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

Angello, Leonard

Electric Power Research Institute (United States)

关键词: Diagnostic Techniques;    Monitoring;    Availability;    Turbines;    Maintenance;   

DOI  :  10.2172/840620
RP-ID  :  NONE
RP-ID  :  FC26-01NT41233
RP-ID  :  840620

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. 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.