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New research identifies vulnerabilities in EMP attacks

In July, Chinese researchers urged their government to strengthen the country's preparations against high-altitude electromagnetic pulse (EMP) attacks. Just over a year ago, a group of American researchers issued a report warning that China has the ability to carry out electromagnetic pulse attacks on the United States. Military and non-proliferation experts worry that nuclear-weapon states are increasingly inclined to use nuclear weapons for the first electromagnetic pulse attack. Although it avoids direct casualties, it may cause devastating damage to the power grid and electronic equipment from smart watches to supercomputers.

For some time, people have realized the huge potential of electromagnetic pulses released by high-altitude explosions of nuclear weapons. In 1962, the United States conducted an atmospheric test of a 1.45 megaton thermonuclear weapon code-named Starfish Prime 250 miles above Johnston Island in the Pacific Ocean. More than 1,000 miles away, the explosion disrupted power supplies in parts of Hawaii, and telephone service was interrupted for a period of time. In addition, the radiation from the test damaged several satellites in low-Earth orbit, making them out of service. Decades later, the committee to assess the threat of electromagnetic pulse (EMP) attacks to the United States as early as 2008 determined that the United States will face EMP attacks in view of the increasing dependence of the United States on various forms of electronic products and complete reliance on electromagnetic pulse (EMP) attacks Catastrophic consequences. Power grid.

However, so far, government and industry risk assessments regarding EMP attacks and their impact on the power grid have been based on an over-simplified solid earth model, assuming zero changes in depth or composition. However, it turns out that the actual impact of outer space electromagnetic pulses on the power grid depends largely on the three-dimensional distribution of rocks under our feet.

The landmark study is the product of a collaboration between the United States Geological Survey (USGS) and the University of Colorado and illustrates the role of the solid earth in determining the extent of any EMP hazard. In an interview, the lead author of the new report, US Geological Survey geophysicist Jeffrey J. Love explained that high-altitude EMP produces three continuous waveforms with different effects on electrical systems: E1, E2, and E3 .

High-frequency E1 pulses disrupt consumer electronics and are often the most concerned; E2 behaves more or less like lightning. Fortunately, our electrical system has (to a large extent) reinforced its effects. The E3 waveform is the part with the lowest amplitude in the EMP signal, but because it is the part with the longest duration, covering a period of time ranging from 0.1 seconds to hundreds of seconds, it may cause catastrophic damage to the power grid through its interaction. Solid earth. (See below.)

A nuclear explosion in the upper atmosphere or space (off the screen) will push the flow of ions and electrons through the atmosphere (B), generating a magnetic field (A), and then inducing currents and electric fields in the earth below it. High conductivity (D) The area of ​​?? is easier to carry the current, while the area of ​​low conductivity (F) is not. On the contrary, the larger electric fields in these areas (E) can help draw current from the ground and through the wires of the large-capacity power line (C), thereby generating surges that are potentially damaging to the power grid caused by the explosive EMP. John McNeill

Love pointed out that three factors together constitute the geoelectric hazard of the power grid: "The level of EMP magnetic disturbance, the electrical conductivity of the surrounding earth, and the specific parameters of the power grid itself." This new study uses existing survey data-initially Collected for geological prospecting-a small area in the eastern and central United States, covering parts of Arkansas, Missouri, Illinois, Mississippi, Kentucky, and Tennessee. Researchers from the US Geological Survey then obtained permission from the owner to place the sensor on the ground to measure the natural changes in the earth's magnetic field over a few weeks. They also used a voltmeter to measure the electric field at the same location over time. These two measurement types provide an estimate of the surface impedance-an electromagnetic characteristic that depends on the electrical conductivity of the rock.

Love and his co-authors used these survey data to assess the impact of the E3 EMP waveform generated by a high-altitude explosion of a nuclear weapon with a yield of hundreds of kilotons—a benchmark description of EMP events in the literature. They also include USGS research on natural magnetic storm disturbances in the continental United States-resistive metamorphic and igneous rocks in northern Minnesota or eastern Appalachian mountains, and conductive sedimentary rocks in Michigan or Illinois.

