Improving Power Grid Reliability Using IoT Analytics

Society depends on reliable utility services to ensure the health and safety of our communities. Electrical grid failures have impact and consequences that can range from daily inconveniences to catastrophic events. Ensuring grid reliability means that data is fully leveraged to understand and forecast demand, predict and mitigate unplanned interruptions to power supply, and efficiently restore power when needed. Neudesic, a Systems Integrator, and DTE Energy, a large electric and natural gas utility serving 2.2 million customers in southeast Michigan, partnered to use large IoT datasets to identify the sources and causes of reliability issues across DTE’s power distribution network. In this session, we will demonstrate how we ingest hundreds of millions of quality measures each day from DTE’s network of smart electric meters. This data is then further processed in Databricks to detect anomalies, apply graph analytics and spatially cluster these anomalies into “hot spots”. Engineers and Work Management Experts use a dashboard to explore, plan and prioritize diverse actions to remediate the hot spots. This allows DTE to prioritize work orders and dispatch crews based on impact to grid reliability. Because of this and other efforts, DTE has improved reliability by 25% year over year. We will demonstrate our notebooks and machine learning models along with our dashboard. We will also discuss Spark streaming, Pandas UDF’s, anomaly detection and DBSCAN clustering. By the end of our presentation, you should understand our approach to infer hidden insights from our IoT data, and potentially apply similar techniques to your own data.

In this session watch:
Jim Brown, Director, Neudesic
Minjie Yu, Data Scientist, DTE Energy