SF_6高压断路器机械故障概率的非精确条件估计
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摘要
在小样本条件下,结合SF_6高压断路器自身特点,引入非精确概率概念,提出一种将非精确狄里赫雷模型(IDM)与贝叶斯网络相结合的断路器机械故障概率估计方法,对断路器机械故障可能性范围进行估计。首先,对表征断路器力学性能的特性参数进行状态划分;其次,根据历史数据,建立处理非精确条件概率推断问题的贝叶斯网络,并利用非精确狄里赫雷模型得到断路器机械故障样本缺乏条件下贝叶斯网络节点的非精确条件概率测度;最后运用贝叶斯网络的推理算法,根据断路器本次开断的参数估计得到在下次开断时其出现机械故障的非精确概率。该方法体现了短时间尺度下,断路器机械故障概率随其运行状态变化而时变的特点,并为小样本情况下断路器机械机构的可靠性评估以及状态检修提供了依据。通过算例验证,证明了所提方法的有效性。
Under the condition of limited outage samples and combined with the characteristics of the SF_6 high voltage circuit breaker itself, this paper introduces the concept of imprecise probability and proposes a probability estimation method for the mechanical outage of the circuit breaker, which integrates imprecise Dirichlet model(IDM) and Bayesian network, to estimate the range of mechanical outage probability for the circuit breaker. Firstly, this paper divides the states for the characteristic parameters of mechanical properties characterizing the circuit breaker. Secondly, according to historical data, Bayesian network for processing the probability extrapolation in imprecise condition is established, and IDM is utilized to obtain the imprecise probabilistic dependences of Bayesian network nodes under the lacking condition of mechanical outage samples. Finally, by utilizing the reasoning algorithm of Bayesian network, the imprecise probability of mechanical outage for the next break is obtained according to the parameters of last break of the circuit breaker. This method not only reflects the time-varying feature of mechanical outage probability of the circuit breaker, but also provides reference for the reliability estimation and condition-based maintenance of the circuit breaker in limited outage samples. Through calculating the samples, the effectiveness of this method is verified.
Under the condition of limited outage samples and combined with the characteristics of the SF_6 high voltage circuit breaker itself, this paper introduces the concept of imprecise probability and proposes a probability estimation method for the mechanical outage of the circuit breaker, which integrates imprecise Dirichlet model(IDM) and Bayesian network, to estimate the range of mechanical outage probability for the circuit breaker. Firstly, this paper divides the states for the characteristic parameters of mechanical properties characterizing the circuit breaker. Secondly, according to historical data, Bayesian network for processing the probability extrapolation in imprecise condition is established, and IDM is utilized to obtain the imprecise probabilistic dependences of Bayesian network nodes under the lacking condition of mechanical outage samples. Finally, by utilizing the reasoning algorithm of Bayesian network, the imprecise probability of mechanical outage for the next break is obtained according to the parameters of last break of the circuit breaker. This method not only reflects the time-varying feature of mechanical outage probability of the circuit breaker, but also provides reference for the reliability estimation and condition-based maintenance of the circuit breaker in limited outage samples. Through calculating the samples, the effectiveness of this method is verified.
引文
[1]程序,关永刚,张文鹏,等.基于因子分析和支持向量机算法的高压断路器机械故障诊断方法[J].电工技术学报,2014,29(7):209-215.Cheng Xu,Guan Yonggang,Zhang Wenpeng,et al.Diagnosis method on the mechanical failure of high voltage circuit breakers based on factor analysis and SVM[J].Transactions of China Electrotechnical Society,2014,29(7):209-215.
[2]韩富春,张海龙.高压断路器运行状态的多级模糊综合评估[J].电力系统保护与控制,2009,37(17):60-64.Han Fuchun,Zhang Hailong.The multi-level fuzzy evaluation of high-voltage circuit breakers’running[J].Power System Protection and Control,2009,37(17):60-64.
[3]王涛,张尚,顾雪平,等.电力系统运行状态的趋势辨识[J].电工技术学报,2015,30(24):171-180.Wang Tao,Zhang Shang,Gu Xueping,et al.Trends identification of power system operating states[J].Transactions of China Electrotechnical Society,2015,30(24):171-180.
[4]张佩,赵书涛,申路,等.基于改进EEMD的SF6高压断路器振声联合故障诊断方法[J].电力系统保护与控制,2014,29(8):77-81.Zhang Pei,Zhao Shutao,Shen Lu,et al.An assessment method of SF6 high-voltage circuit breaker based on association rules and variable weight coefficients[J].Power System Protection and Control,2014,29(8):77-81.
