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. This is a huge help for utilities trying to maintain the power infrastructure. But AI isn't just good for analyzing images. AI already does that to predict weather conditions , the growth of companies , and the likelihood of onset of diseases , to name just a few examples.
We believe that AI will be able to provide similar predictive tools for power utilities, anticipating faults, and flagging areas where these faults could potentially cause wildfires. We are developing a system to do so in cooperation with industry and utility partners.
We are using historical data from power line inspections combined with historical weather conditions for the relevant region and feeding it to our machine learning systems. We are asking our machine learning systems to find patterns relating to broken or damaged components, healthy components, and overgrown vegetation around lines, along with the weather conditions related to all of these, and to use the patterns to predict the future health of the power line or electrical components and vegetation growth around them.
Buzz Solutions' PowerAI software analyzes images of the power infrastructure to spot current problems and predict future ones. Right now, our algorithms can predict six months into the future that, for example, there is a likelihood of five insulators getting damaged in a specific area, along with a high likelihood of vegetation overgrowth near the line at that time, that combined create a fire risk.
We are now using this predictive fault detection system in pilot programs with several major utilities—one in New York, one in the New England region, and one in Canada.
Since we began our pilots in December of , we have analyzed about 3, electrical towers. We detected, among some 19, healthy electrical components, 5, faulty ones that could have led to power outages or sparking.
We do not have data on repairs or replacements made. Where do we go from here? To move beyond these pilots and deploy predictive AI more widely, we will need a huge amount of data, collected over time and across various geographies.
This requires working with multiple power companies, collaborating with their inspection, maintenance, and vegetation management teams. Major power utilities in the United States have the budgets and the resources to collect data at such a massive scale with drone and aviation-based inspection programs.
But smaller utilities are also becoming able to collect more data as the cost of drones drops. Making tools like ours broadly useful will require collaboration between the big and the small utilities, as well as the drone and sensor technology providers. Fast forward to October It's not hard to imagine the western U. S facing another hot, dry, and extremely dangerous fire season, during which a small spark could lead to a giant disaster.
People who live in fire country are taking care to avoid any activity that could start a fire. But these days, they are far less worried about the risks from their electric grid, because, months ago, utility workers came through, repairing and replacing faulty insulators, transformers, and other electrical components and trimming back trees, even those that had yet to reach power lines.
Some asked the workers why all the activity. But does Luther Forest really have the land for Intel? GlobalFoundries wants to build a second factory at Fab 8, but the company has been wary of a plan by a real estate developer called Scannell Properties that wants to build warehouses at Luther Forest — perhaps for a company like Amazon.
GlobalFoundries has opposed Scannell's plans, even sending its lobbyists to meet with town officials telling them the sale of additional land at Luther Forest for warehouses could threaten its plans for another fab. Peter Benyon, the general manager of Fab 8, wrote in an op-ed published by the Times Union in May that the Scannell project threatens GlobalFoundries' expansion and future plans at Luther Forest.
Could that include Intel? Although it is located four hours from Intel's research operations in Albany, STAMP is zoned for up to 6 million square feet of manufacturing space and has other qualities Intel is looking for in a site. Whether Intel is interested in the site has not been made public. Intel recently shared a developer guide for its forthcoming 12th Generation Alder Lake processors. Gamer's Gospel discovered an interesting tidbit that DRM solutions, such as Denuvo will require updates to support Alder Lake's hybrid design.
Gamers typically upgrade to a new processor to get better performance in games. Intel has confirmed in the document that Alder Lake, which is supposed to compete with the best CPUs for gaming , will have compatibility issues with DRM solutions unless the provider issues a special update for the protection in question.
Consequently, game developers that implemented the DRM into their games will have to do the same. Due to the nature of modern DRM algorithms, it might use CPU detection, and should be aware of the upcoming hybrid platforms.
Many, if not all, modern triple-A titles carry some type of DRM protection to defend against piracy. Intel specifically mentioned the Denuvo algorithm, but we suspect that other protections, such as VMProtect or SecuROM will likely necessitate an update as well. Certain games, like Assassin's Creed Origins even have multiple layers of protection, probably requiring multiple updates. It shouldn't be a huge issue for modern games since developers will in all likelihood provide the update for Alder Lake.
The problem arises for older titles that are a couple of years old that likely won't get any updates, meaning they'll be unplayable on Alder Lake chips. There are many gems out there that have high replay value so gamers will be annoyed that they can't play them on the shiny, new Alder Lake processor that they just bought.
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