Expertise for Experts: What do you need to know about PMU data?

NI4AI is building bridges between different areas of expertise.

Laurel Dunn

July 31, 2020

Veteran power systems engineers spend decades learning to excel in their jobs. This learning happens in school, professional training, and from real-world experience.

Becoming a skilled data scientist also takes practice. Data scientists often have advanced degrees in statistics or computer science. These degrees often involve a grueling course load and at least a few all-nighters. Given the overhead involved in becoming an in either domain - for most people it simply isn’t practical to become experts in both.

This blog post points you to some of our favorite resources explaining and motivating grid data analytics. We’ve curated two lists: one for data scientists and another for power systems engineers.

Have a favorite paper or textbook we missed? Send us an email:

For data scientists:

If you know data but want to build intuition about what the data can tell us about the grid, this list is for you.

  • Electric Power Systems: A conceptual introduction is a short and easy read about how the grid works. The book covers the physics of grid components, and touches on how interactions between these components alter physical quantities on the grid — including the quantities that NI4AI sensors are monitoring. The book is written by Alexandra von Meier. Prof. von Meier is an adjunct professor in electrical engineering at UC Berkeley, a research scientist at LBNL, and director of CIEE’s Grid Program. She is also a frequent contributor to our blog!
  • Renewable and Efficient Electric Power Systems is a definitive guide to renewable energy technologies, and their impacts on distribution grids. The textbook offers an in-depth guide to how renewable energy technologies work and the practical considerations related to integrating renewable technologies onto the grid. The book is written by Gilbert M. Masters, a professor emeritus at Stanford.
  • Data Mining Techniques and Tools for Synchrophasor Data (here’s the link) offers a synthesis of machine learning methods that have been used to analyze synchrophasor data. The report is primarily geared at industry practitioners, but gives useful context on why and how the industry thinks about leveraging data resources.

Read to start analyzing synchrophasor data? You can get experience working with synchrophasor data by analyzing the “Sunshine” dataset.

For power systems engineers:

Utility analytics is a hot topic these days, and it can be hard to stay up to date. We follow the North American Synchrophasor Initiative (NASPI), a consortium of industry practitioners, vendors, and researchers geared at advancing the use of synchrophasors and other sensor technologies.

Here are a few resources by NASPI and others that we’ve found to be particularly helpful.

  • Zero to One: A Utility Playbook for (Finally) Delivering Sustainable Value from Your Synchrophasor Data by Enabling Easy Data Exploration and Rapid Use Case Prototyping by Dr. Kevin Jones at Dominion Energy. The paper was published in CIGRE Grid of the Future in 2019, and is specifically geared at sharing lessons learned with other professionals in the industry. Dr. Jones has been a key player in launching the data analytics program at Dominion, which spans engineering, operational, and business decisions.
  • High-Resolution, Time-Synchronized Grid Monitoring Devices (here’s the link) is a recent NASPI report about state-of-the-art sensor technologies and implications on the grid. The report describes what high-frequency sensors can tell us that conventional sensor networks cannot. It also discusses barriers to extracting these insights. Some of these are barriers that NI4AI seeks to address.
  • Data Mining Techniques and Tools for Synchrophasor Data (here’s the link) offers an overview of methods that industry practitioners have used to extract insights from PMU data. Many of the analytics presented in the report could be replicated using data hosted in NI4AI. Stay tuned for blog posts and Jupyter notebooks about analytical methods.

For Anyone Curious to Learn More:

For more updates about new blog posts, datasets, tutorials, and events — we encourage you to follow us on LinkedIn.


Laurel Dunn

Laurel 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.