[1]谢长宁,史宗尚.基于扩展隔离森林算法的小型水力发电系统故障检测研究[J].机械与电子,2025,(09):45-50.
　XIE Changning,SHI Zongshang.Research on Fault Detection in Small Hydropower Systems Based on Extended Isolation Forest Algorithm[J].Machinery & Electronics,2025,(09):45-50.
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基于扩展隔离森林算法的小型水力发电系统故障检测研究
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2025年09期
页码:
45-50
栏目:
自动控制与检测
出版日期:
2025-09-25

文章信息/Info

Title:
Research on Fault Detection in Small Hydropower Systems Based on Extended Isolation Forest Algorithm
文章编号:
1001-2257 ( 2025 ) 09-0045-06
作者:
谢长宁; 史宗尚
国家能源集团四川发电有限公司南桠河水电分公司,四川 雅安 625400
Author(s):
XIE Changning ;  SHI Zongshang
( Nanya River Hydropower Branch , CHN Energy Sichuan Electric Power Generation Co. , Ltd. , Ya ’ an 625400 , China )
关键词:
水力发电系统; 故障诊断; 随机斜率; 扩展隔离森林
Keywords:
hydropower system ;  fault diagnosis ;  random slopes ;  extended isolation forest
分类号:
TM612 ;TP181
文献标志码:
A
摘要:
传统故障诊断方法在处理非线性数据方面存在较大挑战,为此,提出一种基于扩展隔离森林( EIF )算法的小型水力发电系统故障诊断方法,以提升系统故障检测的精度和实时性。 EIF 算法通过构建二叉树来检测系统运行中的多维数据异常,利用随机斜率和截距灵活划分数据并通过随机采样数据训练隔离树,计算路径长度以确定每个数据点的异常得分。实验结果表明,EIF 算法准确性较高,可有效预测系统故障,有助于实现系统的早期预警与维护。
Abstract:
The traditional fault diagnosis methods face significant challenges in handling nonlinear data.To address this issue , this paper proposes a fault diagnosis method for small hydropower systems based on the Extended Isolation Forest ( EIF ) algorithm , aimed at enhancing the accuracy and real-time capabilities of fault detection.The EIF algorithm detects multidimensional data anomalies in system operations by constructing binary trees , flexibly partitioning the data using random slopes and intercepts , and training isolation trees through random sampling.It calculates path lengths to determine the anomaly score for each data point.Experimental results demonstrate that the EIF algorithm achieves high accuracy and effectively predicts system faults , contributing to early warning and maintenance of the system.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2024-09-27
作者简介:谢长宁 ( 1984- ),男,四川成都人,工程师,研究方向为水电站设备管理。
更新日期/Last Update: 2025-09-29