Biblio

The core looseness fault is an important part of transformer fault. The state of the core can be obtained by analyzing the vibration signal. Vibration analysis method has been used in transformer condition monitoring and fault diagnosis for many years, while different methods produce different results. In order to select the correct method in engineering application, five kinds of joint time-frequency analysis methods, such as short-time Fourier transform, Wigner-Ville distribution, S transform, wavelet transform and empirical mode decomposition are compared, and the advantages and disadvantages of these methods for dealing with the vibration signal of transformer core are analyzed in this paper. It indicates that wavelet transform and empirical mode decomposition have more advantages in the diagnosis of core looseness fault. The conclusions have referential significance for the diagnosis of transformer faults in engineering.

Based on Markov chain analysis method, the situation prediction of smart grid security and stability can be judged in this paper. First component state transition probability matrix and component state prediction were defined. A fast derivation method of Markov state transition probability matrix using in system state prediction was proposed. The Matlab program using this method was compiled to analyze and obtain the future state probability distribution of grid system. As a comparison the system state distribution was simulated based on sequential Monte Carlo method, which was in good agreement with the state transition matrix, and the validity of the method was verified. Furthermore, the situation prediction of the six-node example was analyzed, which provided an effective prediction and analysis tool for the security situation.