Today, many utilities don’t have the necessary inputs to accurately apply Artificial Intelligence (AI) in a useful way. In fact, many don’t even know where their poles are located, let alone how to apply AI to their data models. Little do they know, the possibilities are endless – from detecting damage and defects on poles, to understanding how and where to divert electricity during storm events.
AI offers sophisticated tools to address a raft of other challenges from asset maintenance to customer engagement. In today’s blog we show how utilities can use it to glean insights from big data, enhance their decision-making, and anticipate failures with pinpoint accuracy.
The Age of Neural Networks: What Does It Mean?
AI is set to transform utility management. Utility bosses recognize its power, at least in theory, as Itron’s recent survey of 600 providers – entitled ‘Exploring AI for Utilities: The Promise and Challenges of Artificial Intelligence’ found that 86% believed it to be crucial to their energy transition and tackling operational issues in general.
But today’s transmission operators face daunting challenges: creaking infrastructure, growing customer demand, and climatic threats. They often lack the precise data inputs necessary to harness AI effectively. Utilities need to get to work on fully mapping their assets; fortunately, AI can also help here. Once the assets are known, it can turn overwhelming data volumes into actionable knowledge.
In fact, rapidly improving neural networks allow for the interpretation of vast, complex, multi-dimensional datasets. In real-world applications, they enhance and accelerate decision-making by enabling sharp predictive insights and helping utilities to fine-tune their operational strategies in a way that traditional systems could never achieve.
In particular, utilities are better equipped to preemptively adjust load distributions and avoid overloads in extreme weather conditions.
A recent case from Europe’s July 2024 heatwave illustrates this well. In these sweltering conditions, an operator used neural networks to monitor grid stress points in real-time, preventing failure by redistributing power loads dynamically​. This incident confirmed that AI is fully up to the task of protecting the modern grid against harsh environmental and operational stresses.
The technology is also likely to play an important role in the ongoing integration of renewable energy into transmission systems. By analyzing data from solar and wind farms alongside grid performance metrics, AI enables near flawless incorporation of intermittent energy sources. Utilities now have the systems they need to reduce dependence on hydrocarbons without compromising grid reliability.
That’s badly needed, as the US energy sector must figure out how to double or even triple the size of the grid over the next two decades while maintaining affordability.
Damage Detection
Traditionally, utility inspections have relied heavily on human expertise, whether through manual patrols or visual observations from aircraft. This approach often leads to siloed knowledge, inefficiencies, and missed opportunities for proactive maintenance. AI introduces an alternative: automating damage detection through advanced image analysis and machine learning (ML).
Equipped with cameras and sensors, drones can collect vast quantities of data during each flight. AI algorithms then analyze these datasets to identify defects such as corrosion, cracks, or sagging lines. The Thread Whitepaper (2024) highlights how automated tagging and classification of drone imagery reduced post-flight processing times by up to 75% for one California utility.​
AI’s applications extend beyond visual data, however. Using LiDAR and thermal imaging technologies, AI can detect issues invisible to the naked eye, such as overheating components or encroaching vegetation that risks line damage during storms. This ability to interpret multiple data modalities is critical in maintaining grid integrity, especially as extreme weather events grow more frequent.
Preventing Failures Before They Occur
Predictive maintenance, powered by AI, has become one of the most impactful applications for utilities. By analyzing historical performance data and combining it with real-time sensor inputs, AI systems forecast potential equipment failures with high accuracy. This allows utilities to address issues before they escalate into expensive outages.
UNITI Workspace brings some excellent examples, as noted in its 2024 Playbook. The platform’s predictive models estimate the remaining lifespan of critical components based on usage patterns and environmental factors.​ For instance, during a 2023 trial, a Texas transmission operator reduced unplanned outages by 40% and cut maintenance costs by replacing transformers flagged as high-risk.
Utilities can now avoid spur-of-the moment emergency repairs and associated personnel and cost issues that typically come with that type of scenario.
