高压变电站涉鸟故障风险评估方法研究

Study on bird-related faults risk assessment methods in high-voltage substations

近年来,高压变电站涉鸟故障频发,严重威胁变电站的安全稳定运行。为评估高压变电站涉鸟故障风险,从故障机理、故障敏感性、故障后果三个维度构建了变电站涉鸟故障风险评估体系,提出了一种基于球形模糊理论组合赋权和逼近理想解排序法(Technique for Order Preference by Similarity to Ideal Solution,TOPSIS)的变电站涉鸟故障风险评估方法,采用此方法对江西省内88座变电站进行风险评估并划分为三个风险等级。结果表明:球形模糊理论改进的层次分析法削弱了非必要的主观特性,基于最小离差函数的组合赋权法兼顾了主观判断和数据信息,得到的结果相比于单一主、客观赋权更合理;引入马氏距离和灰色关联度的TOPSIS克服了欧氏距离的缺陷,提高了评估准确性,并通过历史涉鸟故障数据进行了验证。结果可指导变电站涉鸟故障差异化防治。

In recent years,bird-related faults have been increasingly frequent in high-voltage substations,posing a serious threat to their safe and stable operation.To assess the risk of bird-related faults in high-voltage substations,we constructed a comprehensive risk assessment system covering three dimensions:fault mechanism,fault sensitivity,and fault consequence.We propose a bird-related fault risk assessment method for high-voltage substations,utilizing a combination of spherical fuzzy theory and the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS).Initially,the weight of each index is determined through Spherical Fuzzy Analytic Hierarchy Process(SFAHP)and Criteria Importance Through Intercriteria Correlation(CRITIC).Subsequently,the Mahalanobis distance and grey correlation degree between each sample and positive and negative ideal solutions are calculated using TOPSIS,and the risk assessment value is obtained through weighting.Finally,this method is applied to evaluate the risk of 88 substations in Jiangxi Province,categorizing them into three risk levels.It is noteworthy that the largest number of substations,totaling 43,fall under grade Ⅲ risk level.The results indicate that,in comparison to the Analytic Hierarchy Process(AHP),SFAHP not only comprehensively incorporates the perspectives of each expert,but also mitigates the introduction of non-essential subjective characteristics,demonstrating higher flexibility.The combination weighting method based on the minimum deviation function takes into consideration both subjective judgment and data information,resulting in more reasonable outcomes than those obtained through single subjective or objective weighting.The TOPSIS method,incorporating Mahalanobis distance and grey correlation degree,overcomes the limitation of Euclidean distance by accounting for the correlation between indicators.This enhancement improves the accuracy of evaluation and has been validated using historical bird-related fault data.The resulting insights can effectively guide the differential prevention and control of bird-related faults in substations.