AI for Wind Energy Optimization

The optimization of operating modes for maximum power generation and the reduction of power consumption during low-wind periods are just two of the problem statements that engineers and subject matter experts can produce AI models for. Along with these, teams can produce models to help forecast future energy supply or compare turbine performances, with models produced in minutes, and deployed straight into a live production environment for real-time updates.

Both run-to-fail and preventative maintenance methods are high-cost maintenance strategies. Using AI, wind farm operators can implement predictive maintenance strategies with timelines for asset degradation and failure. VROC OPUS provides root cause analysis allowing operators to schedule maintenance in advance before a failure. This maintenance approach reduces maintenance costs and helps maintain asset reliability.

Ai can assist wind farm operators during the first three years of the assets life, which are typically plagued with high failure rates. VROC OPUS can predict failures on new assets, with no prior failure history. The advanced automated AI technology continuously learns how the entire system functions under all conditions, helping wind farm operators maintain steady state from the beginning.

Energy producers with a diverse mix of energy production assets can benefit from the insights that can be learned across their entire portfolio using AI. VROC OPUS can help Energy companies optimize their portfolios, improve ESG outcomes, and help manage the grid with real-time monitoring, predictive analytics and forecasting across the entire network.