Receding Horizon Observer Approach to Fault Detection in Electrical Transformers

Internal transformer faults could cause severe damage to electrical networks and also deteriorate the energy quality standard. Most of existing approaches for fault detection in transformers are based on signal processing methods or a static parity check of functionality. Two analytical redundancy approaches one based on dynamic parity checs and a second using a receding horizon observer, to internal fault detection in transformers are proposed in this paper. The main advantage of our proposal is that explicit knowledge of the Lipschitz constant of the transformer nonlinearities is not required and the considered algorithms has a high degree of portability. Furthermore, the effect of load and external faults on the residual are decoupled. Stability properties of the model are demonstrated. Some simulations shown the efficiency of the proposed detection method and highlight the reliability of the considered approach.