Grid component inspections are 10x trending to 100x, but image assessment has been manual. Enter Buzz Solutions.
, a company that provides a platform to utilities for analyzing, storing, and managing the increasing load of high-resolution inspection images of transmission and distribution assets., studied international relations, finance, and business at Stanford, a ran a non-profit around sustainable food with 60 people overseeing the needs of 300 people. Albertoli is from San Clemente, a beach town in southern California, and the San Onofre nuclear power plant was there.
But the process of assessing the data was manual, with trained linespeople and engineers looking at their computers instead of fixing problems. That was a machine learning opportunity. They started trying to assist utilities to identify hotspots and failure spots that could lead to significant outages. And that was before the 2017 wildfire season that damaged so many lines in California. They were there, they were ready and they were able.
It isn’t possible to train 10x the people to deal with 10x the photos. Utilities were struggling with the problem. They were trying to hire engineers and linespeople to analyze the data, but the lag time of imaging to analysis was increasing. That meant an increase in risk, as degrading components saw more degradation in the meantime. Utilities became increasingly interested in solutions to manage and analyze the masses of data.
Historically, line inspections were done with helicopters or walking the lines with binoculars, and it was only a fourth or a third of the lines a year. There are also on-demand inspections when there are major storm risks, storm damage assessments, high winds, or risks of wildfires. Utilities identify hotspot areas prone to higher risk or where critical failures could occur, and those areas are inspected more frequently. There are both high-level flybys and more granular inspections.
The next era of inspections that’s emerging is neural net chips on drones, initially for automated flight around towers, identification of salient components and image capture, and eventually immediate fault detection. Thedrone, as an example, already uses an ML chip for its autonomous flying and imaging. With FAA regulations loosening and more companies getting approval for beyond-line-of-sight operations, there’s a fair amount of work in this space.
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