Fault Classification in VSC-HVDC Transmission System using Machine Learning Approach

Keshri, Jay Prakash; Tiwari, Harpal

doi:10.1109/icps48983.2019.9067699

Cited by 5 publications

(1 citation statement)

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“…KNN model is used for the fault type classification module because it is not being activated continuously (mostly operating in idle phase) and the fourth class of this module (non-fault class) makes a redundancy, which compensates the startup unit's probable error by a different ML model. comparing to machine learningbased protection algorithms such as [38,39,42,43] which just covers dependability of protection (since all test cases are in-zone faults), our results also show the examination of the security aspect of protection reliability too. Although the reference [44] is proposed for VSC based HVDC systems, the results are only limited to fault detection with an accuracy of 86.5%.…”

Section: Highlighted Preferences Of the Proposed Algorithmmentioning

confidence: 63%

Fault detection and classification of an HVDC transmission line using a heterogenous multi‐machine learning algorithm

Ghashghaei

Akhbari

2021

IET Generation Trans & Dist

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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.

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