AI-driven predictive analytics can also help utilities to manage steadily deteriorating legacy infrastructure. The average age of large power transformers, accounting for 90% of U.S. electricity flow, is more than 40 years – which explains why proactive management is so crucial​. By prioritizing interventions, utilities can maximize asset utilization while ensuring grid stability.
Introducing the Internet of Things (IoT)
Utilities are now using IoT sensors deployed across their transmission networks to monitor critical parameters such as line temperature, tension, and environmental conditions. When paired with AI, this data becomes a foundation for dynamic decision-making, enabling utilities to respond to grid fluctuations instantly.
For example, in a bid to avoid a repetition of the 2023 wildfire season in California, PG&E and other major California utilities are now using AI, drones, and computer vision to identify potential equipment failures, such as worn or rusted components, before they lead to fires.
This strategy prevented damage to critical infrastructure and reduced the likelihood of power line-sparked wildfires​.
AI’s integration with IoT also facilitates demand-response strategies, particularly during periods of high grid stress. In 2024, a Texas-based utility implemented AI-driven load balancing using IoT sensor data. By redistributing power away from overheating lines to those with more capacity, the system prevented cascading outages during a heatwave.
Vegetation Management
Vegetation encroachment remains one of the leading causes of transmission outages globally. Traditionally, managing this risk has required time-intensive manual surveys or helicopter inspections. AI has revolutionized this process by automating vegetation analysis through satellite imagery, drone footage, and LiDAR data.
Using AI-powered algorithms, utilities can identify areas where vegetation is encroaching on transmission lines with high precision. Leveraging satellite imagery and AI, utilities can predict, monitor, and identify vegetation threats to transmission and distribution grids. These systems help develop proactive disaster recovery plans for storms and wildfires, accelerate response times, and provide real-time post-disaster insights to reduce outage duration
This precision also extends to storm preparedness. In hurricane-prone regions, AI systems analyze real-time wind forecasts and identify vegetation clusters most likely to cause damage. During Hurricane Ian in 2022, a Florida utility used such a system to target high-risk zones for emergency clearing, mitigating disruptions and expediting recovery efforts.
Improving Cybersecurity with AI
As utilities digitize their transmission networks, the risk of cyberattacks increases significantly. Transmission control systems, including Supervisory Control and Data Acquisition (SCADA) networks, are now prime targets for hackers seeking to disrupt grid stability. AI is emerging as a critical tool in combating these threats by detecting anomalies and responding to intrusions in real-time.
AI-driven cybersecurity solutions identify potentially malicious activity. These systems monitor real-time data streams for unusual patterns, such as unexpected spikes in network traffic or unauthorized access attempts, that may indicate a potential cybersecurity threat.
In addition to anomaly detection, AI enhances authentication processes. By adopting multi-factor authentication (MFA), utilities can more effectively evaluate behavioral patterns—such as login locations or device usage—to flag potentially unauthorized access attempts. This level of security is critical for safeguarding any control system used for managing high-voltage transmission networks.
Edge Computing and Digital Twins
Another emerging application is edge computing, where AI algorithms process data locally on IoT devices. This reduces latency, enabling faster decision-making in remote transmission corridors where connectivity is limited.
For example, edge-enabled drones equipped with AI can analyze line conditions during flights and relay actionable insights directly to field teams. In a recent pilot program in Alberta, Canada, these drones identified damaged insulators in real-time, allowing crews to initiate repairs immediately upon landing. This type of approach eliminates the delays associated with post-flight data processing.
Elsewhere, digital twins—virtual replicas of physical grid infrastructure powered—can help utilities to simulate grid performance under various conditions. They can test scenarios ranging from load surges to extreme weather events without risking real-world failures. Digital twins also support long-term planning by predicting the likely effects of infrastructure upgrades.
AI isn’t entirely new to utility providers; many have been experimenting with load forecasting for some time. However, the proliferation of cloud-based solutions and collapsing chip prices means that the sector has reached an inflection point. With the right solutions in place, it is now a catalyst for growth.
