Detection of Weak Fault Signals in Power Grids Based on Single-Trap Resonance and Dissipative Chaotic Systems

Abstract

Aiming to solve the problem that the performance of classical time–frequency domain signal detection methods is severely degraded in highly noisy environments, a single-trap approximate model of the stochastic resonance of bistable systems is studied in this paper. This method improves the defects of the classical bistable stochastic resonance model that cause it to be inapplicable during non-periodic signal detection. Combining this method with the particle swarm optimization algorithm based on an attenuation factor and cross-correlation detection technology, detection experiments determining the impulse voltage fluctuation signals, motor speed fluctuation signals and low-frequency oscillation signals of a power system are conducted. The results show that the single-trap resonance model has good phase matching performance and noise cancellation abilities. Furthermore, combining it with two kinds of dissipative chaotic systems, a comprehensive frequency and amplitude detection experiment was carried out for multiple harmonic aliasing signals. The results show that the single-trap resonance model can achieve error-free detection of each harmonic frequency and high-precision detection of each harmonic amplitude in highly noisy environments. The research results will provide new ideas for the detection of various types of weak fault signals in power systems.