Power leader fault detection in nonlinear leader-follower networks

This paper presents a model for fault detection of a power leader in nonlinear leader-follower networks. The fault detection method is developed for the network model proposed by Wang and Slotine. Every follower is coupled with a nonlinear, neural net based observer for fault detection. Neural net tuning algorithms are derived and fault identifiers are developed using the Lyapunov approach. We consider fault detection of the power leader, and how such fault propagates through the network. We estimate the power leader fault detectability time based on the followers¿ observers. The paper studies properties of the fault dynamics i.e., the dynamics of a fault evolution process through a network of interconnected dynamic elements. The approach for leader-follower fault detection can also be used with any other type of nonlinear systems observer. Simulation results are presented to illustrate the effectiveness of the proposed technique.