The recent development of distribution-level phasor measurement units, a.k.a. micro-PMUs, has been an important step towards 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
thesis. First, we introduce novel data-driven event detection techniques to pick out valuable portion of data from extremely large raw micro-PMU data. Subsequently, data-driven event classifier are 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 (multi-SVM) classifier is trained and tested over 15 days of real-world data from two micro-PMUs on a distribution feeder in Riverside, CA. 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, five real-world use-cases are presented for the proposed data analytics tools, including:
Transient Load Modeling for Application in Frequency Regulation Market;
Static Load Modeling;
Remote Asset Monitoring;
Protection System Diagnosis;
Lightning Initiated Contingency Analysis.