[5]刘海涛,程林,孙元章,等.采用瞬时概率的运行可靠性短期评估[J].中国电机工程学报,2008,28(13):20-25.Liu Haitao,Cheng Lin,Sun Yuanzhang,et al.Short-term operational reliability evaluation by using time dependent state probability[J].Proceedings of the CSEE,2008,28(13):20-25.
[6]宁辽逸,吴文传,张伯明.电力系统运行风险评估中元件时变故障模型分析[J].电力系统自动化,2009,33(16):7-12.Ning Liaoyi,Wu Wenchuan,Zhang Boming.Analysis of a time-varying power component outage model for operation risk assessment[J].Automation of Electrical Power System,2009,33(16):7-12.
[7]何剑,程林,孙元章,等.条件相依的输变电设备短期可靠性模型[J].中国电机工程学报,2009,29(7):39-46.He Jian,Cheng Lin,Sun Yuanzhang,et al.Condition dependent short-term reliability models of transmission equipment[J].Proceedings of the CSEE,2009,29(7):39-46.
[8]郝爽,仲林林,王小华,等.基于支持向量机的高压断路器机械状态预测算法研究[J].高压电器,2015,51(7):155-159.Hao Shuang,Zhong Linlin,Wang Xiaohua,et al.Mechanical life prognosis of high voltage circuit breaker based on support vector machine[J].High Voltage Apparatus,2015,51(7):155-159.
[9]李文沅.电力系统风险评估模型、方法和应用[M].北京:科学出版社,2006.
[10]Zhou Y,Pahwa A,Yang S S.Modling weatherrelated failures of overhead distribution lines[J].IEEE Transactions on Power Systems,2006,21(4):1683-1690.
[11]齐先军,史新红,丁明,等.电力系统元件稳态可用度的非精确统计推断[J].中国电机工程学报,2014,34(13):2108-2115.Qi Xianjun,Shi Xinhong,Ding Ming,et al.Imprecise statistical inference for steady-state available of components in power systems[J].Proceedings of the CSEE,2014,34(13):2108-2115.
[12]孟光,尤明懿.基于状态监测的设备寿命预测与预防维护规划研究进展[J].振动与冲击,2011,30(8):1-11.Meng Guang,You Mingyi.Review on conditionbased equipment residual life prediction and preventive maintenance scheduling[J].Journal of Vibration and Shock,2011,30(8):1-11.
[13]Li Y,Billington S,Zhang C,et al.Adaptive prognostics for rolling element bearing condition[J].Mechanical Systems&Signal Processing,1999,13(1):103-113.
[14]Qi Bensheng.Research on fault diagnosis of high-voltage circuit breaker based on the improved BP neural network[C]//International Conference on Information Science and Engineering,Hangzhou,China,2011:1460-1463.
[15]徐国政.高压断路器原理和应用[M].北京:清华大学出版社,2000.
[16]宋杲,崔景春,袁大陆.1999~2003年高压断路器运行分析[J].电力设备,2005,6(2):6-13.Song Gao,Cui Jingchun,Yuan Dalu.Operating analysis of high voltage circuit breaker in 1999-2003[J].Electrical Equipment,2005,6(2):6-13.
[17]熊小伏,孙鑫,蔡伟贤,等.基于DSP及ARM的分布式高压断路器机械特性监测系统[J].电力系统保护与控制,2009,37(6):64-68.Xiong Xiaofu,Sun Xin,Cai Weixian,et al.Distributed monitoring system based on DSP and ARM for mechanical characteristic of high voltage breaker[J].Power System Protection and Control,2009,37(6):64-68.
[18]李建生,梁军,贠志皓,等.基于电气信息评估设备状态渐进变化的概率分析方法[J].电工技术学报,2013,28(10):355-363.Li Jiansheng,Liang Jun,Yun Zhihao,et al.Probability analysis of progressive changes of equipment state based on electrical information[J].Transactions of China Electrotechnical Society,2013,28(10):355-363.
[19]魏延芹.基于物元与证据理论相结合的高压断路器状态评估方法研究[D].重庆:重庆大学,2008.
[20]李伟.基于模糊综合评判的高压断路器状态评估方法研究[D].重庆:重庆大学,2004.
[21]Lovric M.International encyclopedia of statistical science[M].Berlin Heidelberg:Springer,2011.
[22]Walley P.Inferences from multinomial data:learning about a bag of marbles[J].Journal of the Royal Statistical Society,1996,58(1):3-57.
[23]Yang Ming,Wang Jianhui,Diao Haoran,et al.Interval estimation for conditional failure rates of transmission lines with limited samples[J].IEEETransactions on Smart Grid,2016,DOI:10.1109/TSG,2016.2618623.
[24]Utkin L V.The imprecise Dirichlet model as a basis for a new boosting classification algorithm[J].Neurocomputing,2015,151:1374-1383.
