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A method for assessing and locating protection measurement loop errors based on an improved similarity algorithm.

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Title: A method for assessing and locating protection measurement loop errors based on an improved similarity algorithm.
Authors: Mu, Zong Jun1 (AUTHOR) 1299091440@qq.com, Liu, Deng Xin1 (AUTHOR), Hu, Bin1 (AUTHOR), Li, Zhen1 (AUTHOR)
Source: Electrical Engineering. Mar2025, Vol. 107 Issue 3, p3725-3736. 12p.
Subjects: Euclidean algorithm, Measurement errors, Entropy, Algorithms
Abstract: It is of importance to detect and locate the errors of the protection measurement loop of the relay protection device for ensuring correct and timely functioning. The complex and changeable power system environment makes error detection and localization challenging. To this end, this paper proposes an entropy weight method-Euclidean distance and Tanimoto similarity (EWM-EDTS)-based method that integrates Euclidean distance and Tanimoto similarity with the entropy weight method. The Euclidean distance algorithm and Tanimoto similarity algorithm are used to calculate and obtain the similarity values between two sequences of samples and then the entropy weight method is used to calculate the weighting coefficients to fuse the two sequences of similarity values to finally obtain the EWM-EDTS distance. By comparing the value of the EWM-EDTS distance with the distance threshold, potential errors in the measurement data can be accurately located and identified. The simulation based on PSCAD shows that the proposed method can significantly improve the accuracy of error detection and localization. [ABSTRACT FROM AUTHOR]
Copyright of Electrical Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A method for assessing and locating protection measurement loop errors based on an improved similarity algorithm.
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  Data: It is of importance to detect and locate the errors of the protection measurement loop of the relay protection device for ensuring correct and timely functioning. The complex and changeable power system environment makes error detection and localization challenging. To this end, this paper proposes an entropy weight method-Euclidean distance and Tanimoto similarity (EWM-EDTS)-based method that integrates Euclidean distance and Tanimoto similarity with the entropy weight method. The Euclidean distance algorithm and Tanimoto similarity algorithm are used to calculate and obtain the similarity values between two sequences of samples and then the entropy weight method is used to calculate the weighting coefficients to fuse the two sequences of similarity values to finally obtain the EWM-EDTS distance. By comparing the value of the EWM-EDTS distance with the distance threshold, potential errors in the measurement data can be accurately located and identified. The simulation based on PSCAD shows that the proposed method can significantly improve the accuracy of error detection and localization. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Electrical Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Mar2025
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