Fault diagnosis of power systems using binary information of breakers and relays through DPNs

Many fault diagnosis techniques have been developed ranging from basic logic tables to recent sophisticated techniques like artificial neural networks, expert systems and model-based observer methods. Many of these recent methods need some kind of deep knowledge of the process to be successfully implemented. Model-based diagnosis is the use of models of correct behavior for diagnostic purposes. This paper describes a model-based system using discrete Petri nets (DPNs) technique which, can diagnose and estimate the faulty section(s) of electric power systems even in case of single fault or multiple faults. Moreover, inured zone faults are discriminated as well. The proposed models use discrete information of breakers, relays (primary and backup one) and communication link as well. Mainly two models are studied and investigated. The first one is transmission line bus bar (TL_BB) model and the second is transformer busbar (TR_BB) model. The final diagnostic report contains information about relay operation and breaker operation as well. The developed methodology has been tested extensively using numerous fault cases of an actual 220/66 kV substation network. Fast and accurate results are obtained and reported. The viability of the proposed methodology, is evident in case of on/off-line as training tool.