Power Grid Partitioning and Monitoring Methods for Improving Resilience

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Virginia Tech


This dissertation aims to develop decision-making tools that aid power grid operators in mitigating extreme events. Two distinct areas are focused on: a) improving grid performance after a severe disturbance, and b) enhancing grid monitoring to facilitate timely preventive actions. The first part of the dissertation presents a proactive islanding strategy to split the bulk power transmission system into smaller self-adequate islands in order to arrest the propagation of cascading failures after an event. Heuristic methods are proposed to determine in what sequence should the island boundary lines be disconnected such that there are no operation constraint violations. The idea of optimal partitioning is further extended to the distribution network. A planning problem for determining which parts of the existing distribution grid can be converted to microgrids is formulated. This partitioning formulation addresses safety limits, uncertainties in load and generation, availability of grid-forming units, and topology constraints such as maintaining network radiality. Microgrids help maintain energy supply to critical loads during grid outages, thereby improving resilience. The second part of the dissertation focuses on wide-area monitoring using Phasor Measurement Unit (PMU) data. Strategies for data imputation and prediction exploiting the spatio-temporal correlation in PMU measurements are outlined. A deep-learning-based methodology for identifying the location of temporary power systems faults is also illustrated. As severe weather events become more frequent, and the threats from coordinated cyber intrusions increase, formulating strategies to reduce the impact of such events on the power grid becomes important; and the approaches outlined in this work can find application in this context.



Power systems resilience, proactive islanding, microgrids, energy adequacy, transmission line switching, PMU data quality, temporary faults, network radiality