All in all, they found that EMP hazards have not been accurately mapped in complex geological environments. They called on the U.S. Geological Survey to analyze the surface impedance in areas such as the eastern mid-continent. They added that the U.S. Geological Survey should pay special attention to the eastern United States, where the damage from magnetic storms is already high. Of course, this is also the location of many of the country's largest cities.

"Better coordination across disciplines is necessary," Love said. "By bringing together weapon engineers, space scientists, and geophysicists, we can implement a holistic approach to EMP threat assessment and prioritize improvements accordingly."

This article appeared in the October 2021 print edition under the title "An atmospheric nuclear explosion could paralyze the entire power grid."

EMP problems are far more insidious than those caused by nuclear EMP. A nuclear bomb is not a necessary condition for major damage to the grid infrastructure. No control electronics can significantly strengthen any type of EMP/IEMI. Controls are usually only tested for radiant sensitivity of 20 V/m. The processor core voltage is dropping to a very low voltage (for example, 3.3V, 1.2V). And the control line is exposed. People don’t have to over-current transformers or burn wires to dismantle the grid. If the control electronics fails, the grid fails. A 50kV/m high-power pulse generator is easy to construct. Although the field strength decreases by the square of the radius E=(kQ)/(r^2), the use of a high-gain horn antenna to focus may keep the bad guys away from the target system. If control is disturbed by IEME, it may theoretically cause a single backup turbine to synchronize out of phase and then come back online. We expect branch OCP devices to trip, but if they are affected by conducted IEMI (induced by radiation) interference, they may not trip. This may cause the downstream OCP device to turn on. Sudden load loss can cause other turbines to overspeed, consume too much current, and then trip more OCP devices, eventually leading to cascading failures. Some simultaneous targeted attacks can disable all ERCOT. Look at what happened to the ice/snow storm. This is a known danger because it happened once before the 2021 event. The grid infrastructure in Michigan has deteriorated so much that simple problems can cause cascading failures. In its 2018 report, the American Society of Civil Engineers rated Michigan's infrastructure as D+ and the energy grid as C-.

So why would a country risk exploding nuclear weapons to destroy the power grid when retaliatory strikes turn the power grid into smoking and poisonous ruins? Isn’t our national defense policy for the past 60 years a mutual guarantee of destruction (MAD)? Or you are claiming that our land-based nuclear strike capability will be banned by EMP. I thought we solved this problem 50 years ago.

Natasha, can I get permission to reprint your article in the Austin Gem & Mineral Society (AGMS) newsletter? AGMS is another 501(c)3 organization, a group of rock collectors and gem lovers. This is just the type of information that most non-technical members would find of interest. James J. Mercier, PE

Intelligent image analysis algorithms provided by cameras carried by drones and ground vehicles can help power companies prevent forest fires

The Dixie fire in northern California in 2021 is suspected to be caused by Pacific Gas & Electric equipment. This is the second largest fire in California history.

The 2020 fire season in the United States is the worst in at least 70 years, with approximately 4 million hectares of land burned on the west coast alone. These West Coast fires killed at least 37 people, destroyed hundreds of buildings, caused nearly 20 billion U.S. dollars in damage, and filled the air with smoke that threatens the health of millions of people. This was done on the basis of the fire season in California burning more than 700,000 hectares of land in 2018, and the wildfire season in Australia from 2019 to 2020 that burned nearly 18 million hectares of land.

Although some of these fires are caused by human negligence or arson, too many fires are triggered and spread by power infrastructure and transmission lines. The California Forestry and Fire Department (Cal Fire) calculated that nearly 100,000 hectares of land burned in the California fire in 2018 was a problem with power infrastructure, including the devastating campfire, which destroyed most of the town of Paradise. In July of this year, Pacific Gas & Electric stated that a blown fuse on one of its utility poles may have caused the Dixie fire, which destroyed nearly 400,000 hectares of land.

