Novel Applications of Data Analytics in Gas Turbine Operation for Distributed Power Generation

Abstract Recent advancements in data analytics and digitalization has increased the value which power generating assets, such as gas turbines, bring to power plant customers. Distributed power generation customers are increasingly partnering with gas turbine original equipment manufacturers and third-party digital service providers for cooperation in technology, innovation, and asset management using real-time data. In the past, it has been discussed that data analytics can be a key enabler in addressing challenges such as operational flexibility and efficient asset management. This paper presents some of the novel applications of data analytics developed by Siemens Energy for gas turbine operation, condition monitoring, and real-time decision making. Specifically, the paper presents the idea, application, and test case of remote diagnostic system, enhanced vibration analytics, and myFleetRisk™. Remote diagnostic system increases uptime, reduces operating cost by resource optimization, and improves plant output. This paper discusses both the reactive and proactive approach to gas turbine maintenance and how remote diagnostic system can be used with data analytics landscape for deviation monitoring. In addition to an overview of the underlying concept and its novelty, real-world applications are presented to show the benefits to distributed power generation customers. MyfleetRisk offers optimized and tailored maintenance solutions resulting in significant life cycle cost reduction. Enhanced vibration analytics module enables reliability centered maintenance and thereby addresses key challenges to customers.