Asset analytics is essential for modernizing the power grid to meet ambitious climate goals, accommodate growing energy consumption, and manage a more complex energy mix. To achieve this, we must transition from relying on statistical models to leveraging real-world data.

Historically, limited data availability has constrained maintenance and investment in power grid infrastructure. Data collection was often expensive and, at times, considered unnecessary. With stable, easily forecastable power generation and consumption in a "one-way" system, grid load was straightforward to manage. For much of the 20th century, energy demand growth was predictable. While extreme weather events have always been a factor, their historically lower frequency and severity allowed for a largely reactive approach to grid maintenance. As a result, investment and maintenance were split into two separate processes:

1. Reactive maintenance in response to unforeseen outages, typically triggered by events like blackouts or brownouts. Proactive maintenance was generally limited to obvious issues such as vegetation interference (e.g., trees falling on power lines).
2. Replacement and investment based on statistical models predicting wear-and-tear, derived from various factors.

However, the world where these methods sufficed is rapidly disappearing. The environment—both natural and operational—is evolving. The grid has shifted from a one-way to a multi-directional system, with energy now flowing both to and from consumers. Electrification of transportation and industry is adding significant pressure to a grid built for a bygone era. Furthermore, extreme weather events are increasing in frequency and intensity, making reactive approaches not only insufficient but a genuine risk to the safety, reliability, and sustainability of the power grid.

Arkion’s asset analytics offers a transformative solution, integrating maintenance and investment data collection for more effective grid management:

1. Predictive maintenance and investment based on real-world conditions. Short-term outage risks can be monitored across all assets, while long-term investment decisions can be informed by detailed data on the grid's condition. Instead of relying on models built on limited data, operators can now assess the actual state and risk factors of grid components. This allows for precise planning—no longer needing to estimate failure rates when real-time data shows exactly which components require maintenance or replacement.

The cornerstone of this approach is scalable analysis. Historically, power grids were limited by the availability of data, such as visual inspections via images. With the growing prevalence of drone technology, a new challenge has emerged: how to efficiently process the vast quantities of data produced by drone inspections. Arkion’s proprietary AI-powered solution addresses this issue by efficiently extracting actionable insights from hundreds of thousands of images.

This in-depth, real-time data on every asset, component, and section of power lines is accessible throughout the organization, ushering in a new era of power grid management—where models are replaced by facts, and uncertainty is replaced by knowledge.