Evaluation model of the power transformer dissolved gas analysis based on the enhanced association rule miningalgorithm
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    Abstract:

    Association rule mining methods are commonly utilized to analyze the dissolved gas which is applied to diagnose the power transformer fault events. For the purpose of improving the performance, this paper proposes a diagnosis method for power transformer fault events based on the enhanced association rule mining algorithm. Firstly, the conditional significance measurements which can be adapted for different input features are established. Thus the rarely distributed but risky data can be incorporated in analysis, and all the potential circumstances in reality can be considered. Next, the corresponding risk weights of input data are generated through their probability of causing a fault rather than their statistical distribution. Therefore, the impact of each input will be measured more precisely. Finally, Relim algorithm is applied to raise the efficiency of mining. The experimentalstudy shows that the proposed method is more pinpoint, realizable and efficient compared with the methods with the fixed significance measurements, the conventional technique to calculatethe risk weight, and Apriori algorithm.

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邓佳乐,孙辰昊,胡博,岳一石,易洲楠,李绍龙.基于改进关联规则挖掘的变压器油中溶解气体分析模型[J].电力科学与技术学报英文版,2022,37(3):165-172. DENG Jiale, SUN Chenhao, HU Bo, YUE Yishi, YI Zhounan, LI Shaolong. Evaluation model of the power transformer dissolved gas analysis based on the enhanced association rule miningalgorithm[J]. Journal of Electric Power Science and Technology,2022,37(3):165-172.

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  • Received:
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  • Online: July 24,2022
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