[25]刁浩然,杨明,韩学山,等.电力设备故障概率的非精确条件估计[J].中国电机工程学报,2016,36(19):5134-5144.Diao Haoran,Yang ming,Han Xueshan,et al.Imprecise estimation for conditional outage probability of power components[J].Proceedings of the CSEE,2016,36(19):5134-5144.
[26]Coolen F P A.An imprecise Dirichlet model for Bayesian analysis of failure data including rightcensored observations[J].Reliability Engineering&System Safety,1997,56(1):61-68.
[27]Bernard J M.An introduction to the imprecise Dirichlet model for multinomial data[J].International Journal of Approximate Reasoning,2005,39(2-3):123-150.
[28]王永强,律方成,李和明.基于贝叶斯网络和油中溶解气体分析的变压器故障诊断方法[J].电工技术学报,2004,19(12):74-77.Wang Yongqiang,LüFangcheng,Li Heming.Fault diagnosis for power transformer based on BN and DGA[J].Transactions of China Electrotechnical Society,2004,19(12):74-77.
[29]颜湘莲,王承玉,季严松,等.气体绝缘设备中SF6气体分解产物与设备故障关系的建模[J].电工技术学报,2015,30(22):231-238.Yan Xianglian,Wang Chengyu,Ji Yansong,et al.Modeling of the relation between SF6 decomposition products and interior faults in gas insulated equipment[J].Transactions of China Electrotechnical Society,2015,30(22):231-238.
[30]李俭川,胡茑庆,秦国军,等.贝叶斯网络理论及其在设备故障诊断中的应用[J].中国机械工程,2003,14(10):896-900.Li Jianchuan,Hu Niaoqing,Qin Guojun,et al.Bayesian network and its application for device fault diagnosis[J].China Mechanical Engineering,2003,14(10):896-900.
[2]韩富春,张海龙.高压断路器运行状态的多级模糊综合评估[J].电力系统保护与控制,2009,37(17):60-64.Han Fuchun,Zhang Hailong.The multi-level fuzzy evaluation of high-voltage circuit breakers’running[J].Power System Protection and Control,2009,37(17):60-64.
[3]王涛,张尚,顾雪平,等.电力系统运行状态的趋势辨识[J].电工技术学报,2015,30(24):171-180.Wang Tao,Zhang Shang,Gu Xueping,et al.Trends identification of power system operating states[J].Transactions of China Electrotechnical Society,2015,30(24):171-180.
[4]张佩,赵书涛,申路,等.基于改进EEMD的SF6高压断路器振声联合故障诊断方法[J].电力系统保护与控制,2014,29(8):77-81.Zhang Pei,Zhao Shutao,Shen Lu,et al.An assessment method of SF6 high-voltage circuit breaker based on association rules and variable weight coefficients[J].Power System Protection and Control,2014,29(8):77-81.
[5]刘海涛,程林,孙元章,等.采用瞬时概率的运行可靠性短期评估[J].中国电机工程学报,2008,28(13):20-25.Liu Haitao,Cheng Lin,Sun Yuanzhang,et al.Short-term operational reliability evaluation by using time dependent state probability[J].Proceedings of the CSEE,2008,28(13):20-25.
[6]宁辽逸,吴文传,张伯明.电力系统运行风险评估中元件时变故障模型分析[J].电力系统自动化,2009,33(16):7-12.Ning Liaoyi,Wu Wenchuan,Zhang Boming.Analysis of a time-varying power component outage model for operation risk assessment[J].Automation of Electrical Power System,2009,33(16):7-12.
[7]何剑,程林,孙元章,等.条件相依的输变电设备短期可靠性模型[J].中国电机工程学报,2009,29(7):39-46.He Jian,Cheng Lin,Sun Yuanzhang,et al.Condition dependent short-term reliability models of transmission equipment[J].Proceedings of the CSEE,2009,29(7):39-46.
[8]郝爽,仲林林,王小华,等.基于支持向量机的高压断路器机械状态预测算法研究[J].高压电器,2015,51(7):155-159.Hao Shuang,Zhong Linlin,Wang Xiaohua,et al.Mechanical life prognosis of high voltage circuit breaker based on support vector machine[J].High Voltage Apparatus,2015,51(7):155-159.
[9]李文沅.电力系统风险评估模型、方法和应用[M].北京:科学出版社,2006.
[10]Zhou Y,Pahwa A,Yang S S.Modling weatherrelated failures of overhead distribution lines[J].IEEE Transactions on Power Systems,2006,21(4):1683-1690.
[11]齐先军,史新红,丁明,等.电力系统元件稳态可用度的非精确统计推断[J].中国电机工程学报,2014,34(13):2108-2115.Qi Xianjun,Shi Xinhong,Ding Ming,et al.Imprecise statistical inference for steady-state available of components in power systems[J].Proceedings of the CSEE,2014,34(13):2108-2115.
