Sentient Energy, Inc. has released its Ample 3.0 Analytics platform. When paired with Sentient Energy’s family of intelligent line sensors, the Ample Analytics platform provides everything needed to manage and analyze the immense amount of data captured by Sentient MM3™ sensors when deployed on the distribution grid. Ample can also serve as the data integration platform for SCADA and DMS.
MM3 line sensors feature Sentient Energy’s proprietary waveform analysis technology that captures high-resolution oscillography associated with faults and network events anywhere on the distribution grid and wirelessly transmits key data or the entire waveform, as needed, to the Ample Analytics platform for further analysis. Once this waveform data is collected, catalogued, and analyzed, it is possible to identify an event and its immediate cause -- such as vegetation, lightning, or animal intrusion. It is also possible to identify many underlying conditions that lead to failure, such as deteriorating connections or faulty equipment.
“The Ample Analytics platform is a vital component of our comprehensive Grid Analytics System. Together, Sentient Energy intelligent sensors and Ample Analytics platform hold the key to accurately predicting future grid equipment failures and preventing outages before they occur,” said Sentient Energy CEO, Jim Keener. “We are confident this technology will lead to a significantly more resilient grid and a major reduction in service interruptions.”
Ample Analytics 3.0 has been deployed by Florida Power & Light (FPL) as part of the utility’s 20,000 MM3 sensor project covering its entire 42,000-mile overhead service territory. This is believed to be the largest distribution grid sensor deployment in the world. FPL will use Sentient Energy’s Grid Analytics System – MM3 sensors, high-resolution oscillography, and the Ample Analytics platform – to detect minute disturbances on the grid and use this information to isolate faults, detect defective equipment before it fails, and analyze the unique patterns of these events to predict the likelihood of future outages.