The website you're reading now gives concrete examples of how to do so. How much energy stuff uses How to measure electrical use Mr. Why is my bill so high? Generating electricity with a bicycle You can't generate a meaningful amount of electricity with a bicycle, and it won't save any money, either, because bike power generates such a tiny amount of electricity versus the cost of the setup.
Google picks the ads, not me. General Electricity Myths Using elec. I don't endorse the advertisers. All Rights Reserved. Unauthorized reprinting is prohibited. All advice is given in good faith. 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 And it's just too long: By the time it's analyzed, the data is outdated.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. This is a huge help for utilities trying to maintain the power infrastructure. But AI isn't just good for analyzing images. These are very obtrusive on the landscape, and they're expensive to build and to maintain, but they do produce large volumes of clean energy.
It's easy to argue that these two types of energy production are mutually exclusive with relatively little in common. And whilst it's true you can't put wind turbines in the average surburban garden, you can, as a home or business owner, benefit from both large and small-scale energy generation. In order to decarbonise the global economy, we must generate more clean energy. There's not enough to keep up with global demand, so why not use microgeneration more effectively, more regularly, to help support these much bigger energy projects.
It's a power generating exercise bike for homes, gyms, and businesses. You can't power an entire house from a single bike, that's true, but if you're drawing power from the entire fitness community - those exercising at home and inside gyms - then that power adds up. The UK has 10 million gym members. Imagine tapping into all those workouts and then capturing and converting that energy and turning it into useable electricity.
It's the benefit of microgeneration: exploiting a form of energy generation that's already happening. Indoor bikes fitted with Energym's technology allows for power generation without the associated economic, environmental, and political impacts of larger scale projects like nuclear plants, hydroelectric dams, and solar farms, etc. Having access to clean, free energy will enable poverty-stricken communities to not only light their homes but to connect to the internet and get educated.
Bhargava says the reason the majority of those who are poor stay poor is because they have no power. He aims to fix this with the free energy bicycle. One bicycle could potentially provide a small village with electricity if each household spends on hour per day pedaling the bike.
In developed nations, the bike could also be utilized to cut energy costs and remedy the obesity crisis. The Free Electric Bike. Credit: NationalGeographic. The bicycle is also a clean way to generate power.
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