Open AccessFault detection and classification of an HVDC transmission line using a heterogenous multi‐machine learning algorithm
Author(s) -
Ghashghaei Saber,
Akhbari Mahdi
Publication year - 2021
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/gtd2.12180
Subject(s) - support vector machine , computer science , fault (geology) , high voltage direct current , algorithm , fault detection and isolation , classifier (uml) , fault indicator , matlab , artificial intelligence , engineering , pattern recognition (psychology) , voltage , direct current , electrical engineering , actuator , seismology , geology , operating system
This paper presents a novel integrated multi‐Machine Learning (ML) system architecture for the protection of bipolar HVDC transmission line in which different ML models of Support Vector Machine (SVM) and K‐Nearest Neighbours (KNN) are used for fault detection and classification. The KNN fault type classifier is designed as a dual‐purpose module, which not only detects the fault type but also acts as a redundant module for unsure fault declaration from the startup unit. Gradients and standard deviations of DC current, voltage, harmonic current, and a correlation coefficient between the aerial and zero modes of DC current are appropriate feature vector extracted from single‐end signal measurement. Overall, 154 training cases and 53 main test cases are obtained by simulating various fault and non‐fault states on a ± 650 kV‐1000 km Current Source Converter (CSC)–HVDC using an EMTDC/PSCAD platform. The ML modules are trained in MATLAB and tested under different severe conditions with a total of 2220 test cases. Thanks to the appropriate feature vector and the proposed system architecture, the obtained results show that the proposed algorithm is effective enough to detect and distinguish a variety of internal faults and pseudo‐faults/external faults. Also, it needs low training data requirements.