A Proactive Approach to Power Grid Asset Maintenance with IIoT System Analytics

---

GE Grid Solutions and Con Edison

---

Overview

Utilities across the world are wrestling with evolving market dynamics, population growth, and climate change. Distributed Energy Resources (DERs) such as solar photovoltaics, distributed generators, and energy storage systems are becoming important parts of the U.S. energy mix, as they offer important benefits such as energy savings, reduced system losses, and improved distribution system resilience. As part of the U.S. Department of Energy/Office of Electricity’s Grid Modernization Initiative, GE Grid Solutions and Con Edison partnered with the Future Concepts Division at SRI (previously PARC) to assess and deploy our predictive, condition-based maintenance TRANSENSOR technology (now referred to as MOXI™ IIoT System Analytics) and help improve the maintenance and management of their grid infrastructure with the integration of DERs.

Objective

Improve power system maintenance and management to accommodate changing grid dynamics.
Con Edison has one of the highest load densities in the world, servicing 3.54 million customers in New York City and Westchester County, New York. Aside from being costly, traditional utility monitoring systems were not sufficiently robust and did not provide real-time visibility into the condition of transformers or accurate measurements of performance. This resulted in the use of lagging indicators, such as oil sample analysis. Additionally, these systems did not provide a systematic method to reliably predict incipient transformer core- and coil-driven failures. Con Edison and its major asset vendor, GE Grid Solutions, were interested in a more proactive, cost-effective, and reliable approach to managing transformers and other grid assets.

Why us?

MOXI IIoT System Analytics suite
With a proven track record of helping organizations manage and improve the health and reliability of critical systems across various sectors, we were the ideal fit. Unlike traditional maintenance practices, which can be costly, have limited accuracy, and lead to downtime, our MOXI technology suite allows for continuous and remote monitoring of the system state. It also employs low-cost embedded sensors and model-based algorithms that enable greater than 90% accuracy with negligible false alarm rates and near-zero missed detections.

Solution

Low-cost MOXI fiber-optic sensors and model-based system analytics
We initiated a phase 1, laboratory-based validation of their MOXI technology suite, which included low-cost fiber-optic (FO) sensors and model-based system analytics for the continuous monitoring of GE network transformers. MOXI FO sensors were embedded in critical components within the latest GE “SAFENET” model, as well as other existing GE model network transformers, where they measured key internal parameters and monitored key events of interest. The MOXI system then provided a high-resolution, high-frequency optical readout. The performance of the MOXI FO sensors was validated against other commercially available sensors.

Results

High sensitivity; Cost-effective and industry-standard qualified
Commercial GE network transformers with the embedded MOXI fiber-optic sensing solution were successfully built, tested, and qualified per industry standards. The parameters monitored by MOXI’s analytics suite were well-validated across a range of scenarios at GE’s testing facilities (located in Shreveport, LA) with lab instruments typically used for qualification testing. Additionally, a version of the technology that could be retrofitted into currently deployed/older network transformers was developed and lab-tested. 

With GE and Con Edison, we have initiated a phase 2 field trial that would demonstrate the robustness of the technology for remote monitoring through the IIoT under Con Edison’s demanding grid deployment conditions in New York City, and show the path for cost-effective scale-up of the MOXI solution towards truly smart grids.

A full-scale implementation of the MOXI solution has the potential to enable cost-effective, remote monitoring of not only network distribution transformers, but also other grid assets, where existing monitoring systems are too costly or limited. The MOXI system could provide systematic alerts for unsafe/unexpected events. Utility professionals could view trends in asset conditions to better plan for maintenance/management, and improve grid performance, which would in turn accommodate DERs and other evolving grid market dynamics.