基于EMD和PNN的故障电弧多变量判据诊断方法
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摘要
故障电弧单变量判据诊断法受不确定因素影响大、特征量提取困难,针对此提出一种基于经验模态分解(EMD)和概率神经网络(PNN)的故障电弧多变量判据的诊断方法。利用经验模态分解分析法对电弧电流进行时频分解,并借助信号相关性理论自动提取故障特征信号;同时,通过分析故障特征信号的无量纲指标,形成多变量特征向量集。在此基础上,构建基于概率神经网络的故障电弧诊断模型。通过分析燃弧前后烧水壶、吸尘器、卤素灯、电钻、荧光灯、计算机的电流波形,验证故障诊断模型的准确性。结果表明,所提方法解决了单变量判据故障诊断中出现的特征量提取困难、交叉重复等问题,准确率超过90%。
Single-variable criterion methods of arc fault diagnosis are greatly influenced by uncertain factors and difficult to extract the characteristic quantities,aiming at which,a multi-variable criterion based on EMD(Empirical Mode Decomposition) and PNN(Probabilistic Neural Network) is proposed. Time-frequency decomposition of arc current is carried out by EMD analysis method,and the fault characteristic signal is extracted by signal correlation theory automatically. The set of multi-variable characteristic vectors is formed by analyzing the dimensionless index of fault characteristic signals. On this basis,an arc fault diagnosis model based on PNN is established. The accuracy of the proposed model is verified by analyzing current waveforms of kettles,vacuum cleaners,halogen lamps,drills,fluorescent lamps and computers before and after arcing. Results show that the proposed method solves the problems of difficult feature extraction and cross-repetition in single-variable criterion fault diagnosis,and its accurate rate is over 90%.
Single-variable criterion methods of arc fault diagnosis are greatly influenced by uncertain factors and difficult to extract the characteristic quantities,aiming at which,a multi-variable criterion based on EMD(Empirical Mode Decomposition) and PNN(Probabilistic Neural Network) is proposed. Time-frequency decomposition of arc current is carried out by EMD analysis method,and the fault characteristic signal is extracted by signal correlation theory automatically. The set of multi-variable characteristic vectors is formed by analyzing the dimensionless index of fault characteristic signals. On this basis,an arc fault diagnosis model based on PNN is established. The accuracy of the proposed model is verified by analyzing current waveforms of kettles,vacuum cleaners,halogen lamps,drills,fluorescent lamps and computers before and after arcing. Results show that the proposed method solves the problems of difficult feature extraction and cross-repetition in single-variable criterion fault diagnosis,and its accurate rate is over 90%.
引文
[1] HOFFMANN D J,SWONDER E M,BURR M T. Arc faulting in hou-sehold appliances subjected to a fire test[J]. Fire Technology,2016,52(6):1659-1666.
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[9] 杨凯,张认成,杨建红,等. 基于分形维数和支持向量机的串联电弧故障诊断方法[J]. 电工技术学报,2016,31(2):70-77.YANG Kai,ZHANG Rencheng,YANG Jianhong,et al. Series arc fault diagnostic method based on fractal dimension and support vector machine[J]. Transactions of China Electrotechnical Society,2016,31(2):70-77.
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[11] 刘艳丽,郭凤仪,王智勇,等. 基于信息熵的串联型故障电弧频谱特征研究[J]. 电工技术学报,2015,30(12):488-495.LIU Yanli,GUO Fengyi,WANG Zhiyong,et al. Research on the spec-tral characteristics of series arc fault based on information entropy[J]. Transactions of China Electrotechnical Society,2015,30(12):488-495.
[12] 王尧,韦强强,葛磊蛟,等. 基于电弧电流高频分量的串联交流电弧故障检测方法[J]. 电力自动化设备,2017,37(7):191-197.WANG Yao,WEI Qiangqiang,GE Leijiao,et al. Series AC arc fault detection based on high-frequency components of arc current[J]. Electric Power Automation Equipment,2017,37(7):191-197.
[13] 胥永刚,马伟峰,马朝永,等. 基于遗传编程的无量纲指标在行星齿轮箱故障诊断中的应用[J]. 噪声与振动控制,2017,37(4):175-179.XU Yonggang,MA Weifeng,MA Chaoyong,et al. Application of dimensionless parameters based on genetic programming to fault diagnosis of planetary gearboxes[J]. Noise and Vibration Control,2017,37(4):175-179.
[14] PORWIK P,DOROZ R,ORCZYK T. Signatures verification based on PNN classifier optimised by PSO algorithm[J]. Pattern Recognition,2016,60:998-1014.
[15] 苑津莎,尚海昆. 基于主成分分析和概率神经网络的变压器局部放电模式识别[J]. 电力自动化设备,2013,33(6):27-31.YUAN Jinsha,SHANG Haikun. Pattern recognition based on principal component analysis and probabilistic neural networks for partial discharge of power transformer[J]. Electric Power Automation Equipment,2013,33(6):27-31.
