Intelligent Decision Support and Fusion Models for Fault Detection and Location in Power Grids

Document Type

Article

Publication Date

6-1-2022

Publication Title

IEEE Transactions on Emerging Topics in Computational Intelligence

Volume

6

Issue

3

First Page

530

Keywords

Computational intelligence, Decision support systems, Fault detection, Fault location, Machine learning, Power grids

Last Page

543

Abstract

Computational intelligence-based diagnostic frameworks have emerged as rapidly evolving but highly efficient approaches for diagnosing faults in power grids. This work aims to build a diagnostic framework by resorting to computational intelligence techniques to improve decision-making and diagnostic accuracy. This diagnostic framework has three modules for signal processing, fault detection, and location. The signal-processing module uses the variational mode decomposition technique to extract informative time-frequency features from the voltage and frequency signals. Voltage features are then fed into the fault detection module to train a set of modular support vector machines that are used for monitoring the binary state of each node in the power grid. Once a faulty state on a node is detected, it activates the third module for identifying fault location. This module benefits from a novel zSlices-based general type-2 fuzzy fusion model for the sake of identifying the fault type as well as mitigating the false alarm rate. The exact location of the fault is then determined through a fuzzy decision support system that is equipped with a recommendation mechanism for the sake of consensus reaching. Various scenarios are simulated on the IEEE 39-bus system and on an experimental setup of a Three-Bus Two-Line transmission system, where the attained results verify the applicability, efficiency, and robustness of the proposed framework.

DOI

10.1109/TETCI.2021.3104330

E-ISSN

2471285X

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