Intelligent Decision Support and Fusion Models for Fault Detection and Location in Power Grids
IEEE Transactions on Emerging Topics in Computational Intelligence
Computational intelligence, Decision support systems, Fault detection, Fault location, Machine learning, Power grids
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.
Hassani, Hossein; Razavi-Far, Roozbeh; Saif, Mehrdad; Zarei, Jafar; and Blaabjerg, Frede. (2022). Intelligent Decision Support and Fusion Models for Fault Detection and Location in Power Grids. IEEE Transactions on Emerging Topics in Computational Intelligence, 6 (3), 530-543.