To stay at the leading edge, the company participates in many IEEE utility technical committees, assisting in creating new power quality standards. Another important facet is the PMI educational library. Their engineers have written hundreds of white papers and case studies on power quality topics, focused on helping utility engineers understand complex data and solve problems. Many of these are based on actual utility issues and problems solved. “By creating educational papers based on real world utility cases, we not only train our engineers on the needs of our customers, but also spread this hard-won knowledge to all other utilities,” states Mullins. In addition, PMI provides both free and accredited power quality training classes, along with handson product training. These training sessions are available in-person around the country and online virtually.

As an example of customer-driven product development, Mullins recollects an instance when two utilities approached him with a need for a low-cost way to measure and control small scale distributed generation. Existing solutions for large solar and wind systems do not scale down to the smaller, much more common 25 kW to 1 MW installations in distribution. The utilities asked PMI to create a product that would allow them to not only control their customer generation but also measure it. Working with the two utilities to create this focused product, Power Monitors introduced the Eclipse, an ideal system for Distributed Energy Resource (DERs) control and monitoring. “It enabled real-time monitoring with email alerts and cloud-based data storage and analysis, helping our clients track DER power flow and voltage regulation, and also gives them a method to switch the generation off during times of voltage regulation problems, weather events, or work on the line,” affirms Mullins.

Speaking about the PMI roadmap, Mullins discussed the PMI R&D team’s focus on advanced machine learning and analytics. Underlining their impact on the utility sector, he draws attention to the improvements not only in power quality, but also in distribution reliability that stem from machine learning algorithms predicting faults. The machine learning and AI tools by Power Monitors will allow utilities to automatically identify existing power quality problems, and even predict problems and equipment failures before they happen. “This is the next step for utilities, where a system will be assisting them with cloud-based machine learning to help them understand what’s happening now, and what will happen in the future, without having to look at every piece of information manually,” concludes Mullins.