Abstract
This article proposes a clustering-based hierarchical framework that includes a consensus decision support system for locating faults in smart grids. Frequency measurements are initially collected by distributed frequency disturbance recorders, and then, decomposed in the time-frequency domain. Extracted time-frequency variational modes are further analyzed through statistical analysis. The resulted features are then used by the affinity propagation (AP) clustering technique to partition the power grid. The faulty partition is determined by evaluating a heuristic index, and, is then fed to a zNumber-based multicriteria group decision support system to decide on the fault location. The effect of various preferences on AP clustering has been handled by resorting to an aggregation scheme, which considers multiple criteria into account. The feasibility and effectiveness of the proposed framework have been validated through a comprehensive study on the IEEE 39-bus system.
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