基于MBBS的电能计量设备故障率预估
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摘要
电能计量设备故障率分析机制是电能准确计量的前提,而通常计量设备故障率数据样本量少且数据缺失严重,难以对故障率数据进行准确评估分析。为此,提出一种基于多层贝叶斯B样条(MBBS)的电能计量设备故障率可靠性评估与分析模型:首先采用Z分数法检测原始故障率数据中的异常值;然后采用基于Weibull分布的MBBS模型对电能计量设备故障率数据进行拟合评估,其中对B样条基函数的样条系数进行改进,采用一阶自回归作为先验分布,以平滑拟合曲线;利用马尔科夫蒙特卡洛方法求解模型,获得后验参数及置信区间估计。通过工程实例进行验证,结果表明采用本方法能评估电能计量设备故障率随时间的变化趋势,兼顾数据整体变化的渐变规律,并准确分析出可靠度。
The fault rate analysis mechanism of power metering equipment is the premise of accurate measurement of electric energy. However, it is usually difficult to accurately evaluate and analyze the failure rate data due to the small sample size and serious data missing. Therefore, a reliability evaluation and analysis model of power metering equipment failure rate is proposed based on multilayered Bayesian B-spline(MBBS). Firstly, the Z-score method is used to detect the abnormal data in the raw failure rate data. Then, an multilayered Bayesian cubic B-spline model based on Weibull distribution is applied to evaluate the failure rate data of the electricity meter. To smooth the fitting curve, the first-order autoregressive distribution is adopted to the spline coefficients of B-spline basis function. The posterior parameters and confidence interval estimation are obtained by Markov Monte Carlo method. Finally, the verification on the engineering case shows that the proposed method can evaluate the variation trend over time of the power metering equipment failure rate, reflect the he gradual change law of the data, and accurately analyze the reliability.
The fault rate analysis mechanism of power metering equipment is the premise of accurate measurement of electric energy. However, it is usually difficult to accurately evaluate and analyze the failure rate data due to the small sample size and serious data missing. Therefore, a reliability evaluation and analysis model of power metering equipment failure rate is proposed based on multilayered Bayesian B-spline(MBBS). Firstly, the Z-score method is used to detect the abnormal data in the raw failure rate data. Then, an multilayered Bayesian cubic B-spline model based on Weibull distribution is applied to evaluate the failure rate data of the electricity meter. To smooth the fitting curve, the first-order autoregressive distribution is adopted to the spline coefficients of B-spline basis function. The posterior parameters and confidence interval estimation are obtained by Markov Monte Carlo method. Finally, the verification on the engineering case shows that the proposed method can evaluate the variation trend over time of the power metering equipment failure rate, reflect the he gradual change law of the data, and accurately analyze the reliability.
引文
[1] 陈垒,赵伟,肖勇,等. 非整周期采样条件下有功电能计量的新算法[J]. 仪器仪表学报, 2017, 38(4):861- 869.CHEN L, ZHAO W, XIAO Y, et al. New active electric energy measurement algorithm under non-integer-period sampling[J]. Chinese Journal of Scientific Instrument, 2017, 38(4):861- 869.
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[13] YUAN T, KUO Y. Bayesian analysis of hazard rate, change point, and cost-optimal burn-in time for electronic devices[J]. IEEE Transactions on Reliability, 2010,59(1):132-138.
[14] 朱磊, 左洪福, 蔡景. 基于Wiener过程的民用航空发动机性能可靠性预测[J]. 航空动力学报, 2013,28(5):1006-1012.ZHU L, ZUO H F, CAI J. Performance reliability prediction for civil aviation aircraft engine based on Wiener proces[J]. Journal of Aerospace Power, 2013, 28(5):1006-1012.
[15] LIU Y, SHUAI Q, ZHOU S, et al. Prognosis of structural damage growth via integration of physical model prediction and bayesian estimation[J]. IEEE Transactions on Reliability, 2017, 66(3):1-12.
