Anomaly Identification Method of Wind Turbine Based on Genetic Algorithm-Back Propagation Neural Network and t-location Scale Model

Zeyu LI; Chuangxin GUO; Chengzhi ZHU

doi:10.7500/AEPS20190203002

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Anomaly Identification Method of Wind Turbine Based on Genetic Algorithm-Back Propagation Neural Network and t-location Scale Model

Basic Research | 更新时间：2025-06-16

- Anomaly Identification Method of Wind Turbine Based on Genetic Algorithm-Back Propagation Neural Network and t-location Scale Model
- Automation of Electric Power Systems Vol. 44, Issue 9, Pages: 95-102(2020)
- 作者机构：
  
  浙江大学电气工程学院，浙江省杭州市 310027
- 作者简介：
- 基金信息：
  
  National Key R&D Program of China;National Natural Science Foundation of China;State Grid Corporation of China
- DOI：10.7500/AEPS20190203002
  CLC：
- Received：03 February 2019，
  
  Revised：2019-10-06，
  
  Published Online：21 February 2020，
  
  Published：10 May 2020
- 稿件说明：
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李泽宇,郭创新,朱承治.采用GA-BPNN与TLS模型的风电机组异常辨识方法[J].电力系统自动化,2020,44(9):95-102.

LI Zeyu,GUO Chuangxin,ZHU Chengzhi.Anomaly Identification Method of Wind Turbine Based on Genetic Algorithm-Back Propagation Neural Network and t-location Scale Model[J].Automation of Electric Power Systems,2020,44(9):95-102.
李泽宇,郭创新,朱承治.采用GA-BPNN与TLS模型的风电机组异常辨识方法[J].电力系统自动化,2020,44(9):95-102. DOI： 10.7500/AEPS20190203002.

LI Zeyu,GUO Chuangxin,ZHU Chengzhi.Anomaly Identification Method of Wind Turbine Based on Genetic Algorithm-Back Propagation Neural Network and t-location Scale Model[J].Automation of Electric Power Systems,2020,44(9):95-102. DOI： 10.7500/AEPS20190203002.

摘要

基于反向传播神经网络（BPNN）建立了风电机组状态参数预测模型，并采用遗传算法(GA)对BPNN模型的初始权重与阈值进行优化，有效消除环境因素对风电机组状态参数的影响；采用TLS（t-location scale）分布模型刻画不同风速区间下预测残差的分布特性，基于矩估计方法实现TLS分布参数估计，并在此基础上提出了计及风速影响的状态残差异常程度量化指标。以某风电场的1.5 MW双馈风电机组为例进行了异常分析，结果验证了模型的有效性和准确性。

Abstract

Based on back propagation neural network (BPNN)

a wind turbine (WT) state parameter prediction model is established

and the initial weight and threshold of BPNN model are optimized by genetic algorithm （GA）

which effectively eliminates the influence of environmental factors on WT state parameters. The t-location scale (TLS) distribution is used to characterize the distribution characteristics of state parameter prediction errors with different wind speed intervals. Moment estimation is used to calculate the TLS distribution parameters and error anomaly index (EAI) is defined to quantify the anomaly level of prediction errors

which is verified to be an indicator of the WT anomalies. The proposed method is used for 1.5 MW WT with doubly fed induction generators and the results show that the proposed method is effective and accurate.

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Keywords

references

CASELITZ P , GIEBHARDT J . Rotor condition monitoring for improved operational safety of offshore wind energy converters [J]. Journal of Solar Energy Engineering , 2005 , 127 ( 2 ): 253 - 261 .

SOUA S , VAN LIESHOUT P , PERERA A , et al . Determination of the combined vibrational and acoustic emission signature of a wind turbine gearbox and generator shaft in service as a pre-requisite for effective condition monitoring [J]. Renewable Energy , 2013 , 51 : 175 - 181 .

BECKER E , POSTA P . Keeping the blades tunning: condition monitoring of wind turbine gearbox [J]. Refocus , 2006 , 7 ( 2 ): 26 - 32 .

VERBRUGGEN T W . Wind turbine operation and maintenance based on condition monitoring , WT-Ω [R/OL]. [ 2019-01-01 ]. https://www.researchgate.net/publication/312443286_Wind_ turbine_operation_and_maintenance_based_on_condition_ https://www.researchgate.net/publication/312443286_Wind_ turbine_operation_and_maintenance_based_on_condition_

monitoring_WT-O .

