Tutorial on PMU and Time Series Data Analysis

Date: Monday, May 24                               Time:  – 

Abstract: This tutorial is designed to train researchers and practitioners on data analysis methods and applications for synchrophasor and point on wave data. The course is divided into three sections, covering (1) fundamentals of synchrophasor measurement, (2) data analysis tools and techniques, and (3) practical applications in industry. The course will help attendees put concepts from power engineering and data science into practice to establish efficient workflows for analyzing and visualizing time series data at scale.

Part 1: Fundamentals synchrophasor measurement (45 min)
a) PMU measurement and physics (Alexandra von Meier, UC Berkeley)
b) Frequency calculation (Miles Rusch, UC Berkeley)
c) Open access data and software tools (Laurel Dunn, NI4AI)

The first part of the course describes foundational concepts for phasor measurement units (PMUs) and synchrophasors. This portion of the course covers concepts from physics and power engineering to provide intuition about how PMU measurements relate to physical phenomena on the grid. It will also cover methods for calculating phase angle and frequency from measurement data. This will offer a quick refresher of relevant concepts for attendees familiar with power engineering, and provides a brief introduction for attendees who are new to the field.

Part 2: Data analysis and machine learning workflows (90 min)
a) Introduction to AI (Sean Murphy, PingThings)
b) Data science workflows in Python (Chris Ryan, PingThings)
c) Voltage sag detection (Mohini Bariya, UC Berkeley)

The second part of the course will cover machine learning workflows for analyzing measurement data. Panelists will step through data visualization and analysis workflows in Jupyter Notebooks. Talks also demonstrate the use of open source data and software tools, and the code they present will be available to attendees via github.

Part 3: PMU applications in industry (45 min)
a) Model validation (Chetan Mishra, Dominion)
b) Use cases and lessons learned (Kevin Jones, Dominion)

The third part of the course will cover practical aspects of using synchrophasor data within a utility. We will discuss case studies of analytical tools developed at Dominion, and lessons learned from launching and scaling up their synchrophasor program. We will also discuss data management and archiving capabilities that have proved critical in enabling the rapid discovery and deployment of new use cases.


Name and title of the organizer: Laurel Dunn, PhD
Organisation: UC Berkeley, USA
Email: lndunn@berkeley.edu
Short biography of the chair: Guglielmo Frigo was born in Padua, Italy, in 1986. He received the B.Sc. and M.Sc. degrees in biomedical engineering from the University of Padova in 2008 and 2011, respectively, and the Ph.D. degree from the School of Information Engineering in 2015, with a dissertation about compressive sensing (CS) theory applications to instrumentation and measurement scenario. He served as PostDoc researcher at the Electronic Measurement Research Group, University of Padova (2015-2017), and at the Distributed Electrical Laboratory, Swiss Federal Institute of Technology of Lausanne (2018-2020). In 2020 he was foreign guest researcher at NIST, Gaithersburg, USA and he is currently scientist at METAS, Wabern, Switzerland. His current research interests include the development of enhanced measurement infrastructures for electrical systems.


Alexandra “Sascha” von Meier (University of California, Berkeley) is an Adjunct Professor in the Department of Electrical Engineering and Computer Science, where she teaches a course on Electric Power Systems. She is also Director in CIEE’s Electric Grid program area focusing on power distribution systems, Smart Grid issues, and the integration of distributed and intermittent generation. Her current research projects center on the use of high-precision micro-synchrophasor measurements for situational awareness, diagnostics and control applications in distribution grids.

Kevin Jones (Dominion Energy) received his Ph.D. in Electrical Engineering from Virginia Tech as a Harry Lynde Bradley Fellow in 2013. He created and helped commercialize the open source linear state estimator and has led the development and integration of a variety of innovative technology solutions in his role at Dominion Energy including the cloud deployment of PredictiveGrid for synchrophasor analytics in Electric Transmission. He currently serves as Manager of ET Operations Engineering Support leading the Fault Analysis, Sensor Data Communication/Engineering/Analytics, Special Studies, and Web Development teams.

Chetan Mishra (Dominion Energy) is an electric transmission engineer with research interests in nonlinear dynamics and control, synchrophasors and renewable energy. He earned his PhD in Electrical Engineering from Virginia Tech in 2017 and has been with Dominion for over five years.

Laurel Dunn (University of California, Berkeley) is a Civil Engineer specializing in data-driven risk assessment and decision analysis for power systems. She has engaged with utilities, regulators, and policymakers about issues like grid modernization and climate resilience. She hopes to help society realize the benefits of scientific advancements by being a facilitator for knowledge transfer among institutions and people.

Mohini Bariya (University of California, Berkeley) is a PhD candidate focusing on the use of novel, high-resolution measurements for improved situational awareness in the electric grid. She has worked extensively with real PMU datasets. She has experience teaching science and engineering concepts to different audiences, including as a graduate student instructor for the Electric Power Systems course at Berkeley.

Miles Rusch (University of California, Berkeley) is a PhD student in Electrical Engineering at UC Berkeley. His research involves using unsupervised learning algorithms to perform data analysis on electric power systems.

Sean Murphy (PingThings) is the CEO of PingThings, developers of the world’s fastest and most scalable platform for time series data to synthesize a deep understanding of large scale systems based on sensor data. Previously, he founded and built a multi-million dollar consulting firm focused on data science and AI after completing his MBA at Oxford and, served as a senior scientist at the Johns Hopkins University Applied Physics Laboratory focused on machine learning, anomaly detection, and high performance and cloud computing.