Technological innovation can be a blessing and a curse. For example, the increased penetration of renewable energy has created new challenges for grid operators. At the same time, big data can provide insight to help address some of those challenges.
Along those lines, tech powerhouse General Electric (GE) recently announced a new suite of grid analytics designed to help utilities improve their operations.
The portfolio relies on a unique combination of domain expertise with artificial intelligence and machine learning. It uses data from across transmission and distribution networks to help achieve goals for operational efficiency.
The analytics address three important areas of operation. Storm readiness analytics are intended to accurately forecast storm impact and prepare response crews and equipment ahead of impending weather. It utilizes high-resolution weather forecasts, outage history, crew response and geographic information system (GIS) data as the basis of its forecasts.
Network connectivity analytics correct and maintain network data integrity. GE’s Network Connectivity algorithms use GIS and other operational system data to detect, recommend and correct pervasive errors that often result from manual input of information.
Effective inertia analytics give enhanced visibility into transmission system operations to address issues of “system inertia” resulting from the influx of renewable energy generation. GE’s Effective Inertia analytics use machine learning to facilitate the measurement and forecasting of system inertia to enable a more stable grid.
Noting that the energy industry today only leverages “a small fraction of its operational data,” acting CEO for GE Digital and chief digital officer for GE Power, Steven Martin, said the new grid analytics will enable utilities to harness more of their data and adjust their operations “in ways previously unimagined.”
The new grid analytics are connected via GE’s common Digital Energy data fabric allowing data to be unified on a secure, scalable and user-friendly platform.