|本期目录/Table of Contents|

[1]李梦诗,余达,陈子明,等.基于深度置信网络的风力发电机故障诊断方法[J].电机与控制学报,2019,23(02):114-122.[doi:10.15938/.emc.2019.02.015]
 LI Meng-shi,YU Da,CHEN Zi-ming,et al.Fault diagnosis and isolation method for wind turbines based on deep belief network[J].,2019,23(02):114-122.[doi:10.15938/.emc.2019.02.015]
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基于深度置信网络的风力发电机故障诊断方法
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《电机与控制学报》[ISSN:1007-449X/CN:23-1 408/TM]

卷:
23
期数:
2019年02
页码:
114-122
栏目:
出版日期:
2019-02-15

文章信息/Info

Title:
Fault diagnosis and isolation method for wind turbines based on deep belief network
作者:
 李梦诗 余达 陈子明 夏侯凯顺 李堉鋆 季天瑶
 ( 华南理工大学电力学院,广州510000)
Author(s):
 LI Meng-shi YU Da CHEN Zi-ming XIAHOU Kai-shun LI Yu-yun JI Tian-yao
 ( School of Electric Power Engineering,South China University of Technology,Guangzhou 510000,China)
关键词:
风力发电机 故障诊断 深度置信网络 数据驱动 基准模型
Keywords:
 wind turbine fault diagnosis and isolation deep belief network data-driven benchmark model
分类号:
TM 315
DOI:
10.15938/.emc.2019.02.015
文献标志码:
A
摘要:
为了避免严重的生产运行事故,同时降低设备运行维护成本,提高风力发电机的可靠性,本文提出一种基于深度置信网络( deep belief network,DBN) 的新型风力发电机故障诊断( fault diagnosis and isolation,FDI) 方法。本文首先通过DBN 网络构建了故障诊断模型,然后在风力发电机的基准模型中进行故障诊断仿真测试,并把该完全数据驱动型的故障诊断效果,与传统的基于模型的诊断方法和数据驱动型诊断方法的效果作对比。此外,在仿真中也采用高斯噪声来模拟风力发电机实际运行环境中的噪声,从而解决了实际使用中网络易受噪声干扰的问题,并进一步对基于DBN 的故障诊断方法进行鲁棒性测试。仿真结果表明基于DBN 的数据驱动型FDI 方法对风力发电机的故障有着更好的诊断效果,同时在有噪声干扰的环境下也保持着较为稳定的诊断效果。
Abstract:
In order to improve the reliability of wind turbines,avoid serious accidents and reduce operation and maintenance costs,a fault diagnosis and isolation ( FDI) method for wind turbines using deep belief network ( DBN) is proposed. The DBN employed no knowledge of physical model but historical data without any selection. The proposed method was evaluated in a wind turbine benchmark model,in comparison with model-based algorithms and conventional data-driven methods. Besides,considering the disturbance
in real application,extensive evaluation was taken to analyze the robustness of proposed method which applied Gaussian noise to simulate real noise. The simulation results show that the data-driven FDI method based on DBN for wind turbines achieves the highest accuracy,and it keeps stable diagnostic performance in the strong disturbance of noise.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2017 - 06 - 21
基金项目: 国家自然科学基金( 51307062)
作者简介: 李梦诗( 1982—) ,男,博士,副教授,研究方向为电力系统运行、保护与控制;
余达( 1992—) ,男,硕士研究生,研究方向为电力系统运行、保护与控制;
陈子明( 1989—) ,男,博士研究生,研究方向为电力系统运行、保护与控制;
夏侯凯顺( 1991—) ,男,博士研究生,研究方向为电力系统运行、保护与控制;
李堉鋆( 1995—) ,女,硕士研究生,研究方向为电力系统运行、保护与控制;
季天瑶( 1983—) ,女,教授,研究方向为电力系统运行、保护与控制。
通信作者: 李梦诗
更新日期/Last Update: 2019-03-24