An ANN Based Fault Diagnosis System for Tapped HV/EHV Power Transmission Lines

Abstract. This paper presents a design for a fault diagnosis system(FDS) for tapped high/extra-high voltage (HV/EHV) powertransmission lines (TL s). These tapped lines have two differentprotection zones. The proposed approach reduces the cost and thecomplexity of the FDS for these types of lines. The FDS, basically,utilizes fifteen artificial neural networks (ANN s) to reach its outputdiagnosis. The FDS basic objectives are mainly: 1. the detection of thesystem fault; 2. the localization of the faulted zone; 3. theclassification of the fault type; and finally 4. the identification of thefaulted phase. This FDS is structured in a three hierarchical stages. Inthe first stage, a preprocessing unit to the input data is performed. AnANN, in the second stage, is designed in order to detect and zonelocalize the line faults. In the third stage, two zone diagnosis systems(ZDS) are designed. Each ZDS is dedicated to one zone and consistsof seven parallel-cascaded ANN s. Four-parallel ANN s are designedin order to achieve the fault type classification. While, the other threecascaded ANN’s are designed mainly for the selection of the faultedphase. A smoothing unit is also configured to smooth out the outputresponse of the proposed FDS.The proposed FDS is designed and evaluated using the localmeasurements of the three-phase voltage and current samples acquiredat only one side. A sampling rate of 16 samples per cycle of the powerfrequency was taken. A data window of 4 samples was also utilized.These samples were generated using the EMTP simulation program,applied to the High-Dam/Cairo 500 kV tapped TL. All possible shuntfault types were considered. The effect of fault location and faultincipience time were also included. Moreover, the effect of load andcapacitor switchings on the FDS performance was investigated.Testing results have proved the capability as well as the effectivenessof the proposed FDS.