基于IAR‑CI模型的配变重过载预测方法  

Overload prediction method of distribution transformer based on IAR‑CI model

作　　者：张少峰 王佳琳 李润润 段笑阳 孙辰昊 陈春[3] ZHANG Shaofeng;WANG Jialin;LI runrun;DUAN Xiaoyang;SUN Chenhao;CHEN Chun(Sanmenxia Electric Company,State Grid Hebei Electric Power Co.,Ltd.,Sanmenxia 472000,China;Lushi Electric Company,State Grid Hebei Electric Power Co.,Ltd.,Sanmenxia 472200,China;School of Electrical&Information Engineering,Changsha University of Science&Technology,Changsha 410114,China)

机构地区：[1]国网河南省电力公司三门峡供电公司,河南三门峡472000 [2]国网河南省电力公司卢氏县供电公司,河南三门峡472200 [3]长沙理工大学电气与信息工程学院,湖南长沙410114

出　　处：《电力科学与技术学报》2024年第5期67-76,共10页Journal of Electric Power Science And Technology

基　　金：国网河南省电力公司科技项目资助(5217I0230002)。

摘　　要：随着电力系统的数字化和智能化发展,配变重过载预测成为了实现智能状态检修的关键技术之一。配变过载时空因子在现实场景中通常呈偏置分布。其中,部分高风险罕见(high risk and rare,HRR)因子一旦出现,将对变压器造成无法逆转的伤害。为此,该文提出一种基于提高关联规则关键重要性(improved association rules‐criticality importance,IAR‐CI)模型的配变过载预测方法。首先,考虑内部与外部因素,收集多个数据源并建立配变运行状态数据库,且通过ICA识别与配变重过载强关联的罕见高危时段与HRR;其次,基于关键性重要度(criticality importance,CI)度量计算,设计一种因子权重计算方法,准确衡量因子的风险权重;最后,应用TBFP‐Growth算法,增强模型的运行效率。采用中国南方某地区电网数据进行算例仿真。研究表明,该方法能够提升配变重过载的预测性能,有助于后续巡检、检测策略的合理统筹和科学规划,可在降低电力设备运维检修成本的同时提高供电的可靠性。With the digitization and intellectualization of power systems,the prediction of distribution transformer overload has become one of the key technologies for realizing intelligent condition-based maintenance.In real-world scenarios,the spatiotemporal factors of distribution transformer overload often exhibit a biased distribution,among which some high-risk and rare(HRR)factors,once occurred,can cause irreversible damage to transformers and should not be ignored.Therefore,this paper proposes a prediction method for distribution transformer overload based on the improved association rules-criticality importance(IAR-CI)model.Firstly,considering both internal and external factors,multiple data sources are collected to establish a database of distribution transformer operating states,and ICA is used to identify rare high-risk periods and HRR factors that are strongly associated with severe transformer overload.Secondly,based on the criticality importance(CI)metric calculation,a factor weighting method is designed to accurately measure the risk weight of each factor.Finally,the TBFP-Growth algorithm is applied to enhance the operational efficiency of the model.Simulation analysis conducted in a region in southern China demonstrates that the proposed method can improve the prediction performance of severe distribution transformer overload,facilitating the reasonable planning and scientific scheduling of subsequent inspection and testing strategies.This reduces the operation and maintenance costs of power equipment while enhancing the reliability of power supply.

关 键 词：配变 重过载 关联规则 关键性重要度(CI) 高风险罕见因子(HRR) 

分 类 号：TM421[电气工程—电器]