Revolutionizing Energy Grid Maintenance: How Artificial Intelligence Is Transforming the Future
Using the latest in artificial intelligence (AI) technology, Argonne researchers developed AI-enabled software that could predict when grid components would fail. The system analyzes vast amounts of information energy companies collect from sensors installed throughout the grid, creating a predictive model that forecasts wear and tear over time. Ultimately, the software could recommend when to repair or replace parts before any problems occur.
“Companies want to know the health of their assets,” said Feng Qiu, head of the Advanced Grid Modeling group at Argonne, who led this research. “Our prognostic models that leverage condition-monitoring information can tell them the useful remaining time of their equipment — how many years, months and weeks it has left.”
Shijia Zhao, an energy systems scientist at Argonne who played a crucial role in the research, explains that their approach goes beyond traditional reactive maintenance strategies. “Instead of waiting for equipment to break down, we use AI to proactively identify potential issues and schedule maintenance just-in-time, saving both time and money for energy companies.”
At the heart of this innovative approach is the ability to estimate infrastructure and asset health, predict failure risks and adapt maintenance decisions based on current real-world data. By transitioning from lab models to data collected from the field, Argonne researchers have shown how useful this technology can be to energy providers. In one project on solar inverters, the team showed that it could potentially reduce total maintenance costs by 43-56%, unnecessary crew visits by 60-66% and increase profit by 3-4%.
“Our goal is to equip energy providers with the tools they need to ensure a reliable and resilient grid for years to come,” said Qiu. “With this technology, companies can make informed decisions about when and how to repair or replace equipment, ultimately enhancing the overall efficiency, security and reliability of America’s energy infrastructure.”
The benefits of this research extend far beyond cost savings and efficiency gains. By minimizing downtime and addressing maintenance issues before they escalate, energy providers can enhance grid reliability and resilience, crucial factors in an era of increasing energy demand and an evolving energy landscape.
The power and scale of the AI-enabled prediction and optimization models means they can optimize maintenance at a grid level. “This is critical to keep your lights on,” said Qiu.
By looking holistically at the electric grid — from power plants to power lines — the models can predict failures in the entire network that produces and transports electricity from where it is generated to where it is consumed. In the U.S., there are more than 240,000 high-voltage transmission lines and 50 million transformers. Most of the large and expensive transformers are near the end of their lifespan. About 70% have been in service for 25 years or more. Increasing load and volatile renewable energy integration are pushing an aging power grid to the limit.
That is why Argonne is providing this asset health management tool to operators. This will help ensure the future reliability and security of our electric grid. But it will also level the playing field, providing small energy companies with the same cutting-edge technology as the major corporations.
Qiu’s team is quick to note that this research would not have been possible without close collaboration with their partners in the energy industry. Their long list of partners includes power companies as well as representatives from the areas of hydropower, solar power, and wave energy, and academia such as Wayne State University and Iowa State University. “Our research represents a collaborative effort between scientists, engineers and industry partners,” Zhao noted. “Together, we’re driving positive change and shaping the future of energy grid maintenance.”
This is just one of the ways Argonne is tackling today’s grid challenges. By harnessing the power of AI and real-world data, energy providers using prognostics-based maintenance technology can maximize the lifespan of existing infrastructure, minimize downtime and ensure a reliable energy supply for generations to come. With continued innovation and collaboration, the future of energy grid maintenance looks brighter than ever before.
Liz Thompson is a writer at the Argonne National Laboratory (ANL). The article was originally posted to the website of ANL.
