A New Approach of Determination of Comprehensive Weight in Condition Assessment of Power Transformer

Zhao, Bao Guang; Zhao, Xin; Wang, Cheng Zhang; Tian, Chun Zheng; Zhao, Xiang; Li, Ke Jun

doi:10.4028/www.scientific.net/amm.273.365

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…Therefore, the condition of the transformer cannot be accurately assessed by DGA alone.Many researchers also integrate multiple condition quantities in addition to dissolved gas data to assess the overall state of the transformer. Most of the currently used methods are data mining and intelligent algorithms such as combination of subjective and objective weights (Zhao et al, 2013), cloud models (Li et al, 2016;Du and Sun, 2020), fuzzy theory (Luo et al, 2007;Li et al, 2015), object element theory (Tan et al, 2020), set-pair analysis (Liao et al, 2010), association rules (Li et al, 2013) and their improvement algorithms. (Khalyasmaa et al, 2019) used random forest to solve the problem of actual technical status of operating power transformers.…”

Section: Introductionmentioning

confidence: 99%

Research on Variable Weight Synthesizing Model for Transformer Condition Assessment

et al. 2022

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Transformer is one of the important equipment in the power grid, which helps to integrate renewable energy into the transmission and distribution network efficiently. The safe and stable operation of transformer is of great importance for the reliable transmission of electricity generated from renewable energy and for the reliable use of electricity by the end users. Therefore, it is important to assess the condition to avoid the faults of the transformer. In this paper, a variable weight synthesizing assessment model is presented that combines the G1 method, the entropy weight method, and a variable-weight method proposed in this paper to assess the condition of transformer based on the offset of the transformer equivalent circuit parameters. First, we propose deterioration indexes oriented to the maintenance management needs, which can well reflect the degree of deterioration of each transformer component. Second, the various defects of the transformer are used as the assessment indexes, and the initial weight is given to the assessment indexes according to the damage degree of the defect. The initial weight is calculated comprehensively by the G1 method and the entropy weight method. Then, each index is scored according to the offset of the equivalent circuit parameters, and the weights are adjusted appropriately according to the scores of the indicators using a variable weighting method to emphasize the severity of the defect or the “sub-health” condition of the transformer. Finally, the respective scores and combined weights of the assessment indexes are weighted to obtain a comprehensive score. The simulation shows that the model is more sensitive to abnormal and “subhealth” conditions of the transformer, which verifies the feasibility of the variable weight synthesizing model to assess the condtion of the transformer.

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