Case Studies

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 power generation operator sought to expand their monitoring and diagnostic capabilities for aeroderivative gas turbines. While digital twin technology had proven valuable for frame-type units, the client needed similar capabilities extended to a different class of equipment, along with support for implementing advanced combustion monitoring techniques.

A client faced a growing compatibility challenge with a critical performance monitoring tool that had been in use for years. The software relied on older architecture that was becoming increasingly incompatible with modern computing environments as industry standards evolved.

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 better understand the real-world performance benefits of bifacial photovoltaic modules compared to traditional monofacial installations. With growing industry interest in bifacial technology, there was a need for rigorous, data-driven analysis to quantify actual performance gains and identify the factors that most significantly influence energy production from these advanced solar modules.

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.