采用GA-BPNN与TLS模型的风电机组异常辨识方法

李泽宇; 郭创新; 朱承治

doi:10.7500/AEPS20190203002

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采用GA-BPNN与TLS模型的风电机组异常辨识方法

学术研究 | 更新时间：2025-06-16

- 采用GA-BPNN与TLS模型的风电机组异常辨识方法
- Anomaly Identification Method of Wind Turbine Based on Genetic Algorithm-Back Propagation Neural Network and t-location Scale Model
- 电力系统自动化-方正平台 2020年44卷第9期页码：95-102
- 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：
  
  李泽宇(1995—)，男，硕士研究生，主要研究方向：电力设备与电力系统可靠性评估。E-mail：lizeyu@zju.edu.cn
  郭创新(1969—)，男，通信作者，教授，博士生导师，主要研究方向：智能调度及风险调度、智能信息处理及其在电力系统应用。E-mail：guochuangxin@zju.edu.cn
  朱承治(1977—)，男，博士，高级工程师，主要研究方向：电力经济信息化与电力系统稳定。E-mail：530981742@qq.com
- 基金信息：
  
  国家重点研发计划资助项目(2017YFB0902600);国家自然科学基金资助项目(51877190);国家电网公司科技项目(52110418000T)
- DOI：10.7500/AEPS20190203002
  中图分类号：
- 收稿日期：2019-02-03，
  
  修回日期：2019-10-06，
  
  网络出版日期：2020-02-21，
  
  纸质出版日期：2020-05-10
- 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.

关键词

Keywords

references

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