Abstract
Failures of the gas turbine hot components often cause catastrophic consequences. Early fault detection can detect the sign of fault occurrence at an early stage, improve availability and prevent serious incidents of the plant. Monitoring the variation of exhaust gas temperature (EGT) is an effective early fault detection method. Thus, a new gas turbine hot components early fault detection method is developed in this paper. By introducing a priori knowledge and quantum particle swarm optimization (QPSO), the exhaust gas temperature profile continuous distribution model is established with finite EGT measuring data. The method eliminates influences of operating and ambient condition changes and especially the gas swirl effect. The experiment reveals the presented method has higher fault detection sensitivity.
Highlights
The hot components are the crucial components of gas turbines, including the combustors and turbines
The model proposed in this paper reduces the influence of gas swirl effectively, compresses fault detection proposed in this paper reduces the influence of gas swirl effectively, compresses fault detection proposed in this paperfault reduces the influence of gas swirl effectively, compresses fault detection threshold, increases sensitivity
A method used for early warning of gas turbine hot components is developed
Summary
The hot components are the crucial components of gas turbines, including the combustors and turbines. The temperature differences between different EGT values could be caused by the hot component faults and by various ambient and operating conditions. The influence of different ambient and operating conditions on the EGT profile should be fully considered, especially the swirl effect. Before and after the ambient or operating conditions change, one exhaust thermocouple measuring results may reveal the performance of different combustor because of the swirl effect. Based on the characteristics of the EGT profile, a novel early fault detection method of gas turbine hot components is developed. The developed method fully considers the influence of different ambient and operating conditions on the EGT profile by estimating the EGT profile’s continuous distribution. The third section verifies the availability of the proposed method with experiments, follows, which is the conclusion
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