عیب یابی در خطوط انتقال با استفاده از شبکه های عصبی با دامنه پیچیده

Fault location is a critical task when a severe disturbance is caused by insulation failure on a transmission line. In order to avoid further economical and social costs because of load interruptions, the fault diagnosis has to be concluded as soon as possible. Intelligent systems have been successful in dealing with fault diagnosis problems. This paper proposes the application of complex-domain neural networks for mapping the relationship between electrical signals and fault locations on transmission lines. Complex-domain neural networks allow voltage/current representation without arbitrarily decoupling amplitude and phase. Furthermore, several voltage and current representation schemes, based on electromagnetic transient and steady-state information, are analyzed in this paper. For comparison purpose, these input representations are also tested with real-domain neural networks. The tests consider realistic operating/fault conditions and assume that fault classification has already been handled.