Fault detection and prediction scheme for nonlinear stochastic distribution systems

Model-based fault detection technique has a broad range of applications because of the small change to the system when the system state is known to be available and the low cost. For nonlinear stochastic distribution systems containing uncertain disturbance term, a model-based fault detection and failure time prediction scheme is proposed in this paper, and observers are designed to detect whether the incipient fault has occurred in the system. The residual is obtained by comparing the output of the actual system with the output of the observer. When the residual exceeds the threshold value obtained by derivation, it is determined that the fault has occurred in the system. The fault size can then be estimated in real time and used to determine the time to failure (TTF) or the remaining useful life of the system. The TTF of the system is obtained by comparing the magnitude of the current system fault with the fault threshold. Finally, the feasibility of the presented fault detection scheme is proved by the Lyapunov stability theory and the validity of the scheme is proved by computer simulation.