Case Studies

A client needed to expand their gas turbine monitoring and diagnostic capabilities to cover additional equipment types within their fleet. Existing digital twin models were limited to certain turbine configurations, creating gaps in their ability to perform comprehensive performance analysis across all assets.

Power generation operators managing multiple assets often struggle to balance competing priorities: responding quickly to grid demands, maintaining optimal efficiency, and minimizing maintenance impacts across their fleet. Traditional dispatch approaches rely on static models that fail to account for real-time equipment health, leading to suboptimal bidding strategies and reduced profitability.

A utility-scale solar plant operator sought to transition from reactive and preventative maintenance practices toward more cost-effective condition-based approaches. The challenge was identifying performance anomalies and equipment issues early enough to reduce energy losses and maintenance costs, while minimizing false alarms that waste operational resources.

A client sought to improve their ability to detect and diagnose equipment faults in their gas turbine fleet before they led to costly unplanned outages. Traditional monitoring approaches were reactive, often identifying problems only after performance had already degraded significantly.

A client needed a way to evaluate the economic impact of different air filtration strategies for their gas turbine operations. The challenge involved balancing multiple competing factors: filter efficiency, pressure drop effects on turbine performance, maintenance costs, water wash scheduling, and long-term operational expenses.

A client sought to expand their capabilities in gas turbine performance monitoring and analysis. They needed both educational resources to help their team understand and apply digital twin technology, as well as refinements to calibration software that would meet rigorous quality standards for deployment.