GE has announced that it has made available three new grid analytics that combine domain expertise with artificial intelligence (AI) and machine learning (ML) for electric grid operations. The analytics use data from across transmission and distribution networks to help achieve goal for operational efficiency. The portfolio includes:
Storm Readiness. This utilises high-resolution weather forecasts, outage history, crew response, and geographic information system (GIS) data to accurately forecast storm impact and prepare response crews and equipment ahead of impending weather. GE’s Storm Readiness analytic helps reduce outage restoration time, predict future outages, reduce operational spend, and improve crew safety.
Network Connectivity. This corrects and maintains network data integrity. Data errors, which often arise from manual input of information at the customer or equipment level, can hinder emergency and outage response and lead to poor customer experience. GE’s Network Connectivity algorithms use GIS and other system operational data to detect, recommend, and correct pervasive errors. With better data, utilities can more efficiently dispatch crews, reduce outage restoration time, and avoid incorrect outage notifications to customers.
Effective Inertia. This gives enhanced visibility into transmission system operations. The operation of transmission networks is continuing to grow in complexity, in large part due to the influx of renewable generation. This has led to a massive displacement of “system inertia”, or the resiliency of power generation, given spikes in customer demand or reduced supply due to fluctuations in wind or sunlight. Ineffective management of a transmission system could result in blackouts and major financial and reputational penalties. GE’s Effective Inertia analytic uses ML to facilitate the measurement and forecasting of system inertia and enable a more stable grid.
The new grid analytics are connected via GE’s common Digital Energy data fabric. Unifying data on a secure, scalable, and user-friendly platform drives efficiencies, allowing data stored in one location to be utilised by many solutions across the energy value chain.