Title :
Extraction of symptom for on-line diagnosis of power equipment based on method of time series analysis
Author :
Yang, L. ; Yang, M.Z. ; Yan, Z. ; Shi, B.Z.
Author_Institution :
Xi´´an Jiaotong Univ., China
Abstract :
Some methods for extracting the symptoms of faults in power equipment are presented by analyzing and modeling time series (TS). In this paper, the on-line data are preprocessed at first, which will be helpful to reduce the influence of temperature and humidity. And then the relevant autoregressive-moving average (ARMA) models are established. Furthermore, the characteristic values of the model can be calculated. These values are used as symptoms to estimate the state of power equipment. In this paper the concepts of stationary stochastic process and time series are also introduced. At last some measured data, which from a substation in Guangdong province prove the feasibility of the method
Keywords :
autoregressive moving average processes; insulation testing; stochastic processes; time series; Guangdong province; autoregressive moving average model; condition based maintenance; data processing; fault detection; insulation monitoring; on-line diagnosis; power equipment; stationary stochastic process; substation; time series; Data mining; Fault diagnosis; Humidity; Insulation; Power system reliability; Probability; Statistics; Stochastic processes; Temperature; Time series analysis;
Conference_Titel :
Properties and Applications of Dielectric Materials, 2000. Proceedings of the 6th International Conference on
Conference_Location :
Xi´an
Print_ISBN :
0-7803-5459-1
DOI :
10.1109/ICPADM.2000.875693
