TRANSENSOR (Transformer Real-time Assessment INtelligent System with Embedded Network of Sensors and Optical Readout) will increase reliability and safety by providing real time asset health.
As part of the Energy Department’s commitment to a reliable and resilient power grid, the OE is investing nearly $10 million in early-stage research to help utilities inform decisions about increased deployment of DERs such as solar photovoltaics, plug-in electric vehicles, combustion engines, and energy storage systems on the grid.
“It’s been predicted that the smart grid sensor market will reach $39 billion by 2019,” said Ajay Raghavan, PARC research area manager and principal investigator leading the effort. “Clearly, the sensor market in a myriad of industries will grow rapidly, given the IoT explosion. We are working on innovative low-cost embedded fiber-optic sensors that can reliably monitor conditions in a broad variety of harsh environments, including those seen in smart grids, so that we can understand the real-time state of critical systems. This work with the DOE OE is an exciting new application, and we think we can help gain a greater understanding of and better manage the electric grid that serves us all as it increasingly integrates distributed resources. It’s very important work at a very important time.”
Con Edison, the energy company that serves 3.4 million customers in New York City and Westchester County, NY, has one of the highest load densities in the world. The company provides industry-leading reliable electric service via a complex, expansive grid. Con Edison personnel will lead the final field demonstration of TRANSENSOR.
GE Power’s Grid Solutions business helps enable utilities to effectively manage electricity from generation to consumption, helping maximize grid efficiency and resiliency in 80 countries. GE personnel will play a key support role in the testing for proving/qualifying TRANSENSOR on its Safe-NET network transformers leading to the planned field demonstration with Con Edison.
The project utilizes PARC’s low-cost, high-resolution, multiplexed compact wavelength-shift detection technology, along with its machine learning algorithms to exploit the sensors for effective asset monitoring, enabling utilities to predict future failures.