Predictive maintenance for hydropower producers

Unexpected hydropower asset failures can grind operations to a halt for extensive periods of time, driving costs into the millions of dollars in production downtime and repairs.

The hydropower industry can prevent surprise asset failures by adopting an artificial intelligence (AI)-powered predictive maintenance paradigm that provides more lead time to identify expensive future failures.

SparkCognition provides an AI-based predictive analytics solution that enhances renewable power producers’ operational visibility and delivers actionable insights on asset health.

SparkCognition’s solution enables hydropower producers to:

  • Increase asset availability across massive operations
  • Predict asset failures in advance, ranging from days to weeks
  • Minimize costs and labor associated with model maintenance
  • Reduce risk exposure for employees

It delivers the power of expecting the unexpected.

“Cutting-edge technologies, like AI-based predictive analytics, are key enablers to improving the efficiency of our operations and meeting our ambition to become a net-zero company by 2050 or sooner. Working collaboratively with SparkCognition, we have delivered this project in an agile way,”

– Fereidoun Abbassian, former VP of Transformation, Upstream Technology at bp

Prevent catastrophic asset failures

Continuously monitoring component reliability helps hydropower producers avoid damaging generation losses. Using natural language processing to assess fault codes, SparkCognition’s predictive maintenance solution delivers the most relevant documentation to streamline repair, while capturing user inputs to improve model results.

Increase lead time to fix assets trending toward failure

Get early notice of suboptimal operations and impending asset failure—up to days or weeks in advance—with minimal false positives. For one hydropower utility, the SparkPredict® product used machine learning models to find a large-scale outage with one month advance warning.

Quickly build more accurate, cost-efficient models

The time has passed for labor-intensive, brittle, and hard-to-scale models. Automated model building enables data scientists and non-technical users to quickly create highly accurate models using production data they already have, without requiring the purchase of new parts.

Improve efficiency, safety, and profitability

Machine learning-based anomaly management and predictive analytics work intelligently together to proactively identify anomalous behaviors over time. For hydropower producers, that means optimized maintenance scheduling, efficiency gains from grouping work orders and putting the right parts in place at the right time, and being able to ensure a safer hydropower plant.

SparkCognition solutions have been used to reduce operating cost and improvement in efficiency by 2-5%

Modern hydro turbines are massive assets, producing hundreds of megawatts of power. Due to the scale of these assets, any scheduled or unscheduled downtime translates into significant opportunity costs — upwards of $50,000 per day.

To mitigate the effects of asset deterioration and ensure maximum uptime, hydropower producers need to future-proof their assets today.

SparkCognition delivers an AI-based predictive analytics solution for hydropower producers designed to prevent unplanned outages and increase lead time to address assets trending to failure.

Learn how our solution predicted unknown failures for a leading hydropower producer.

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