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Iranian Journal of Electrical and Electronic Engineering، جلد ۹، شماره ۳، صفحات ۱۷۷-۱۸۸
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عنوان انگلیسی Accurate Fault Classification of Transmission Line Using Wavelet Transform and Probabilistic Neural Network
چکیده انگلیسی مقاله Fault classification in distance protection of transmission lines, with considering the wide variation in the fault operating conditions, has been very challenging task. This paper presents a probabilistic neural network (PNN) and new feature selection technique for fault classification in transmission lines. Initially, wavelet transform is used for feature extraction from half cycle of post-fault three phase currents at one end of line. In the proposed method three classifiers corresponding with three phases are used which fed by normalized particular features as wavelet energy ratio (WER) and ground index (GI). The PNNs are trained to provide faulted phase selection in different ten fault types. Finally, logic outputs of classifiers and GI identify the fault type. The feasibility of the proposed algorithm is tested on transmission line using PSCAD/EMTDC software. Variation of operating conditions in train cases is limited, but it is wide for test cases. Also, quantity of the test data sets is larger than the train data sets. The results indicate that the proposed technique is high speed, accurate and robust for a wide variation in operating conditions and noisy environments.
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نویسندگان مقاله m mollanezhad حیدر آبادی | m mollanezhad heydar abadi
semnan iran


a اکبری فرود | a akbari foroud
semnan iran
نشانی اینترنتی http://ijeee.iust.ac.ir/browse.php?a_code=A-10-515-6&slc_lang=en&sid=en
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کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده 1-Fault Diagnosis
نوع مقاله منتشر شده Research Paper
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