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- W4320933429 abstract "In traditional power system, the kinetic energy in the rotating masses of the synchronous generators provides an inherent stability region. In the event of a disturbance, the synchronous generators can absorb or release kinetic energy to avoid the loss of synchronism, while the grid is stabilized and then restored to the nominal frequency by controllers. A key challenge with high penetration of inverter-based distributed energy resources (DERs) units in the power distribution systems and hence microgrids is that inverters normally have zero inertia causing a high rate of voltage and frequency variability in the event of an anomaly. The bane of this variability, leading to uncertainty, uncontrollability, violation of voltage and thermal limits, thus eclipses the very benefits of environmentally friendly and low-cost renewable energy. The anomalies can be due to cyber attacks or from load and generation perturbation, controller malfunctions and other faults. This paper proposes an explicit data driven solution against these treacherous anomalies without relying on the strong modeling assumption and cause of anomalies. It is shown that these anomalies may be caused by more than one sources simultaneously, and hence the source detection method should be capable to identify them. Detailed descriptions of a source identification procedure are given, and the effectiveness of the method is validated by using multiple anomalies such as islanded operation initiation, multiple cyber attacks affecting multiple sources, high fluctuations in load simulations and finally extreme mitigation approaches such as disaggregation. In addition performance in presence of ambient noise is also discussed." @default.
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- W4320933429 date "2023-05-01" @default.
- W4320933429 modified "2023-10-18" @default.
- W4320933429 title "Proactive anomaly source identification using novel ensemble learning with adaptive mitigation measures for microgrids" @default.
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- W4320933429 doi "https://doi.org/10.1016/j.epsr.2023.109157" @default.
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