[16] ALEXANDER M, ALKEMA L. Global estimation of neonatal mortality using a Bayesian hierarchical splines regression model[J]. Demographic Research, 2018, 38(15): 336-366.
[17] 李志强, 徐廷学, 安进, 等. 冗余系统共因失效动态贝叶斯网络建模[J]. 仪器仪表学报, 2018, 39(3): 190-198.LI ZH Q, XU Y X, AN J, et al. Common cause failure modeling for redundant system based on dynamic Bayesian network[J]. Chinese Journal of Scientific Instrument, 2018, 39(3): 190-198.
[18] ALEXANDER M. A flexible bayesian model for estimating subnational mortality[J]. Demography, 2017,54(2):1-17.
[19] 刘浩然, 孙美婷, 李雷, 等. 基于蚁群节点寻优的贝叶斯网络结构算法研究[J]. 仪器仪表学报, 2017, 38(1): 143-150.LIU H R, SUN M T, LI L, et al. Study on Bayesian network structure learning algorithm based on ant colony node order optimization[J]. Chinese Journal of Scientific Instrument, 2017, 38(1): 143-150.
[20] RAM M. On system reliability approaches: a brief survey[J].International Journal of System Assurance Engineering and Management, 2013,4(2):101-117.
[21] 全国电磁计量技术委员会. JJG 596- 2012 中华人民共和国国家计量检定规程[S]. 电子式交流电能计量设备, 2012.National technical committee for electromagnetic measurement. JJG 596- 2012 national metrological verification regulations of the People′s Republic of China [S]. Electrical Meters for Measuring Alternating-current Electrical Energy, 2012.
[2] 向世强, 唐求, 宋鹏,等. 非线性负荷电能计量方法研究[J]. 电子测量与仪器学报. 2018, 32(4):181-186.XIANG SH Q, TANG Q, SONG P, et al. Research on nonlinear load energy measurement method [J]. Journal of Electronic Measurement and Instrumentation,2018, 32(4):181-186.
[3] 孟明, 舒展. 基于缺陷扣分法和三角模糊数层次分析法的智能电能计量设备全生命周期质量评价[J]. 电力系统保护与控制, 2012,40(22):88-93.MENG M, SHU ZH. Full quality evaluation on smart meters based on the defect deduction method and the AHP of triangular fuzzy number[J]. Power System Protection and Control, 2012,40(22):88-93.
[4] JIN L, PENG C, JIANG T. System-level electric field exposure assessment by the fault tree analysis[J]. IEEE Transactions on Electromagnetic Compatibility, 2017,59(4):1095-1102.
[5] HIRAOKA Y, MURAKAMI T, YAMAMOYO K, et al. Method of computer-aided fault tree analysis for high-reliable and safety design[J]. IEEE Transactions on Reliability, 2016,65(2):687-703,.
[6] 李练兵, 张秀云, 王志华,等. 故障树和BAM神经网络在光伏并网故障诊断中的应用[J]. 电工技术学报,2015,30(2):248- 254.LI L B, ZHANG X H, WANG ZH H, et al. Fault diagnosis in solar photovoltaic grid-connected power system based on fault tree and BAM neural network[J]. Transactions of China Electrotechnical Society, 2015,30(2):248- 254.
[7] PINDORIYA NM, JIRUTITIJAROEN P, SRINIVASAN D, et al. Composite reliability evaluation using monte carlo simulation and least squares support vector classifier[J]. IEEE Transactions on Power Systems,2011,26(4):2483- 2490.
[8] 马小骏, 任淑红, 左洪福,等. 基于LS-SVM算法和性能可靠性的航空发动机在翼寿命预测方法[J]. 交通运输工程学报,2015, 15(3):92-100.MA X J, REN SH H, ZUO H F, et al. Prediciton method of aero-engine life on wing based on LS-SVM algorithm and performance reliability[J].Journal of Traffic and Transportation Engineering, 2015, 15(3):92-100.
