Robust H<inf>∞</inf> fault diagnosis for stochastic distribution systems with disturbance rejection performance

Abstract

The task of robust fault diagnosis of stochastic distribution control (SDC) systems with unknown external disturbance is to use the measured input and the system output PDFs to still obtain possible faults information of the system. In this paper, an enhanced robust fault diagnosis scheme is presented for the non-Gaussian stochastic distribution systems (SDCs). The available driven information for fault diagnosis is the probability density functions (PDFs) rather than the real output value. Different from conventional FDD problems, the measured information for FDD is the output probability density functions and the stochastic variables involved are not conned to Gaussian ones. A B-spline neura 1 network model is established, where a static neural network is applied to model the output PDFs and a nonlinear dynamic model is used to describe the relationships between the input and the weight. The concerned problem is transformed into the fault diagnosis problem of the weighting system presented by a nonlinear system with unknown external disturbance. The composite observer for SDCs is constructed by combining a fault diagnosis observer with a disturbance observer, with which the fault can be diagnosed and the disturbance can be rejected simultaneously. At last, an illustrated example is given to demonstrate the effectiveness of the proposed algorithm, and satisfactory results have been obtained.