In remote areas, helicopters carry inspectors with cameras with optical zooms that let them inspect power lines from a distance. These long-range inspections can cover more ground but can't really replace a closer look. Recently, power utilities have started using drones to capture more information more frequently about their power lines and infrastructure.
In addition to zoom lenses, some are adding thermal sensors and lidar onto the drones. Thermal sensors pick up excess heat from electrical components like insulators, conductors, and transformers.
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 And it's just too long: By the time it's analyzed, the data is outdated.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. In this instance, he was less than accurate! He compiled a number of quotes from the time period before and after Strauss' speech; none indicated anything but a rational technical approach to the economics of nuclear power.
Morgan's review provided abundant evidence that few people in the industry at the end of s really believed that nuclear power would be very cheap. Strauss did not refer to nuclear energy in his speech.
Some argued that he was talking about energy from fusion rather than fission. He also thought that Strauss was talking about "fusion" because Strauss knew that fission would probably be more expensive than coal. Anderson clarified that "too cheap to meter" didn't mean free -- it just meant too cheap to monitor closely. He noted that some buildings built around that time, including the World Trade Center, were designed without light switches in each office; the building managers could just turn whole floors on and off , like a Christmas tree.
Is there anything too cheap to meter? Nicholas Carr of roughtype. The credit card company refused to process the bill! Google searches with the names of well known antinuclear critics, Praful Bidwai, M. If the demand is low enough so that fossil fuel plants can be shut down, it is possible to even reduce the labor component of the cost, since a skeleton crew can keep the plant in a condition where it can be restarted once the demand returns.
The time scale needed for power restoration varies depending on the type of plant used and can vary from minutes for simple cycle gas turbines to hours or days for coal fired steam plants. In contrast, nuclear plants have fuel costs that are low enough to disappear into insignificance. They also have permanent crews that do not get much smaller when the plant is not running. In fact, it is often more expensive to maintain a nuclear plant in a shutdown than it is to operate it.
Because of those characteristics, measuring the actual use for each customer is not required; it would be more cost effective and fair if customers were charged a flat fee based on the amount of power that they wanted to have available at any one time. This capacity charge would be more like a cable bill or a local phone bill. The utility would know how much capacity it needed to have on hand and could invest wisely to ensure that it could meet its obligations and it would save money in its billing systems.
There would be no need to install, monitor and repair meters; though there would be a need for devices that prevented more than the agreed upon power from flowing through the lines in order to make sure that would be cheaters did not sign up for less capacity than they really needed. Customers would gain predictability of a regular bill and not be surprised by the kinds of seasonal variation that is becoming more and more troublesome. They might even begin to understand that running the washer, dryer, oven, microwave, toaster oven, blender, big screen television, and three hair dryers at the same time in the evening might be a bad idea.
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