基于希尔伯特黄变换和灰色关联度的光伏系统健康状态研究
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摘要
针对传统光伏系统性能评估方法的单一与不足,基于健康管理技术,提出一种新型光伏系统健康状态评估方法。通过希尔伯特黄变换解决非平稳随机信号在特征提取过程中难以获取准确特征值的问题,利用灰色关联理论分析光伏系统当前状态和理论状态的关联程度,从而计算光伏系统的健康指数,用以表征该系统当前的健康程度。这样解决了利用PR判别方法对轻微故障进行评估时,由于评估结果准确度较低而导致误判现象发生的问题。实验结果表明,该健康状态评估方法的评估结果具有较高的有效性和准确性,为提高光伏系统的发电效率,保障光伏电站的安全运行提供了有利的支撑。
In view of the singleness and insufficiency of traditional photovoltaic system performance evaluation methods, a new health assessment method of photovoltaic system is proposed based on health management technology. Hilbert Huang transform is used to solve the problem that it is difficult to obtain accurate eigenvalues of non-stationary random signals in the process of feature extraction.Grey correlation theory is used to analyze the correlation degree between the current state and theoretical state of the photovoltaic system, so as to calculate the health index of the photovoltaic system and characterize the current health degree of the system. This solves the problem of misjudgment due to low judgment accuracy when using the Performance Ratio evaluation method to judge minor faults. The experimental results show that the assessment results of the health status assessment method have high effectiveness and accuracy, which provides a favorable support for improving the power generation efficiency of photovoltaic system and ensuring the safe operation of photovoltaic power stations.
In view of the singleness and insufficiency of traditional photovoltaic system performance evaluation methods, a new health assessment method of photovoltaic system is proposed based on health management technology. Hilbert Huang transform is used to solve the problem that it is difficult to obtain accurate eigenvalues of non-stationary random signals in the process of feature extraction.Grey correlation theory is used to analyze the correlation degree between the current state and theoretical state of the photovoltaic system, so as to calculate the health index of the photovoltaic system and characterize the current health degree of the system. This solves the problem of misjudgment due to low judgment accuracy when using the Performance Ratio evaluation method to judge minor faults. The experimental results show that the assessment results of the health status assessment method have high effectiveness and accuracy, which provides a favorable support for improving the power generation efficiency of photovoltaic system and ensuring the safe operation of photovoltaic power stations.
引文
[1]IEC61724-1998,Photovoltaic System Performance Monitoring-guidelines for Measurement,Data Exchange and Analysis(2006)[S].
[2]GB20513-2006,光伏系统性能检测、测量、数据交换和分析导则(2006版)[S].
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[4]Michael Pecht,Kang Rui.Diagnostics,Prognostics and System's Health Management[M].Hong Kong:City University of Hong Kong,2010.
[5]AVIC Shanghai Aeronautical Measurement-controlling Research Institute.Aviation Fault Diagonsis and Health Management Technology[M].Beijing:China Aviation Publishing Press,2010.
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[13]邓拥军,王伟,钱成春,等.EMD方法及Hilbert变换中边界问题的处理[J].科学通报,2001(3):257-263.
[14]王敏,刘佳,顾煜炯,等.基于IMF希尔伯特解调的风电齿轮箱复合故障识别方法[J].可再生能源,2012,30(11):45-49.
[15]刘思峰,党耀国,方志耕,等.灰色系统理论及其应用[M].北京:科学出版社,2002.
[16]Liang R H.Application of grey relation analysis to hydroelectric generation scheduling[J].International Journal of Electrical Power&Energy Systems,1999,21(5):357-364.
[17]张利,张立勇,王学芝.工业装备系统亚健康诊断方法[M].北京:电子工业出版社,2013.
[2]GB20513-2006,光伏系统性能检测、测量、数据交换和分析导则(2006版)[S].
[3]上海航空测控技术研究所.航空故障诊断与健康管理技术[M].北京:航空工业出版社,2010.
[4]Michael Pecht,Kang Rui.Diagnostics,Prognostics and System's Health Management[M].Hong Kong:City University of Hong Kong,2010.
[5]AVIC Shanghai Aeronautical Measurement-controlling Research Institute.Aviation Fault Diagonsis and Health Management Technology[M].Beijing:China Aviation Publishing Press,2010.
[6]张宪超,武继刚,蒋增荣,等.离散傅里叶变换的算术傅里叶变换算法[J].电子学报,2000,28(5):105-107.
[7]张立影,刘智昱,孟令甲,等.基于小波变换和神经网络的光伏功率预测[J].可再生能源,2015,33(2):171-176.
[8]高阳,钟宏宇,陈鑫宇,等.基于神经网络和小波分析的超短期风速预测[J].可再生能源,2016,34(5):705-711.
[9]Huang N E,Zheng S,Steven R L,et al.The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings of the Royal Society A-Mathematical Physical and Engineering Sciences,1998,454:903-906.
[10]于德介,程军圣,杨宇.Hilbert能量谱及其在齿轮故障诊断中的应用[J].湖南大学学报(自然科学版),2003,30(4):47-50.
[11]邓聚龙.灰理论基础[M].武汉:华中科技大学出版社,2002.
[12]Chang K C,Yeh M F.Grey relational analysis based approach for data clustering[J].IEEE ProceedingsVision Image and Signal Processing,2005,152(2):165-172.
[13]邓拥军,王伟,钱成春,等.EMD方法及Hilbert变换中边界问题的处理[J].科学通报,2001(3):257-263.
[14]王敏,刘佳,顾煜炯,等.基于IMF希尔伯特解调的风电齿轮箱复合故障识别方法[J].可再生能源,2012,30(11):45-49.
[15]刘思峰,党耀国,方志耕,等.灰色系统理论及其应用[M].北京:科学出版社,2002.
[16]Liang R H.Application of grey relation analysis to hydroelectric generation scheduling[J].International Journal of Electrical Power&Energy Systems,1999,21(5):357-364.
[17]张利,张立勇,王学芝.工业装备系统亚健康诊断方法[M].北京:电子工业出版社,2013.