[9] WANG X, SHEN J. Reliability analysis of multi-stress accelerated life test based on BP neural network[C]. Fifth International Conference on Business Intelligence and Financial Engineering,2012:6-9.
[10] 戴志辉, 李芷筠, 焦彦军,等. 基于BP神经网络的小样本失效数据下继电保护可靠性评估[J]. 电力自动化设备, 2014,34(11):129-134.DAI ZH H,LI ZH J,JIAO Y J,et al. Reliability assessment based on BP neural network for relay protection system with a few failure data samples[J]. Electric Power Automation Equipment, 2014,34(11):129-134.
[11] 张广德, 于连城, 张译, 等. 基于数据挖掘的电网数据分析方法[J]. 国外电子测量技术, 2018, 37(7): 24- 28.ZHANG G D, YU L CH, ZHANG Y, et al. Data analysis method based on data mining[J]. Foreign Electronic Measurement Technology, 2018, 37(7): 24- 28.
[12] 江京, 许敏鹏, 印二威,等. 整合贝叶斯动态停止策略对 SSVEP-BCIs 的性能提升研究[J]. 仪器仪表学报, 2018, 39(5):65-72.JIANG J, XU M P, YIN ER W, et al. Study on enhancing performance of SSVEP-based BCIs by incorporating Bayes-based dynamic stopping strategy[J]. Chinese Journal of Scientific Instrument, 2018, 39(5):65-72.
[13] YUAN T, KUO Y. Bayesian analysis of hazard rate, change point, and cost-optimal burn-in time for electronic devices[J]. IEEE Transactions on Reliability, 2010,59(1):132-138.
[14] 朱磊, 左洪福, 蔡景. 基于Wiener过程的民用航空发动机性能可靠性预测[J]. 航空动力学报, 2013,28(5):1006-1012.ZHU L, ZUO H F, CAI J. Performance reliability prediction for civil aviation aircraft engine based on Wiener proces[J]. Journal of Aerospace Power, 2013, 28(5):1006-1012.
[15] LIU Y, SHUAI Q, ZHOU S, et al. Prognosis of structural damage growth via integration of physical model prediction and bayesian estimation[J]. IEEE Transactions on Reliability, 2017, 66(3):1-12.
[16] ALEXANDER M, ALKEMA L. Global estimation of neonatal mortality using a Bayesian hierarchical splines regression model[J]. Demographic Research, 2018, 38(15): 336-366.
[17] 李志强, 徐廷学, 安进, 等. 冗余系统共因失效动态贝叶斯网络建模[J]. 仪器仪表学报, 2018, 39(3): 190-198.LI ZH Q, XU Y X, AN J, et al. Common cause failure modeling for redundant system based on dynamic Bayesian network[J]. Chinese Journal of Scientific Instrument, 2018, 39(3): 190-198.
[18] ALEXANDER M. A flexible bayesian model for estimating subnational mortality[J]. Demography, 2017,54(2):1-17.
[19] 刘浩然, 孙美婷, 李雷, 等. 基于蚁群节点寻优的贝叶斯网络结构算法研究[J]. 仪器仪表学报, 2017, 38(1): 143-150.LIU H R, SUN M T, LI L, et al. Study on Bayesian network structure learning algorithm based on ant colony node order optimization[J]. Chinese Journal of Scientific Instrument, 2017, 38(1): 143-150.
[20] RAM M. On system reliability approaches: a brief survey[J].International Journal of System Assurance Engineering and Management, 2013,4(2):101-117.
[21] 全国电磁计量技术委员会. JJG 596- 2012 中华人民共和国国家计量检定规程[S]. 电子式交流电能计量设备, 2012.National technical committee for electromagnetic measurement. JJG 596- 2012 national metrological verification regulations of the People′s Republic of China [S]. Electrical Meters for Measuring Alternating-current Electrical Energy, 2012.