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Space-Time Approach for Disturbance Detection and Classification



Hamid Gharavi


A major challenge facing future grid systems is to identify the source of abnormal behaviors caused by faults or voltage instability. In this paper Phasor Measurement Units (PMUs) have been considered for detecting disturbances and degradation in the grid. Considering that the source of voltage instability mainly impacts neighboring areas, we present a simple and yet efficient algorithm that can identify affected areas. The algorithm is based on K-Mean optimization that classifies PMUs into different classes of power quality. We introduce the concept of a space-time solution to identify multiple faults. Since this is a multi-objective problem, we extend the K- mean algorithm to achieve space-time optimization in two successive stages (i.e., time and space). As an alternative approach to the space-time solution, we also present a hierarchical network that is based on a distributed mapping technique. This approach may only require time- based clustering as it limits the PMU population search to a small local region. We used our real-time Virtual PMU (VPMU) embedded into our software in the loop testbed to verify the performance of the proposed schemes. For the IEEE 39-bus transmission system, it is shown that the proposed space-time synchrophasor data classification scheme is capable of detecting and isolating areas in the grid that suffer from multiple disturbances, such as faults.
IEEE Transactions on Smart Grid


Smart Grid, fault detection, synchrophasors, K-mean clustering, WAMS, SCADA, Virtual PMU (VPMU), PDC, Emulab, emulation.


Gharavi, H. (2017), Space-Time Approach for Disturbance Detection and Classification, IEEE Transactions on Smart Grid, [online], (Accessed February 29, 2024)
Created March 9, 2017, Updated November 30, 2017