AI in Power Sector

Target actionable sources of underperformance and maximize uptime with AI in power sector advantages

SparkCognition’s AI solutions help you monitor component reliability to know up to weeks in advance when power-generating assets are trending toward failure. With machine learning (ML) models that adapt and maintain themselves dynamically whenever components or assets change, our solutions scale efficiently up and down your operations to correct areas of underperformance and maximize your uptime.

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Enabling safer, more reliable operations: case studies

Unexpected downtime leads to painful production losses and unplanned spending for power producers. To remain competitive in the growing energy landscape, you can’t allow critical assets to run to failure. If you can predict them, you can prevent or mitigate them to reduce costly downtime.

Predicting rare failures in hydro turbines

Learn how SparkCognition helped a leading hydro utility recover from an unplanned outage that cost the business an estimated $1.5M. Using sparse failure data to evaluate and machine learning models, SparkCognition implemented a solution to identify future large-scale outages with a full month’s advanced warning. Read our case study

Providing advance notice to prevent the next $30M outage

Learn how our ML model identified—a full month in advance—a devastating row-two vane failure that had previously caused massive secondary damage to our customer’s compressor, leading to two months of downtime and up to $30M in repair costs and lost opportunity. Read our case study

Improving grid reliability & resilience

Asset management can account for up to 30% of a transmission and distribution (T&D) company’s operating expenses and up to 20% of its capital expenditures. Learn how SparkCognition’s AI in power sector solution brings down costs, improves customer satisfaction, and increases the reliability of T&D networks. Read our solution sheet

    AI and power: A new approach to streamline maintenance and boost performance

    Proactively identify anomalous behavior​

    SparkCognition solutions leverage machine learning at scale, monitoring component reliability and providing actionable insights to help you reduce generation losses and prevent catastrophic asset failures before they happen.

    Optimize your maintenance planning ​

    Machine learning-based anomaly management works in real-time and over time, helping you plan and bundle work orders for remote sites, reduce unnecessary checks and repairs, and increase asset availability across all operations.

    Correct areas of underperformance​

    AI in power uses the quantitative data you already have, plus qualitative data from sources like subject matter experts, to help you discover hidden areas of underperformance, for better production efficiency.

    Reducing downtime, driving efficiency, and improving profitability

    The power and utilities industry is facing disruption from the rapid growth of renewable energy sources and fluctuating energy demand. Power-producing companies require high ROI, innovation, and digital transformation to stay ahead of their competition.

    Leverage the full potential of your data to enable safer and more predictable operations and de-risk your future.

    Investing in a data-driven approach to focus on actual observations and system behavior within the contextual environment is a winning strategy to de-risk the future and maximize profitability for your power generation business. SparkCognition’s AI in power sector solutions help you leverage the full potential of your data—enabling safer and more predictable operations.

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