Before these recent disasters, most people, even those living in vulnerable areas, did not think too much about the fire risk of power infrastructure. The power company regularly (if not particularly frequently) trims the trees and checks the wiring.

However, the frequency of these inspections has hardly changed over the years, although climate change has led to drier and hotter weather conditions leading to more intense wildfires. In addition, many key electrical components have exceeded their shelf life, including insulators, transformers, arresters and connectors that have been in use for more than 40 years. Many transmission towers (most of which have a construction life of 40 years) are entering the final decade.

There has also been little change in the way inspections are conducted.

Historically, it has always been the responsibility of frontline personnel to check the status of the power infrastructure. If you are lucky and have access, line workers will use bucket trucks. However, when the electrical structure is located in a backyard easement, on the side of a mountain, or out of the reach of a mechanical elevator, line workers must still tie their tools and start climbing. In remote areas, helicopters carry inspectors with optical zoom cameras, allowing them to inspect power lines from a distance. These remote inspections can cover more ground, but they cannot really replace careful observation.

Recently, power companies have started using drones to capture more information about their power lines and infrastructure more frequently. In addition to the zoom lens, some drones also add thermal sensors and lidar to the drone.

Thermal sensors absorb excess heat from electrical components such as insulators, conductors, and transformers. If ignored, these electronic components may generate sparks, or even worse, explode. Lidar can help with vegetation management, scan the area around the line, and collect data, which the software will later use to create a 3D model of the area. This model allows power system managers to determine the exact distance between vegetation and power lines. This is important because when tree branches are too close to the power cord, they can cause a short circuit or spark from other malfunctioning electrical components.

Algorithms based on artificial intelligence can find areas where vegetation has invaded power lines and process tens of thousands of aerial images within a few days. Buzz Solutions

Combining any technology that allows more frequent and better inspections is good news. This means that, using the most advanced and traditional monitoring tools, major utility companies now capture more than 1 million images of the power grid infrastructure and its surroundings every year.

Artificial intelligence is not only suitable for analyzing images. It can predict the future by looking at the pattern of data changes over time.

Now is the bad news. When all this visualized data is returned to the utility data center, it takes months for field technicians, engineers, and line workers to analyze it—up to six to eight months per inspection cycle. This prevents them from performing maintenance work on site. And it's too long: by the time of analysis, the data is out of date.

Now is the time for artificial intelligence to step in. It has already begun to do so. Artificial intelligence and machine learning have begun to be used to detect faults and breakages in power lines.

Several power companies, including Xcel Energy and Florida Power and Light, are testing artificial intelligence to detect electrical component problems on high- and low-voltage power lines. These power companies are enhancing their drone inspection procedures to increase the amount of data they collect (optical, thermal, and lidar), and expect artificial intelligence to make these data more immediately useful.

My organization, Buzz Solutions, is one of the companies that provide such AI tools to the power industry today. But we want to do more than just detect problems that have already occurred-we want to predict them before they happen. Imagine what the power company would do if it knew the location of the equipment that was about to fail, allowed staff to enter and took preemptive maintenance measures, before the spark triggered the next large-scale wildfire.

It's time to ask if artificial intelligence can be a modern version of the old smoke bear mascot of the US Forest Service: prevent wildfires before they happen.

Damage to the power line equipment due to overheating, corrosion or other problems may cause a fire. Buzz solution

We began to build our system using data collected by government agencies, non-profit organizations such as the Electric Power Research Institute (EPRI), power companies, and aerial inspection service providers that provide helicopter and drone surveillance rental services. Put together, this dataset contains thousands of images of electrical components on power lines, including insulators, conductors, connectors, hardware, poles, and towers. It also includes a collection of images of damaged components, such as damaged insulators, corroded connectors, damaged conductors, rusty hardware structures, and broken utility poles.

