Avangrid to Create In-house Artificial Intelligence Systems

Orange, CT – Avangrid, Inc., a sustainable energy company and member of the Iberdrola Group, announced it has launched a Data Science and Analytics team aimed at developing unique and proprietary artificial intelligence (AI) systems that are focused on improving reliability for customers. The goal of these machine learning models, once complete, is to change how the company approaches grid investments, equipment upgrades, storm forecasting and more.

Avangrid’s Data Science and Analytics team sits within the Operational Performance organization and is made up of seven data scientists, engineers and analysts who come from industries like healthcare, astrophysics and finance. The team is creating three unique AI systems: Predictive Health Analytics, GeoMesh and HealthAI. Each technology will take existing data from Avangrid companies’ electric grids and analyze it to forecast future performance of the grid, determine the condition of grid equipment or target at-risk locations for inspections and investment, with the goal of increasing reliability for the 2.31 million electric customers served by Avangrid’s subsidiaries, including United Illuminating.

Predictive Health Analytics

This project takes a proactive approach to determine the condition of substation equipment such as circuit breakers, which are like fuses in a house, and uses data to prioritize planned replacements and upgrades. Traditionally, equipment is replaced primarily based on age or if it malfunctions and causes an outage. Predictive Health Analytics will take a data-driven approach to determine equipment’s overall health and life expectancy based on numerous factors, including age, frequency of use, manufacturer and maintenance notifications. This means Avangrid will save money and help reduce outages for customers by replacing at-risk equipment, such as circuit breakers, before it causes an outage.

GeoMesh

This project maps Avangrid’s service areas to identify the strengths and weakness of its electric networks to help forecast its performance during both blue-sky and storm scenarios. The goal is to improve understanding about how the electric grid is performing under various weather conditions so that Avangrid’s companies can better plan upgrades, storm response and more.

To accomplish this, GeoMesh breaks Avangrid’s service areas into small sections to allow the company to focus on one specific region at a time. For the chosen selection, GeoMesh makes predictions by analyzing millions of data points, such as average wind speed, precipitation type and amount, outage history and reason,

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HealthAI

This project analyzes Avangrid’s existing millions of high-resolution photos of its street-level distribution system – poles, wires and grid equipment – to identify the assets in the photos and, eventually, catalogue their health. This increases Avangrid’s awareness of the condition of its grid equipment and helps to identify areas of concern. HealthAI will save Avangrid time and money by targeting at-risk locations for inspections and maintenance. It will also reduce outages and improve safety for lineworkers by giving them more information before they arrive on scene.

population and density of tree limbs and other vegetation. All of this lets Avangrid make informed, data-based decisions on things like where and what upgrades are most needed or which customers are most likely to be impacted by a storm.

Currently, Avangrid is training the AI system to correctly identify grid equipment in photos, such as cross arms, transformers or wire. Next, the AI system will learn to analyze and determine the health of that equipment. For instance, it will identify if the cross arm is broken or if the wire is sagging. Right now, Avangrid learns of these equipment damages or failures from customer reports, manual inspections or outages. HealthAI will make it an automatic and proactive process to make adjustments before the customer is impacted. Long term, Avangrid aims to also use HealthAI to also identify threats to its distribution network such as hanging tree limbs or dead trees that may fall onto company electric lines.