Measurement Error Identification of Protection Secondary Circuit by Fusing Chebyshev Distance and Modified Cosine Similarity Algorithms

doi:10.11930/j.issn.1004-9649.202508007

Abstract

Abstract:

Correctly identifying the measurement error of the protection secondary circuit is crucial to improving protection reliability and ensuring stable operation of the power system.Therefore, a method using the entropy weight-based fusion of Chebyshev distance and modified cosine similarity algorithm (EBW-CDMCS) is proposed to accurately identify measurement errors of the protection secondary circuits. Firstly, in order to ensure the accuracy and consistency of data analysis, two sets of measured data undergo standardized preprocessing, and the distance values between the two sets of sampled data are obtained using the Chebyshev distance and the modified cosine similarity algorithm, respectively, to characterize the dispersion of the data. Then, the entropy-based weight method is used to calculate the weights of the two sets of distance values, which are then fused to obtain the EBW-CDMCS distance index. Finally, by analyzing the measured current data of the secondary circuit under normal operating conditions, an appropriate threshold is set. The calculated EBW-CDMCS distance index is compared with this threshold to identify potential measurement errors in the secondary circuit. A 220kV substation model was built based on PSCAD, and simulation results verified the effectiveness and accuracy of the proposed method.

Key words: measurement error identification, Chebyshev distance, modified cosine similarity, entropy-based weight, protection secondary circuit

WANG Haiming, WANG Jianfeng, WANG Zhongyuan, JIANG Jiali, WENG Hanli. Measurement Error Identification of Protection Secondary Circuit by Fusing Chebyshev Distance and Modified Cosine Similarity Algorithms[J]. Electric Power, 2025, 58(12): 165-177.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202508007

https://www.electricpower.com.cn/EN/Y2025/V58/I12/165

Figures/Tables 20

Fig.1 Common error

Fig.2 Actual plant topology (local)

Fig.3 Comparison of two algorithms under fixed error

Fig.4 Comparison of two algorithms under failure error

Fig.5 Comparison of two algorithms under drifting errors

Fig.6 Comparison of two algorithms under accuracy degradation error

Fig.7 Comparison of two algorithms under noisy points

Fig.8 The measured values of iSA, iTLA, and the d curve under normal operating conditions

Fig.9 the measured values of iSA, iTLA, and the d curve under fixed error

Fig.10 The measured values of iSA, iTLA, and the d curve under failure error

Fig.11 The measured values of iSA, iTLA, and the d curve under drifting error

Fig.12 The measured values of iSA, iTLA, and the d curve under accuracy degradation error

Fig.13 The measured values of iSA1, iSA2, iSA3, and the d curve under normal operating conditions

Fig.14 The measured values of iSA1, iSA2, iSA3, and the d curve under error conditions

Fig.15 The measured values of iTLA, iTLB, iTLC, and the d curve under normal operating conditions

Fig.16 The measured values of iTLA, iTLB, iTLC, and the d curve under error conditions

Fig.17 The measured values of iSA, iTLA, iTHA under normal operating conditions

Fig.18 The measured values of iSA, iTLA, iTHA (calculated), and the d curve under normal operating conditions

Fig.19 The measured values of iSA, iTLA, iTHA(calculated), and the d curve under error conditions

Table 1 Error recognition accuracy under different errors

误差值/%	固定误差/%	失效误差/%	漂移误差/%	精度下降误差/%
2	63.13	68.27	69.74	68.54
5	100.00	100.00	100.00	100.00
10	100.00	100.00	100.00	100.00

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