Extraction of symptom for on-line diagnosis of power equipment based on method of time series analysis

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.