ADVANCED MONITORING TO IMPROVE COMBUSTION TURBINE/COMBINED CYCLE CT/(CC) RELIABILITY, AVAILABILITY AND MAINTAINABILITY (RAM)

Angello, Leonard

Electric Power Research Institute, Inc. (United States)

关键词: Monitoring;    Availability;    Turbines;    Maintenance;    Reliability;   

DOI  :  10.2172/840623
RP-ID  :  NONE
RP-ID  :  FC26-01NT41233
RP-ID  :  840623

美国|英语

来源: UNT Digital Library

【 摘 要 】

Power generators are concerned with the maintenance costs associated with the advanced turbines that they are purchasing. Since these machines do not have fully established operation and maintenance (O&M) track records, power generators face financial risk due to uncertain future maintenance costs. This risk is of particular concern, as the electricity industry transitions to a competitive business environment in which unexpected O&M costs cannot be passed through to consumers. These concerns have accelerated the need for intelligent software-based diagnostic systems that can monitor the health of a combustion turbine in real time and provide valuable information on the machine's performance to its owner/operators. Such systems would interpret sensor and instrument outputs, correlate them to the machine's condition, provide interpretative analyses, forward projections of servicing intervals, estimate remaining component life, and identify faults. EPRI, Impact Technologies, Boyce Engineering, and Progress Energy have teamed to develop a suite of intelligent software tools integrated with a diagnostic monitoring platform that will, in real time, interpret data to assess the ''total health'' of combustion turbines. The Combustion Turbine Health Management System (CTHM) will consist of a series of dynamic link library (DLL) programs residing on a diagnostic monitoring platform that accepts turbine health data from existing monitoring instrumentation. The CTHM system will be a significant improvement over currently available techniques for turbine monitoring and diagnostics. CTHM will interpret sensor and instrument outputs, correlate them to a machine's condition, provide interpretative analyses, project servicing intervals, and estimate remaining component life. In addition, it will enable real-time anomaly detection and diagnostics of performance and mechanical faults, enabling power producers to more accurately predict critical component remaining useful life and turbine degradation.