LAUSR

Power equipment is one kind of basic element in smart grid, and how to design an efficient detection and analysis scheme of electric signature (ES) for power equipment failure (PEF) monitoring is a key and challenging issue. This article proposes an ES detection and analysis method which can monitor multiple kinds of PEF in smart substation. The bottleneck of ES analysis is explored in the view of Heisenberg uncertainty, and an optimal time–frequency analysis method is designed to solve the problems. The proposed method (PM) is based on union of time and frequency bases whose decomposition is realized by Bayesian compressive sensing using Laplace prior. Simulated and field ESs are employed to test PM with comparisons of existing methods. Also, PM is applied in a smart substation of China. Several typical PEFs and measurement soft failures caused by electromagnetic interference are discussed. The results indicate that the PM can accurately monitor PEFs whose mechanism can be revealed by time–frequency features of ESs, if the required sampling rate and sampling time are satisfied because of its immunity of the uncertainty principle restriction. The robustness in noise environment and optimal time–frequency representation of ESs make the PM an efficient general-purpose PEF monitoring in smart grid by time–frequency analysis.