量回路误差评估

doi:10.11930/j.issn.1004-9649.202309022

摘要/Abstract

摘要：

为提高保护测量回路可靠性和保证电力系统安全运行，须及时解决保护装置的测量误差问题。针对保护测量回路，构建了基于因子分析与统计学技术的误差评估方法。该方法基于三相数据间具有线性强相关性的基本原理，对保护测量回路获取的三相电流进行了因子分析，构建了误差启动判据；基于因子分析的结果，采用统计学技术中的3种经典统计量和控制阈值实现了误差源定位，达到了误差小于5%的评估精度。仿真结果证明了基于因子分析与统计学技术的保护测量回路误差评估方法的有效性与准确性。

关键词: 保护测量回路, 误差评估, 因子分析, 统计学技术

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

易亚文, 江传宾, 龚世玉, 秦小元, 赵静朴, 李振兴. 基于因子分析与统计学技术的保护测量回路误差评估[J]. 中国电力, 2023, 56(12): 183-190.

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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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202309022

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

图/表 16

图 1 因子分析算法流程

Fig.1 Factor analysis algorithm flow

表 1 前提性检验KMO&Bartlett

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

图 2 保护测量回路误差评估流程

Fig.2 Error assessment process for protective measuring loop

图 3 某电站保护测量回路仿真模型

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

图 4 样本数据与5%测量误差

Fig.4 Sample data and 5% measurement error

表 2 前提性检验结果

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

表 3 公共因子个数选取

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

图 5 Q统计算法误差评估

Fig.5 Q statistical algorithm error evaluation

图 6 $T_{\rm H}^2 $ 统计算法误差评估

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

图 7 T2统计算法误差评估

Fig.7 T2 statistical algorithm error evaluation

表 4 不同统计算法的灵敏度分析

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

图 8 GZ 误差定位

Fig.8 GZ error positioning

图 9 电网波动训练集

Fig.9 Test set on normal fluctuation of grid

图 10 T2 统计算法正常不误判

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

图 11 T2统计算法误差评估

Fig.11 T2 statistical algorithm error evaluation

图 12 GZ误差定位

Fig.12 GZ error positioning

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