Harnessing data, machine learning, and predictive analytics for storm resilience

In the world of electric utilities, storm season can bring disruptions from mild to catastrophic. It’s not just about reliability but the safety and well-being of communities across the service territory. The industry is embarking on a transformative journey that includes embracing data, machine learning, and predictive analytics as mission-critical tools to bolster grid resilience.

Utility professionals serve as a guiding compass for storm hardening and risk mitigation efforts, using their experience to identify vulnerabilities within the system. Valuable as it may be, experience can only carry us so far – particularly in light of the challenges of an aging workforce. As we face the future of increasingly unpredictable weather patterns and intensifying storms, the future looks daunting.

Fortunately, the industry is moving towards more data-driven decision-making. This is especially important when it comes to managing overhead assets, which are highly vulnerable to storms. There is a massive amount of information that can paint a comprehensive picture of vulnerabilities threatening the grid and assist in prioritizing actions to mitigate them. Top-performing utilities are at the forefront of this shift, having recognized the potential of harnessing data to reach greater levels of understanding structural resilience.

Using disparate data sources such as historical storm data, asset imagery, pole loading analysis, and geographical location, machine learning algorithms can classify levels of risk and identify areas prone to weather-related outages. Informed by insights into network conditions, utilities can proactively implement targeted inspections and, ultimately, predictive maintenance and infrastructure upgrades. Data analytics can drive strategic investments such as pole replacements, power line reinforcement in high-wind zones, or equipment upgrades in flood-prone areas.

Extending the benefits of data-driven resilience to operational efficiency and customer satisfaction affords utilities with additional benefits. Preemptively shoring up vulnerable points in the network reduces the frequency or duration of outages as well as infuses customers with a sense of confidence, underscoring the utility’s commitment to deliver uninterrupted power despite adversity.

This journey towards increased resilience is not without its challenges. The efficacy of predictive analytics hinges on the availability of high-quality data. Investments in technology are essential to ensure the continuous flow of actionable insights.

It also necessitates robust data governance frameworks to ensure the integrity and security of sensitive information. Adherence to stringent privacy regulations and cybersecurity protocols is critical to safeguarding against potential breaches as utilities navigate the complexities of data collection and analysis. Informed decision-making depends on fostering a culture of data literacy and maximizing the use of analytical tools by utility personnel.

Despite the challenges, the industry’s momentum towards greater resilience through data-driven decision-making continues to accelerate. As storms grow in frequency and intensity, the ability to quickly and accurately anticipate, mitigate, and recover from their impact becomes more crucial. As capabilities such as advanced data analysis and machine learning become part of the fabric of the industry, utilities can assess network resilience, identify weak points, and fortify infrastructure before the storms hit. With storm season looming, the imperative to embrace data to improve resilience has never been clearer, propelling the industry towards a future of preparedness and resilience.

Tools like IKE’s suite of pole data and analysis solutions can help utilities develop comprehensive storm-hardening strategies, prioritize projects, ensure NESC compliance, and restore power faster. Learn more.

IKE for Electric Utilities

John J. Simmins is the Executive Director of the NYS Center for Advanced Ceramic Technology (CACT) at Alfred University. In this position, he supports sponsored research for approximately 50 engineering faculty and 50 graduate students. Alfred provides undergraduate and graduate degrees in Renewable Energy Engineering, Mechanical Engineering, as well as Glass and Ceramic Engineering. Dr. Simmins spent ten years at EPRI as a Technical Executive before going to Alfred. At EPRI he studied the intersection of augmented reality, artificial intelligence, and geospatial information systems. He holds a B.S. and Ph.D. in Ceramic Engineering from Alfred University.