A study of improved isolation forest algorithm for data management of transmission line defects and hazards

Abstract

The data management method for transmission line defects and hidden dangers enables timely identification and resolution of safety risks in transmission lines, thereby reducing the probability of failures. However, existing data on defects and hidden dangers are often affected by redundant interference, resulting in low mining accuracy. To address this issue, this paper proposes a data management approach for transmission line defects based on an improved isolation forest algorithm. The types of transmission line hidden dangers are analyzed, and a data governance framework for such hidden dangers is established. This framework collects basic data of transmission lines through multiple channels, performs denoising and normalization processing, and constructs a sample dataset for transmission lines. The isolation forest algorithm is selected as the method for detecting hidden trouble data in transmission lines. The algorithm is enhanced using binary particle swarm optimization to improve the detection of hidden trouble data. The detected defect data are applied to the early warning of transmission lines, thereby completing the defect data management process. Experimental results demonstrate that the proposed method can quickly and accurately detect defect data in transmission lines, and the detection results can effectively facilitate risk warning for transmission lines.