[12]孟光,尤明懿.基于状态监测的设备寿命预测与预防维护规划研究进展[J].振动与冲击,2011,30(8):1-11.Meng Guang,You Mingyi.Review on conditionbased equipment residual life prediction and preventive maintenance scheduling[J].Journal of Vibration and Shock,2011,30(8):1-11.
[13]Li Y,Billington S,Zhang C,et al.Adaptive prognostics for rolling element bearing condition[J].Mechanical Systems&Signal Processing,1999,13(1):103-113.
[14]Qi Bensheng.Research on fault diagnosis of high-voltage circuit breaker based on the improved BP neural network[C]//International Conference on Information Science and Engineering,Hangzhou,China,2011:1460-1463.
[15]徐国政.高压断路器原理和应用[M].北京:清华大学出版社,2000.
[16]宋杲,崔景春,袁大陆.1999~2003年高压断路器运行分析[J].电力设备,2005,6(2):6-13.Song Gao,Cui Jingchun,Yuan Dalu.Operating analysis of high voltage circuit breaker in 1999-2003[J].Electrical Equipment,2005,6(2):6-13.
[17]熊小伏,孙鑫,蔡伟贤,等.基于DSP及ARM的分布式高压断路器机械特性监测系统[J].电力系统保护与控制,2009,37(6):64-68.Xiong Xiaofu,Sun Xin,Cai Weixian,et al.Distributed monitoring system based on DSP and ARM for mechanical characteristic of high voltage breaker[J].Power System Protection and Control,2009,37(6):64-68.
[18]李建生,梁军,贠志皓,等.基于电气信息评估设备状态渐进变化的概率分析方法[J].电工技术学报,2013,28(10):355-363.Li Jiansheng,Liang Jun,Yun Zhihao,et al.Probability analysis of progressive changes of equipment state based on electrical information[J].Transactions of China Electrotechnical Society,2013,28(10):355-363.
[19]魏延芹.基于物元与证据理论相结合的高压断路器状态评估方法研究[D].重庆:重庆大学,2008.
[20]李伟.基于模糊综合评判的高压断路器状态评估方法研究[D].重庆:重庆大学,2004.
[21]Lovric M.International encyclopedia of statistical science[M].Berlin Heidelberg:Springer,2011.
[22]Walley P.Inferences from multinomial data:learning about a bag of marbles[J].Journal of the Royal Statistical Society,1996,58(1):3-57.
[23]Yang Ming,Wang Jianhui,Diao Haoran,et al.Interval estimation for conditional failure rates of transmission lines with limited samples[J].IEEETransactions on Smart Grid,2016,DOI:10.1109/TSG,2016.2618623.
[24]Utkin L V.The imprecise Dirichlet model as a basis for a new boosting classification algorithm[J].Neurocomputing,2015,151:1374-1383.
[25]刁浩然,杨明,韩学山,等.电力设备故障概率的非精确条件估计[J].中国电机工程学报,2016,36(19):5134-5144.Diao Haoran,Yang ming,Han Xueshan,et al.Imprecise estimation for conditional outage probability of power components[J].Proceedings of the CSEE,2016,36(19):5134-5144.
[26]Coolen F P A.An imprecise Dirichlet model for Bayesian analysis of failure data including rightcensored observations[J].Reliability Engineering&System Safety,1997,56(1):61-68.
[27]Bernard J M.An introduction to the imprecise Dirichlet model for multinomial data[J].International Journal of Approximate Reasoning,2005,39(2-3):123-150.
[28]王永强,律方成,李和明.基于贝叶斯网络和油中溶解气体分析的变压器故障诊断方法[J].电工技术学报,2004,19(12):74-77.Wang Yongqiang,LüFangcheng,Li Heming.Fault diagnosis for power transformer based on BN and DGA[J].Transactions of China Electrotechnical Society,2004,19(12):74-77.
[29]颜湘莲,王承玉,季严松,等.气体绝缘设备中SF6气体分解产物与设备故障关系的建模[J].电工技术学报,2015,30(22):231-238.Yan Xianglian,Wang Chengyu,Ji Yansong,et al.Modeling of the relation between SF6 decomposition products and interior faults in gas insulated equipment[J].Transactions of China Electrotechnical Society,2015,30(22):231-238.
[30]李俭川,胡茑庆,秦国军,等.贝叶斯网络理论及其在设备故障诊断中的应用[J].中国机械工程,2003,14(10):896-900.Li Jianchuan,Hu Niaoqing,Qin Guojun,et al.Bayesian network and its application for device fault diagnosis[J].China Mechanical Engineering,2003,14(10):896-900.
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