| Title | Situational Awareness in Distribution Grid Using Micro-PMU Data: A Machine Learning Approach |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Shahsavari, Alireza, Farajollahi, Mohammad, Stewart, Emma, Rad, Hamed Mohsenian |
| Conference Name | 2020 IEEE Power Energy Society General Meeting (PESGM) |
| Keywords | composability, event detection, phasor measurement units, power quality, Power systems, Predictive Metrics, pubcrawl, Resiliency, situational awareness, Support vector machines, Tools, Transforms |
| Abstract | 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 paper. First, we introduce a novel data-driven event detection technique to pick out valuable portion of data from extremely large raw micro-PMU data. Subsequently, a datadriven 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 (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, two real-world use-cases are presented for the proposed data analytics tools, including remote asset monitoring and distribution-level oscillation analysis. |
| DOI | 10.1109/PESGM41954.2020.9281911 |
| Citation Key | shahsavari_situational_2020 |
Situational Awareness in Distribution Grid Using Micro-PMU Data: A Machine Learning Approach