Anti-disturbance fault diagnosis for non-Gaussian stochastic distribution systems with multiple disturbances

Abstract

In this paper, an anti-disturbance fault diagnosis scheme is proposed for non-Gaussian stochastic distribution systems (SDSs) with multiple disturbances. The available driven information for fault diagnosis is probability density functions (PDFs) of output rather than output value. Using B-spline expansion technique, the output PDFs can be approximated in terms of dynamic weights of B-spline neural network by which a nonlinear model can be established between input and weights. Therefore, the concerned problem is transformed into fault diagnosis problem of the weighting system presented by an uncertain nonlinear system with multiple disturbances and time-varying fault. Different from most of the existing results, the multiple disturbances are supposed to include unknown disturbance modeled by an exo-system and norm bounded uncertain disturbances. In the proposed approach, a disturbance observer is designed to estimate and compensate the modeled disturbance, and H∞ optimization technology is applied to attenuate the norm bounded disturbance. Finally, simulation results are given to show the efficiency of the proposed approach.