CROSS P , MA X D . Nonlinear system identification for model-based condition monitoring of wind turbines [J]. Renewable Energy , 2014 , 71 : 166 - 175 .

YAMPIKULSAKUL N , BYON E , HUANG S , et al . Condition monitoring of wind power system with nonparametric regression analysis [J]. IEEE Transactions on Energy Conversion , 2014 , 29 ( 2 ): 288 - 299 .

赵洪山 , 刘辉海 , 刘宏杨 , 等 . 基于堆叠自编码网络的风电机组发电机状态监测与故障诊断 [J]. 电力系统自动化 , 2018 , 42 ( 11 ): 102 - 108 .

ZHAO Hongshan , LIU Huihai , LIU Hongyang , et al . Condition monitoring and fault diagnosis of wind turbine generator based on stacked autoencoder network [J]. Automation of Electric Power Systems , 2018 , 42 ( 11 ): 102 - 108 .

李刚 , 张建付 , 文福拴 , 等 . 计及信息不确定性的风电机组健康状态实时评估方法 [J]. 电力系统自动化 , 2017 , 41 ( 18 ): 111 - 117 .

LI Gang , ZHANG Jianfu , WEN Fushuan , et al . Evaluation of real-time health condition for wind turbines considering information uncertainty [J]. Automation of Electric Power Systems , 2017 , 41 ( 18 ): 111 - 117 .

赵洪山 , 闫西慧 , 王桂兰 , 等 . 应用深度自编码网络和XGBoost的风电机组发电机故障诊断 [J]. 电力系统自动化 , 2019 , 43 ( 1 ): 81 - 86 .

ZHAO Hongshan , YAN Xihui , WANG Guilan , et al . Fault diagnosis of wind turbine generator based on deep autoencoder network and XGBoost [J]. Automation of Electric Power Systems , 2019 , 43 ( 1 ): 81 - 86 .

缪书唯 , 谢开贵 , 杨贺钧 , 等 . 基于数据驱动方法的风电机组功率优化 [J]. 电力系统自动化 , 2016 , 40 ( 22 ): 7 - 14 .

MIAO Shuwei , XIE Kaigui , YANG Hejun , et al . Power optimization of wind turbine generators based on data-driven approach [J]. Automation of Electric Power Systems , 2016 , 40 ( 22 ): 7 - 14 .

KALOGIROU S A . Artificial neural networks in renewable energy systems applications: a review [J]. Renewable and Sustainable Energy Reviews , 2001 , 5 ( 4 ): 373 - 401 .

SCHLECHTINGEN M , FERREIRA SANTOS I . Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection [J]. Mechanical Systems and Signal Processing , 2011 , 25 ( 5 ): 1849 - 1875 .

颜永龙 , 李剑 , 李辉 , 等 . 采用信息熵和组合模型的风电机组异常检测方法 [J]. 电网技术 , 2015 , 39 ( 3 ): 737 - 743 .

YAN Yonglong , LI Jian , LI Hui , et al . A wind turbine anomaly detection method based on information entropy and combination model [J]. Power System Technology , 2015 , 39 ( 3 ): 737 - 743 .

KUSIAK A , VERMA A . Analyzing bearing faults in wind turbines: a data-mining approach [J]. Renewable Energy , 2012 , 48 : 110 - 116 .

SUN P , LI J , WANG C S , et al . A generalized model for wind turbine anomaly identification based on SCADA data [J]. Applied Energy , 2016 , 168 : 550 - 567 .

张文修 , 梁怡 . 遗传算法的数学基础 [M]. 西安 : 西安交通大学出版社 , 2000 .

ZHANG Wenxiu , LIANG Yi . Mathematical foundation of genetic algorithms [M]. Xi’an : Xi’an Jiaotong University Press , 2000 .

黎静华 , 左俊军 , 汪赛 . 大规模风电并网电力系统运行风险评估与分析 [J]. 电网技术 , 2016 , 40 ( 11 ): 3503 - 3513 .

LI Jinghua , ZUO Junjun , WANG Sai . Analysis and assessment of operation risk for power system with large-scale wind power integration [J]. Power System Technology , 2016 , 40 ( 11 ): 3503 - 3513 .

YAN Y L , LI J , GAO D . Condition parameter modeling for anomaly detection in wind turbines [J]. Energies , 2014 , 7 ( 5 ): 3104 - 3120 .

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