Charles River Analytics Inc., developer of intelligent systems solutions, is building our Probabilistic Operations Warranted for Energy Reliability Evaluation and Diagnostics (POWERED) app under a contract awarded by the US Army Corps of Engineers (USACE), Engineer Research and Development Center (ERDC). POWERED helps operators predict energy equipment reliability before an outage. We have proudly partnered with City Light & Power, Inc., provider of electric utility systems, on the POWERED effort.
Over time, the performance of energy equipment degrades. A power supply failure in a critical situation can have a serious impact to residents, commercial businesses, and local, state, and federal Government. Our POWERED app uses rich, modular probabilistic modeling to help operators determine the reliability of power supplies before a crisis occurs. During unexpected failures, POWERED also significantly decreases the cost of unexpected outages.
“POWERED is a prognostic and diagnostic app for energy equipment that can help operators maintain power supplies and protect against future risks,” said Max Metzger, Senior Software Engineer at Charles River Analytics. “Our POWERED app is driven by the Army’s overall vision of energy-informed operations that provide a reliable and uninterrupted power supply with improved operational efficiency.”
Our POWERED app helps maintain and protect power supplies
POWERED includes a distributed network of continuous-time dynamic probabilistic relational models to accurately and efficiently diagnose and predict system- and component-level failures due to long-term degradation. We are building detailed models of energy supply health and status using Figaro™, our flexible probabilistic programming language.
Contact us to learn more about POWERED and our other Probabilistic Modeling/Programming capabilities.
This material is based upon work supported by the Small Business Innovative Research (SBIR) Program and the Engineering Research and Development Center (ERDC) – Construction Engineering Research Laboratory (CERL) under Contract W9132T-17-C-0018. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the SBIR Program and ERDC-CERL.
