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Research on transformer condition evaluation method based on association rule set pair analysis theory

Shanyuan Wang, Donglin Cai, Zhonghong Chen, Menglong Bai, Hongyu Ouyang, Xiaoyang Lai, Weichao Hu, Changke Chen, Yangchen Shi

Diagnostyka

2023

Vol. 24, No. 4

art. no. 2023410

Combining the advantages of set pair analysis and association rules, This paper proposes a transformer condition evaluation based on association rule with set pair analysis theory. In this paper, by analyzing the correlation between the various fault symptoms of transformer, a set of fault types is obtained. At the same time, this paper introduces variable weight formula based on the support degree and confidence degree of association rules, and finally the weight coefficients of fault types and fault symptoms are obtained. By comparing and calculating the support and confidence of association rules, while introducing variable weight formulas, the weight coefficients of fault types and fault symptoms are obtained. it effectively avoid the subjectivity of expert opinions or experiences. Based on the scalability of set pair analysis, a 5-element connection degree is adopted to improve the accuracy of handling uncertain factors in transformer fault diagnosis.

Fault location of distribution network with distributed generation based on Karrenbauer transform and support vector machine regression

Wang Siming, Zhao Kaikai

Archives of Electrical Engineering

2023

Vol. 72, nr 2

461--481

As the capacity and scale of distribution networks continue to expand, and distributed generation technology is increasingly mature, the traditional fault location is no longer applicable to an active distribution network and "two-way" power flow structure. In this paper, a fault location method based on Karrenbauer transform and support vector machine regression (SVR) is proposed. Firstly, according to the influence of Karrenbauer transformation on phase angle difference before and after section fault in a low-voltage active distribution network, the fault regions and types are inferred preliminarily. Then, in the feature extraction stage, combined with the characteristics of distribution network fault mechanism, the fault feature sample set is established by using the phase angle difference of the Karrenbauer current. Finally, the fault category prediction model based on SVR was established to solve the problem of a single-phase mode transformation modulus and the indistinct identification of two-phase short circuits, then more accurate fault segments and categories were obtained. The proposed fault location method is simulated and verified by building a distribution network system model. The results show that compared with other methods in the field of fault detection, the fault location accuracy of the proposed method can reach 98.56%, which can enhance the robustness of rapid fault location.