We worked with EPRI and power companies to create guidelines and taxonomy for labeling image data. For example, what does a damaged insulator or corroded connector look like? What does a good insulator look like?

Then, we must unify the different data, even with images taken from the air and the ground with different types of camera sensors running at different angles and resolutions and shooting under various lighting conditions. We increased the contrast and brightness of some images, trying to put them in a cohesive range, we standardized the image resolution, and created a set of images of the same object taken from different angles. We must also adjust our algorithm to focus on the objects of interest in each image, such as insulators, instead of considering the entire image. We use machine learning algorithms running on artificial neural networks to make most of the adjustments.

Today, our AI algorithm can identify damage or failures involving insulators, connectors, dampers, rods, cross arms, and other structures, and highlight problem areas for on-site maintenance. For example, it can detect what we call flashover insulators-damage caused by overheating caused by excessive discharge. It can also find conductor wear (also caused by overheating of the wiring), connector corrosion, damage to wooden poles and cross arms, and more.

The development of algorithms for analyzing power system equipment needs to determine what exactly damaged components look like from all angles under different lighting conditions. Here, the software flags problems with equipment used to reduce wind-induced vibration. Buzz Solutions

But one of the most important issues, especially in California, is that our AI recognizes when and where vegetation grows too close to high-voltage power lines, especially when combined with faulty components, which is a dangerous situation in fire countries. combination.

Today, our system can process tens of thousands of images and find problems in a few hours and days, while manual analysis can take months. This is a huge help for utility companies trying to maintain power infrastructure.

But artificial intelligence is not just for analyzing images. It can predict the future by looking at the pattern of data changes over time. Artificial intelligence has already done this to predict weather conditions, company development, and the likelihood of disease outbreaks, just to name a few.

We believe that artificial intelligence will be able to provide power companies with similar predictive tools, predict failures, and mark areas where these failures may cause wildfires. We are working with industry and utility partners to develop a system.

We are using historical data from power line inspections and historical weather conditions in related areas and providing them to our machine learning system. We require our machine learning system to find patterns related to broken or damaged components, healthy components, and overgrown vegetation around the line, as well as weather conditions related to all of these, and use these patterns to predict the future health of electricity Wiring or electrical components and vegetation growth around them.

Buzz Solutions’ PowerAI software analyzes images of power infrastructure to find current problems and predict future problems

Now, our algorithm can predict the situation in the next 6 months. For example, there may be five insulators damaged in a specific area, and the vegetation near the line at that time is likely to overgrow. Combined, there is a risk of fire.

We are now using this predictive failure detection system in pilot projects of several major utility companies-one in New York, one in New England, and one in Canada. Since the pilot started in December 2019, we have analyzed approximately 3,500 electrical towers. We detected 5,500 faulty components that could cause power outages or sparks among approximately 19,000 healthy electrical components. (We have no data on repairs or replacements.)

Where do we go from here? In order to go beyond these pilots and deploy predictive artificial intelligence more widely, we will need a lot of data, which is collected in different regions over time. This requires cooperation with multiple power companies and their inspection, maintenance and vegetation management teams. Major US power companies have the budget and resources to collect data on such a large scale through drones and aviation-based inspection programs. But as the cost of drones drops, small utility companies can also collect more data. Making tools like ours widely available requires cooperation between large and small utility companies and drone and sensor technology providers.

Fast forward to October 2025. It is not difficult to imagine that the western United States will face another hot, dry and extremely dangerous fire season, during which a small spark may cause a huge disaster. People living in fire countries should take care to avoid any activities that may cause a fire. But now, they are much less worried about grid risks, because a few months ago, utility workers came to repair and replace faulty insulators, transformers and other electrical components, and trim trees, even those that have not yet reached the power line . Some people asked the workers why all the activities. "Oh," they were told, "Our artificial intelligence system indicates that the transformer next to this tree may produce sparks in the fall. We don't want this to happen."