基于机器学习的高压电力变电站设备故障预测与诊断方法研究
Research on Fault Prediction and Diagnosis Method of High Voltage Power Substation Equipment Based on Machine Learning
朱泉缙1
作者信息
- 1. 国网丽水供电公司,浙江丽水 323000
- 折叠
摘要
随着电力系统的不断发展,高压电力变电站设备的故障预测与诊断显得尤为重要.传统的故障诊断方法往往依赖于人工经验和定期检测,但这种方法效率低下且准确度不高.近年来,机器学习技术的快速发展为设备故障的预测与诊断提供了新的思路.文中详细探讨了机器学习在高压电力变电站设备故障预测与诊断中的优势,并深入研究了基于机器学习的故障预测模型与诊断方法.通过实验验证机器学习模型在故障预测与诊断中表现出色,为电力系统的稳定运行提供了有力支持.
Abstract
With the continuous development of the power system,fault prediction and diagnosis for high-voltage power substation equipment have become increasingly crucial.Traditional fault diagnosis methods often rely on manual experience and regular inspections,but this approach is inefficient and lacks accuracy.In recent years,the rapid development of machine learning technology has provided new insights for equipment fault prediction and diagnosis.This article explores in detail the advantages of machine learning in fault prediction and diagnosis for high-voltage power substation equipment,and conducts in-depth research on fault prediction models and diagnostic methods based on machine learning.Through experimental validation,machine learning models have shown excellent performance in fault prediction and diagnosis,providing strong support for the stable operation of the power system.
关键词
机器学习/高压电力变电站/故障预测/故障诊断Key words
machine learning/high-voltage power substation/fault prediction/fault diagnosis引用本文复制引用
出版年
2024
NETL
NSTL
万方数据