Open AccessWavelet Based Fault Detection and Classification Algorithm for a Real Distribution Feeder
Author(s) -
Hatice OkumuÅŸ,
Fatih Mehmet Nuroğlu
Publication year - 2019
Publication title -
emitter international journal of engineering technology
Language(s) - English
Resource type - Journals
eISSN - 2443-1168
pISSN - 2355-391X
DOI - 10.24003/emitter.v7i1.382
Subject(s) - busbar , algorithm , fault (geology) , wavelet transform , fault detection and isolation , wavelet , transformer , electric power system , computer science , power flow , engineering , power (physics) , artificial intelligence , voltage , electrical engineering , seismology , actuator , geology , physics , quantum mechanics
As the importance of protection in power systems increase, knowing the type of malfunction occurring in the system has become crucial. Especially in the distribution system where electricity is delivered to the consumer, detecting the right fault type with a short amount of time is important. For this purpose in this study, Akyazı-Düzköy distribution feeder in Trabzon province, where faults commonly occur, is modeled with Digsilent Powerfactory. The model is performed with actual parameters including 465 lines, 243 loads, 233 transformers and 1093 busbars. First, the load flow and short circuit analysis have been carried out for the validation of the model. Then a fault detection and classification algorithm is enhanced using the wavelet transform and the energy of the coefficients. Different types of short circuit faults are created at different points on the model to test the accuracy of the algorithm. The fault inception time and the effect of the fault resistance are also investigated.