Situational Awareness in Distribution Grid Using Micro-PMU Data: A Machine Learning Approach

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 real-world 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.