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1.上海电力大学电气工程学院,上海市 200090
2.上海绿色环保能源有限公司,上海市 200433
3.国网上海市电力公司电缆分公司,上海市 200072
Received:08 September 2021,
Revised:2021-12-10,
Published:25 October 2022
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魏书荣,闫梦飞,任子旭等.考虑运行环境影响的海上双馈风电机组状态判别[J].电力系统自动化,2022,46(20):181-189.
WEI Shurong,YAN Mengfei,REN Zixu,et al.State Discrimination for Offshore DFIG-based Wind Turbine Considering Influence of Operation Environment[J].Automation of Electric Power Systems,2022,46(20):181-189.
魏书荣,闫梦飞,任子旭等.考虑运行环境影响的海上双馈风电机组状态判别[J].电力系统自动化,2022,46(20):181-189. DOI: 10.7500/AEPS20210908004.
WEI Shurong,YAN Mengfei,REN Zixu,et al.State Discrimination for Offshore DFIG-based Wind Turbine Considering Influence of Operation Environment[J].Automation of Electric Power Systems,2022,46(20):181-189. DOI: 10.7500/AEPS20210908004.
运行环境对海上风电机组状态的影响不可忽视,而运行环境因素与风电机组状态变量间耦合关系复杂,文中提出一种基于贝叶斯决策理论和Copula函数的海上风电机组状态判别方法。首先,挖掘运行环境与风电机组状态变量间的相关性特征,分别在风电机组正常和故障两种情况下,利用多元Copula函数构建对应变量的联合密度函数;然后,依托贝叶斯决策理论进行风电机组状态判别;最后,以中国某海上风电场实际运行工况为例进行分析。结果表明:运行环境对风电机组状态判别影响较大,考虑运行环境影响可提高风电机组判别结果的准确率,为利用海上环境因素进行风电机组早期状态预测提供新的参考依据。
The influence of the operation environment on the state of offshore wind turbines cannot be ignored
and the coupling relationship between operation environment factors and state variables of wind turbines is complex. A state discrimination method for offshore wind turbines based on the Bayesian decision theory and the Copula function is proposed in this paper. First
the correlation characteristics between the operation environment and state variables of wind turbines are excavated
and the joint density function of the corresponding variables is constructed by using the multivariate Copula function under the normal and fault conditions of the wind turbine
respectively. Then
the state discrimination for wind turbines is carried out based on the Bayesian decision theory. Finally
the actual operation conditions of an offshore wind farm in China are analyzed as an example. The results show that the operation environment has a great influence on the state discrimination for wind turbines. Considering the influence of the operation environment can improve the accuracy of the wind turbine discrimination results
and provide a new reference basis for the early state prediction of the wind turbine by using offshore environmental factors.
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