[16] PERERA N,RAJAPAKSE A D. Recognition of fault transients using a probabilistic neural network classifier[J]. IEEE Transac-tions on Power Delivery,2016,26(1):410-419.
[2] MULLER P,TENBOHLEN S,MAIER R,et al. Characteristics of se-ries and parallel low current arc faults in the time and frequency do-main[C]//The 56th IEEE Holm Conference on Electrical Contact. Charleston,SC,USA:IEEE,2010:1-7.
[3] 朱森,孙鹏. 基于周波分析法的串联型故障电弧诊断技术[J]. 电气技术,2016(2):46-48.ZHU Sen,SUN Peng. The series fault arc detection method based on cycle analysis[J]. Electrical Engineering,2016(2):46-48.
[4] 刘晓明,徐叶飞,刘婷,等. 基于电流信号短时过零率的电弧故障检测[J]. 电工技术学报,2015,30(13):125-133.LIU Xiaoming,XU Yefei,LIU Ting,et al. The arc fault detection ba-sed on the current signal short time zero crossing rate[J]. Transactions of China Electrotechnical Society,2015,30(13):125-133.
[5] GRASSETTI R,OTTOBONI R,ROSSI M. A novel algorithm for the parallel arc fault identification in DC aircraft power plants[C]//International Instrumentation and Measurement Technology Conference Proceedings. Graz,Austria:IEEE,2012:852-855.
[6] AHN J,OH Y T. Series arcing fault detection method using harmo-nics analysis of supply current[J]. Journal of the Korean Institute of Illuminating and Electrical Installation Engineers,2014,28(12):30-37.
[7] 郑昕,许志红. 低压串联电弧故障通用诊断方法的研究[J]. 电子测量与仪器学报,2014,28(9):991-997.ZHENG Xin,XU Zhihong. Research on universal diagnostic method for low-voltage series arc fault[J]. Journal of Electronic Measurement and Instrumentation,2014,28(9):991-997.
[8] 张士文,张峰,王子骏,等. 一种基于小波变换能量与神经网络结合的串联型故障电弧辨识方法[J]. 电工技术学报,2014,29(6):290-295,302.ZHANG Shiwen,ZHANG Feng,WANG Zijun,et al. Series arc fault identification method based on energy produced by wavelet transformation and neural network[J]. Transactions of China Electrotechnical Society,2014,29(6):290-295,302.
[9] 杨凯,张认成,杨建红,等. 基于分形维数和支持向量机的串联电弧故障诊断方法[J]. 电工技术学报,2016,31(2):70-77.YANG Kai,ZHANG Rencheng,YANG Jianhong,et al. Series arc fault diagnostic method based on fractal dimension and support vector machine[J]. Transactions of China Electrotechnical Society,2016,31(2):70-77.
[10] 周越,王永兴,邹积岩,等. 基于EMD-AR方法的航空串联故障电弧特性研究[J]. 电器与能效管理技术,2016(11):1-6,18.ZHOU Yue,WANG Yongxing,ZOU Jiyan,et al. Investigation on the characters of aviation series fault arc based on EMD-AR method[J]. Electrical & Energy Management Technology,2016(11):1-6,18.
[11] 刘艳丽,郭凤仪,王智勇,等. 基于信息熵的串联型故障电弧频谱特征研究[J]. 电工技术学报,2015,30(12):488-495.LIU Yanli,GUO Fengyi,WANG Zhiyong,et al. Research on the spec-tral characteristics of series arc fault based on information entropy[J]. Transactions of China Electrotechnical Society,2015,30(12):488-495.
[12] 王尧,韦强强,葛磊蛟,等. 基于电弧电流高频分量的串联交流电弧故障检测方法[J]. 电力自动化设备,2017,37(7):191-197.WANG Yao,WEI Qiangqiang,GE Leijiao,et al. Series AC arc fault detection based on high-frequency components of arc current[J]. Electric Power Automation Equipment,2017,37(7):191-197.
[13] 胥永刚,马伟峰,马朝永,等. 基于遗传编程的无量纲指标在行星齿轮箱故障诊断中的应用[J]. 噪声与振动控制,2017,37(4):175-179.XU Yonggang,MA Weifeng,MA Chaoyong,et al. Application of dimensionless parameters based on genetic programming to fault diagnosis of planetary gearboxes[J]. Noise and Vibration Control,2017,37(4):175-179.
[14] PORWIK P,DOROZ R,ORCZYK T. Signatures verification based on PNN classifier optimised by PSO algorithm[J]. Pattern Recognition,2016,60:998-1014.
[15] 苑津莎,尚海昆. 基于主成分分析和概率神经网络的变压器局部放电模式识别[J]. 电力自动化设备,2013,33(6):27-31.YUAN Jinsha,SHANG Haikun. Pattern recognition based on principal component analysis and probabilistic neural networks for partial discharge of power transformer[J]. Electric Power Automation Equipment,2013,33(6):27-31.
[16] PERERA N,RAJAPAKSE A D. Recognition of fault transients using a probabilistic neural network classifier[J]. IEEE Transac-tions on Power Delivery,2016,26(1):410-419.