The recent development of distribution-level phasor measurement units, also known as micro-PMUs, has been an important step toward achieving situational awareness in power distribution networks. The challenge however is to transform the large amount of data that is generated by micro-PMUs to actionable information and then match the information to use cases with practical value to system operators. This open problem is addressed in this paper. First, we introduce a novel data-driven event detection technique to extract events from the extremely large collection of raw micro-PMU data. Subsequently, a data-driven event classifier is developed to effectively classify power quality events. Importantly, we use field expert knowledge and utility records to conduct an extensive data-driven event labeling. Moreover, certain aspects from event detection analysis are adopted as additional features to be fed into the classifier model. In this regard, a multi-class support vector machine classifier is trained and tested over 15 days of realworld data from two micro-PMUs on a distribution feeder in Riverside, CA, USA. In total, we analyze 1.2 billion measurement points and 10,700 events. The effectiveness of the developed event classifier is compared with prevalent multi-class classification methods, including k-nearest neighbor method as well as decision-tree method. Importantly, two real-world use-cases are presented for the proposed data analytics tools, including remote asset monitoring and distribution-level oscillation analysis.
Abstract-Built upon real-world SCADA and other measurements of a featured utility-scale testbed, this paper addresses the participation of customer side battery energy storage in providing peak load shaving at a 12.47 kV distribution feeder. A stochastic optimization-based battery operation framework is developed that enables feeder load peak shaving under offline (day-ahead) as well as online (close-to-real-time) control settings. Both designs work through establishing a secured communications line to the utility's feeder-level SCADA system. Multiple field experiments are conducted, including a full day test with complete control of a 1 MWh / 200 kW battery system, as well as various numerical assessments based upon one year of real feeder data.
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