电力工程技术2026，Vol.45Issue(2)：70-79,10.DOI:10.12158/j.2096-3203.2026.02.008
基于OCSVM的行业负荷特征异常辨识方法
OCSVM-based method for identifying abnormal load characteristics in industry
摘要
Abstract
To address the challenge faced by power grid companies in accurately detecting changes in user industry information,which has been complicated by the increasing variability of industry characteristics in recent years,a data-driven approach for identifying anomalies in load characteristics is proposed.Initially,a two-stage methodology for developing typical load patterns for various industries is presented.The hierarchical density-based spatial clustering of applications with noise(HDBSCAN)technique is utilized to extract typical daily load curves for users under different scenarios.Subsequently,these extracted daily load curves are clustered using an improved K-means algorithm to establish typical load patterns for the respective industries.In the second phase,a multidimensional intelligent diagnostic method for load characteristic anomalies is introduced.User load characteristics are constructed,and the entropy weight method is employed to evaluate the relative significance of typical industry scenarios.The one-class support vector machine(OCSVM)algorithm is then utilized to quantify the degree of anomaly present in user load characteristics across each scenario.Comprehensive suspicion scores are calculated and ranked to accurately identify users exhibiting abnormal load characteristics.The effectiveness of the proposed method is validated through the analysis of actual user data from a specific region.The results demonstrate that the method is both feasible and practical for constructing typical industry load scenarios and for the identification of load characteristic anomalies.关键词
数据驱动/负荷特征异常/基于层次密度的含噪声应用空间聚类(HDBSCAN)-改进K-means算法/多维场景分析/单分类支持向量机(OCSVM)/综合嫌疑得分分类
信息技术与安全科学引用本文复制引用
陈光宇,杨光,施蔚锦,蔡鑫灿,陈婉清,刘昊..基于OCSVM的行业负荷特征异常辨识方法[J].电力工程技术,2026,45(2):70-79,10.基金项目
国家自然科学基金资助项目(52107098) （52107098）
