Who is beacon power

Beacon restarted manufacturing at its Tyngsboro, Massachusetts facility in December , and reports that it is again making flywheels. The company began work on the Pennsylvania energy storage plant in Hazle Township in December Lead image: Stacks of coins via Shutterstock. Power Engineering. Solar Energy. Texas adds battery storage to support grid ahead of winter. Microgrid lab opens at University of Central Florida.

If ignored, these electrical components can spark or, even worse, explode. Lidar can help with vegetation management, scanning the area around a line and gathering data that software later uses to create a 3-D model of the area. The model allows power system managers to determine the exact distance of vegetation from power lines.

That's important because when tree branches come too close to power lines they can cause shorting or catch a spark from other malfunctioning electrical components. AI-based algorithms can spot areas in which vegetation encroaches on power lines, processing tens of thousands of aerial images in days.

Buzz Solutions. Bringing any technology into the mix that allows more frequent and better inspections is good news. And it means that, using state-of-the-art as well as traditional monitoring tools, major utilities are now capturing more than a million images of their grid infrastructure and the environment around it every year. AI isn't just good for analyzing images. It can predict the future by looking at patterns in data over time. Now for the bad news. When all this visual data comes back to the utility data centers, field technicians, engineers, and linemen spend months analyzing it—as much as six to eight months per inspection cycle.

That takes them away from their jobs of doing maintenance in the field. And it's just too long: By the time it's analyzed, the data is outdated. It's time for AI to step in.

And it has begun to do so. AI and machine learning have begun to be deployed to detect faults and breakages in power lines. Multiple power utilities, including Xcel Energy and Florida Power and Light , are testing AI to detect problems with electrical components on both high- and low-voltage power lines. These power utilities are ramping up their drone inspection programs to increase the amount of data they collect optical, thermal, and lidar , with the expectation that AI can make this data more immediately useful.

My organization, Buzz Solutions , is one of the companies providing these kinds of AI tools for the power industry today. But we want to do more than detect problems that have already occurred—we want to predict them before they happen.

Imagine what a power company could do if it knew the location of equipment heading towards failure, allowing crews to get in and take preemptive maintenance measures, before a spark creates the next massive wildfire. It's time to ask if an AI can be the modern version of the old Smokey Bear mascot of the United States Forest Service: preventing wildfires before they happen.

Damage to power line equipment due to overheating, corrosion, or other issues can spark a fire. We started to build our systems using data gathered by government agencies, nonprofits like the Electrical Power Research Institute EPRI , power utilities, and aerial inspection service providers that offer helicopter and drone surveillance for hire. Put together, this data set comprises thousands of images of electrical components on power lines, including insulators, conductors, connectors, hardware, poles, and towers.

It also includes collections of images of damaged components, like broken insulators, corroded connectors, damaged conductors, rusted hardware structures, and cracked poles. We worked with EPRI and power utilities to create guidelines and a taxonomy for labeling the image data.

For instance, what exactly does a broken insulator or corroded connector look like? What does a good insulator look like? We then had to unify the disparate data, the images taken from the air and from the ground using different kinds of camera sensors operating at different angles and resolutions and taken under a variety of lighting conditions.

We increased the contrast and brightness of some images to try to bring them into a cohesive range, we standardized image resolutions, and we created sets of images of the same object taken from different angles. We also had to tune our algorithms to focus on the object of interest in each image, like an insulator, rather than consider the entire image.

We used machine learning algorithms running on an artificial neural network for most of these adjustments. Today, our AI algorithms can recognize damage or faults involving insulators, connectors, dampers, poles, cross-arms, and other structures, and highlight the problem areas for in-person maintenance. For instance, it can detect what we call flashed-over insulators—damage due to overheating caused by excessive electrical discharge.

It can also spot the fraying of conductors something also caused by overheated lines , corroded connectors, damage to wooden poles and crossarms, and many more issues. Developing algorithms for analyzing power system equipment required determining what exactly damaged components look like from a variety of angles under disparate lighting conditions.

Here, the software flags problems with equipment used to reduce vibration caused by winds. But one of the most important issues, especially in California, is for our AI to recognize where and when vegetation is growing too close to high-voltage power lines, particularly in combination with faulty components, a dangerous combination in fire country.

Today, our system can go through tens of thousands of images and spot issues in a matter of hours and days, compared with months for manual analysis. NYISO requires a ramp rate of 20 MW within 6 seconds, although the plant can respond faster, with no limits on degradation due to cycle, duty, depth of discharge, charging rate, ambient temperature, and so on. To build the Stephentown facility, in , Beacon Power received a U.

When Beacon Power Corp. On Feb. That plant, configured like the Stephentown Plant, will place the first 4 MW into commercial service in September and the remainder by the second quarter of Brits shared that in the past, the company struggled to earn revenue when there was no market tariff in place that placed a monetary value on regulation services, particularly fast-response regulation.

Today, tariff changes are in place in several ISO regions that will pay for regulation services. Brits expects the remaining ISO markets to follow suit in time and develop attractive tariff structures.

With an established tariff, Beacon can build plants and earn a return on its investment. In the long term, the company will pursue global opportunities where fast-responding grid regulation services have a prescribed market value, particularly in islanding applications and in regions with high power prices and a high percentage of renewables. Not surprisingly, Brits was in Germany exploring market opportunities when we made contact by phone. With new tariffs for these services now available, the uncertainty that made investors reluctant to provide financing in the past has been removed.

Brits suggests that there is ample money available from energy private equity or from hedge funds to construct new projects without difficulty. Energy storage developments got a boost as Beacon Power Corp. Energy storage in the U. The grid is unique…. View more. Facebook Twitter LinkedIn. Defense Daily subscriber and registered users, please log in here to access the content.