交流电力测功机运行状态的混沌预测与异常诊断
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摘要
为提高交流电力测功机运行过程的安全性与可靠性,利用混沌理论对交流电力测功机运行状态的特征信息进行混沌辨析,主要内容包括对交流电力测功机运行过程中的齿轮最大温度、绕组最大温度、转速、有功功率进行混沌预测,对交流电力测功机运行过程中的油膜涡动、流体激励、径向碰摩状态进行混沌诊断。研究结果表明:运行状态参数齿轮最大温度、绕组最大温度和转子平均转速混沌预测的平均相对误差均为8.75%;通过混沌关联维数可以较好地区分交流电力测功机的油膜涡动异常、流体激励异常和碰摩异常这3种异常运行状态,为交流电力测功机的运行状态检测提供一种新思路。
In order to improve the safety and reliability of the running state of alternating current(AC) dynamometer,chaos theory was applied to analyze the abstracted characteristics signals from the running state of AC dynamometer. The study includes the chao prediction of the maximum temperature of gear and winding, rotate speed and active power, the chao diagnosis of oil whirling, flow excitation and radial collision friction of alternating current dynamometer. The results show that the average relative errors of chao prediction of running state parameters of gear and winding and rotate speed are all 8.75% and the abnormal state of alternating current dynamometer can be differentiated by chaotic correlation,which provides a new method for the detection of running state, including oil whirl, fluid vibration and impact-rub of alternating current dynamometer.
In order to improve the safety and reliability of the running state of alternating current(AC) dynamometer,chaos theory was applied to analyze the abstracted characteristics signals from the running state of AC dynamometer. The study includes the chao prediction of the maximum temperature of gear and winding, rotate speed and active power, the chao diagnosis of oil whirling, flow excitation and radial collision friction of alternating current dynamometer. The results show that the average relative errors of chao prediction of running state parameters of gear and winding and rotate speed are all 8.75% and the abnormal state of alternating current dynamometer can be differentiated by chaotic correlation,which provides a new method for the detection of running state, including oil whirl, fluid vibration and impact-rub of alternating current dynamometer.
引文
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[3]WARREN K,HAWKINS R C,SPROTT J C.Substance abuse as a dynamical disease:evidence and clinical implications of nonlinearity in a time series of daily alcohol consumption[J].Addictive Behaviors,2003(28):369-374.
[4]E Jiaqaing,WANG Yaonan,MEI Chi,et al.Chaos behavior of crude copper composition time series from the copper convertor and its predicable time scale[J].Nonlinear Analysis:Real World Applications,2006,7(4):651-661.
[5]BATCHELOR R,ALIZADEH A,VISVIKIS I.Forecasting spot and forward prices in the international freight market[J].International Journal of Forecasting,2007,23(1):101-114.
[6]MAO M,CHIRWA E C.Application of grey model GM(1,1)to vehicle fatality risk estimation[J].Technological Forecasting and Social Change,2006,75(5):588-605.
[7]XIAO Gang,NI Mingjiang,CHI Yong,et al.Gasification characteristics of MSW and an ANN prediction model[J].Waste Management,2009,29(1):240-244.
[8]HAQUE M E,SUDHAKAR K V.ANN back-propagation prediction model for fracture toughness in microalloy steel[J].International Journal of Fatigue,2002,24(9):1003-1010.
[9]AN Xueli,JIANG Dongxiang,ZHAO Minghao,et al.Short-term prediction of wind power using EMD and chaotic theory[J].Communications in Nonlinear Science and Numerical Simulation,2012,17(2):1036-1042.
[10]E Jiaqiang,QIAN Cheng,LIU Teng,et al.Chaos analysis on the acceleration control signals of the piezoelectric actuators in the stewart platform[J].Shock and Vibration,2016,2016:1-9.
[11]E Jiaqiang,ZUO Qingsong,LIU Haili,et al.Endpoint forecasting on composite regeneration by coupling cerium-based additive and microwave for diesel particulate filter[J].Journal of Central South University,2016,23(8):2118-2128.
[12]E Jiaqiang,QIAN Cheng,LIU Haili,et al.Design of the H∞robust control for the piezoelectric actuator based on chaos optimization algorithm[J].Aerospace Science and Technology,2015,47:238-246.
[13]HAGHIGHI H S,MARKAZI A H D.Chaos prediction and control in MEMS resonators[J].Communications in Nonlinear Science and Numerical Simulation,2010,15(10):3091-3099.
[14]DLASK M,KUKAL J.Application of rotational spectrum for correlation dimension estimation[J].Chaos,Solitons&Fractals,2017,99:256-262.
[15]SRIRAAM N.Correlation dimension based lossless compression of EEG signals[J].Biomedical Signal Processing and Control,2012,7(4):379-388.
[16]张英堂,任国全,李国璋.柴油机振动信号分形特征诊断的改进算法[J].内燃机学报,2006,24(5):459-464.ZHANG Yingtang,REN Guoquan,LI Guozhang.Improved algorithm of diesel engine diagnosis based on fractal dimension of vibration signals[J].Transactions of CSICE,2006,24(5):459-464.
[17]LI Mengshan,HUANG Xingyuan,LIU Hesheng,et al.Prediction of gas solubility in polymers by back propagation artificial neural network based on self-adaptive particle swarm optimization algorithm and chaos theory[J].Fluid Phase Equilibria,2013,356(25):11-17.
[18]WU Jun,LU Jian,WANG Jiaquan.Application of chaos and fractal models to water quality time series prediction[J].Environmental Modeling&Software,2009,24(5):632-636.
[19]SUN Y,BABOVIC V,CHAN E S.Multi-step-ahead model error prediction using time-delay neural networks combined with chaos theory[J].Journal of Hydrology,2010,395(1/2):109-116.
[20]SUN Fenglan,GAO Lingxia,ZHU Wei,et al.Generalized exponential input-to-state stability of nonlinear systems with time delay[J].Communications in Nonlinear Science and Numerical Simulation,2017,44:352-359.
[21]WILLIAMS G.Chows theory tamed[M].London:CRC Press,2014:1-20.