基于集成隔离森林与业务感知动态选择的电力调度数据异常检测方法OA

To address the challenges in the power dispatch automation systems,such as diverse busi-ness types,multi-dimensional monitoring,and complex anomaly patterns,this paper proposes an anomaly detection method based on business-aware hybrid selection integration.Building upon the existing Loga-rithmic Interval Isolation Forest(LIIF)framework,the method incorporates Mahalanobis distance metrics and dimensional correlation analysis to enhance local anomaly identification within multi-dimensional fea-ture spaces.A business-aware hybrid selection ensemble strategy is introduced:first,base detectors with performance biases are pre-filtered through an isolation forest-based static screening to improve true/false value reliability.Subsequently,a dynamic ensemble selection is performed for each test sample based on the consistency,business relevance,and measurement point correlation indicators,thereby balancing de-tection accuracy with computational efficiency.Experimental results on public datasets and business data-sets from a provincial power grid dispatch center demonstrate that the proposed method outperforms exist-ing approaches in comprehensive metrics such as AUC.It effectively identifies typical power dispatch a-nomalies,including data jumps,application disconnections,and remote telemetry table failures.