Locating Faults in Distribution Systems in the Presence of Distributed Generation using Machine Learning Techniques

A novel data-based method is proposed to solve a multi-step fault classification and identification problem in distribution systems. Several machine learning techniques such as artificial neural network (ANN), support vector machine (SVM), bagged tree (BT), and adaptive boosting (AdaBoost) are utili...

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Vydáno v:IEEE International Symposium on Power Electronics for Distributed Generation Systems (Online) s. 1 - 6
Hlavní autoři: Mesbah Maruf, H M, Muller, Felicitas, Hassan, Md. Shakawat, Chowdhury, Badrul
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.06.2018
Témata:
Adaboost
artificial neural network
Artificial neural networks
Circuit faults
Fault location
high impedance faults
Impedance
Machine learning
support vector machine
Support vector machines
ISSN:2329-5767
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Shrnutí:A novel data-based method is proposed to solve a multi-step fault classification and identification problem in distribution systems. Several machine learning techniques such as artificial neural network (ANN), support vector machine (SVM), bagged tree (BT), and adaptive boosting (AdaBoost) are utilized by employing the sensor and smart meter data containing the voltage and current measurements in the presence of distributed generations (DG) at certain buses on the feeder. Considering the strength of each technique, a new method is applied to combine the predictions of different classifiers at three different steps: (a) classification of faulty phase, (b) detection of impedance level, and (c) identification of faulty line segment to improve the overall accuracy. The proposed method is validated on the modified IEEE 13 bus test system for phase to ground (L-G) type faults. The performance of the proposed method is also evaluated considering system uncertainties like missing sensor data and varying DG penetration level. K-means clustering method is used to predict the missing data to improve overall accuracy.
ISSN:2329-5767
DOI:10.1109/PEDG.2018.8447728