Dalgacık Paket Dönüşümü, ReliefF Özellik Seçimi ve Topluluk Öğrenme Algoritması Tabanlı Bir Kısmi Deşarj Arızası Tespit Yöntemi

Year 2023, Volume: 35 Issue: 2, 505 - 516, 01.09.2023

Belkıs Erişti

https://doi.org/10.35234/fumbd.1284537

Abstract

Enerji nakil hatlarında birçok arıza olayı meydana gelebilmektedir. Özellikle hatlarda faz iletkenlerinin çevresel bitki örtüleriyle ve birbirleriyle temas etmeleri sonucunda oluşan arızalar sıklıkla meydana gelir. Bu şekilde oluşabilecek arızaların önüne geçebilmek için özellikle enerji nakil hatlarında izolasyonlu iletkenler yaygın olarak kullanılmaktadır. Ancak izolasyonlu iletkenlerin yalıtım malzemesinde meydana gelebilecek deformasyonlar bu iletkenlerde kısmi deşarj (KD) adı verilen olaylara sebep olabilirler. Oluşabilecek çok daha büyük arızaların önüne geçebilmek için KD’lerin hızlı bir şekilde tespit edilmesi gerekir. Bu çalışmada, iletim hatlarında meydana gelen KD’lerin tespiti için dalgacık paket dönüşümü (DPD), ReliefF özellik seçim yaklaşımı ve topluluk öğrenme algoritma sınıflandırıcı tabanlı etkili bir tespit yaklaşımı önerilmiştir. Bu yaklaşımın en önemli özelliği, KD verilerinin DPD kullanarak etkili frekans bantlarına dayanan özellikler elde edilmesi ve ReliefF yaklaşımı kullanılarak bu özellikler içerisinden tespit performansı yüksek özelliklerin seçilmesidir. Önerilen tespit sistemi VSB gerçek veri seti kullanılarak test edilmiş ve 89.22% doğruluk oranı elde edilmiştir. Literatürde VSB veri seti kullanan benzer çalışmalarla karşılaştırıldığında başarımın oldukça yüksek olduğu ve önerilen yaklaşımın KD tespiti için etkili bir performans sergilediği görülmüştür.

Keywords

Kısmi deşarj, arıza tespiti, dalgacık paket dönüşümü, ReliefF, topluluk öğrenme algoritması

Supporting Institution

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Project Number

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Thanks

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A Partial Discharge Fault Detection Method Based on Wavelet Packet Transform, ReliefF Feature Selection and Ensemble Learning Algorithm

Abstract

Many faults can occur in power transmission lines. Especially in power transmission lines, faults occur frequently as a result of phase conductors coming into contact with environmental vegetation and each other. Insulated conductors are widely used, especially in power transmission lines, in order to prevent malfunctions that may occur in this way. However, deformations that may occur in the insulating material of insulated conductors may cause events called partial discharge (PD) in these conductors. PD need to be detected quickly in order to prevent much larger failures that may occur. In this paper, an effective detection approach based on wavelet packet transform (WPT), ReliefF feature selection approach and ensemble learning algorithm classifier is proposed for the detection of PD in the transmission line. The most important advantage of this approach is to obtain features based on effective frequency bands by using WPT of PD data and to select features with high detection performance among these features by using ReliefF approach. The proposed detection system is tested using the VSB real dataset and an accuracy rate of 89,22% is obtained. When compared with similar studies using VSB dataset in the literature, it has been seen that the performance is quite high and the proposed approach has an effective performance for PD detection.

Keywords

Partial discharge, fault detection, wavelet packet transform, ReliefF, ensemble learning algorithm.

Project Number

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