Electric power grids constantly confront potential fast- and slow-dynamic disruptions ranging from unpredictable faults, weather-driven disasters, malicious cybersecurity attacks, load variations, among others. With the growing demand to ensure electricity with higher quality to the end-use customers and mission-critical systems and services, enhancing the resilience and operational endurance of the power delivery infrastructure against disruptive events and reducing and mitigating such threatening risks is urgently needed. This calls for fundamental advancements of new, fast, and efficient analytical frameworks for online situational awareness in power grids that can accurately measure and effectively monitor, detect, adapt and respond to a wide range of threats.
We first propose an inclusive next-generation smart sensor technology embedded with novel and sophisticated data-driven analytics for online surveillance and situational awareness in power grids. The proposed analytics take the electrical signals as the input and unlock the full potential in advanced signal processing and machine learning for real-time pattern recognition, event detection and classification. A robust measurement mechanism is housed within the proposed sensor technology that will be triggered following a detected event and guides on the adaptive selection of the best-fit and most accurate synchrophasor estimation algorithms at all times. Embedding such analytics within the sensors and closer to where the data is generated, the proposed distributed intelligence mechanism mitigates the potential risks to communication failures and latencies, as well as malicious cyber threats, which would otherwise compromise the trustworthiness of the end-use applications in distant control centers. Our experiments demonstrate that the introduced sensor technology achieves a promising event detection and classification accuracy with improved quality of measurements, collectively resulting in enhanced online situational awareness in power grids. Also, the performance of the proposed smart sensor analytic is tested and verified in several event detection applications in the power grid.
|Commitee:||Harrington, Robert, Doroslovački, Miloš, Ahmadi, Shahrokh, Shamma, Mohammed|
|School:||The George Washington University|
|School Location:||United States -- District of Columbia|
|Source:||DAI-B 82/2(E), Dissertation Abstracts International|
|Subjects:||Electrical engineering, Information Technology, Computer Engineering|
|Keywords:||Event detection and classification, Machine learning, Power system resilience, Situational awareness, Synchrophasor estimation, Wavelet transform (WT)|
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