Fault traceability of power grid dispatching system based on DPHS-MDS and LambdaMART algorithm

Q2 Energy

Energy Informatics Pub Date : 2024-09-27 DOI:10.1186/s42162-024-00391-7

Sheng Yang, Yuan Fu, Shengyuan Li

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Abstract

Under the background of increasing system service data, it is difficult to trace the faults of power dispatching system. The current fault tracing method has some problems, such as low precision, low efficiency and difficult troubleshooting. To solve this problem, a fault tracing method based on data partition hybrid sampling method and multiple incremental regression tree algorithm is proposed. In this paper, it first uses the hybrid sampling method of data partition and dynamic selection technology to detect the business anomaly, and then applies the clustering algorithm and the information difference graph model to realize the fault tracing of system components. The experimental results showed that the F-metric value and geometric mean value of the study design method were 0.964 and 0.685, respectively. In addition, normalized discounted cumulative gains were observed in the top 10, and the mean average precision of the top 10 was 0.752 and 0.186, respectively. The proposed method can effectively improve the fault tracing efficiency of power grid operation and maintenance personnel, and provide strong data support for the safety maintenance of power grid dispatching system.

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基于 DPHS-MDS 和 LambdaMART 算法的电网调度系统故障可追溯性

在系统业务数据不断增加的背景下，电力调度系统的故障追踪变得十分困难。目前的故障追踪方法存在精度低、效率低、故障排除困难等问题。为解决这一问题，本文提出了一种基于数据分区混合采样法和多元增量回归树算法的故障追踪方法。本文首先利用数据分区混合采样方法和动态选择技术检测业务异常，然后应用聚类算法和信息差图模型实现系统组件的故障追踪。实验结果表明，研究设计方法的 F 度量值和几何平均值分别为 0.964 和 0.685。此外，还观察到归一化折现累积增益进入了前 10 名，前 10 名的平均精度分别为 0.752 和 0.186。所提出的方法能有效提高电网运维人员的故障追踪效率，为电网调度系统的安全维护提供有力的数据支持。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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