Fault Detection and Isolation Based on Neural Networks Case Study: Steam Turbine ()
Abstract
The real-time fault diagnosis system is very important for steam turbine generator set due serious fault results in a reduced amount of electricity supply in power plant. A novel real-time fault diagnosis system is proposed by using Levenberg-Marquardt algorithm related to tuning parameters of Artificial Neural Network (ANN). The model of novel fault diagnosis system by using ANN are built and analyzed. Cases of the diagnosis are simulated. The results show that the real-time fault diagnosis system is of high accuracy and quick convergence. It is also found that this model is feasible in real-time fault diagnosis. The steam turbine is used as a power generator by SONELGAZ, an Algerian company located at Cap Djinet town in Boumerdes district. We used this turbine as our main target for the purpose of this analysis. After deep investigation, while keeping our focus on the most sensitive parts within the turbine, the weakest and the strongest points of the system were identified. Those are the points mostly adequate for failure simulations and at which the designed system will be better positioned for irregularities detection during the production process.
Share and Cite:
D. Benazzouz, S. Benammar and S. Adjerid, "Fault Detection and Isolation Based on Neural Networks Case Study: Steam Turbine,"
Energy and Power Engineering, Vol. 3 No. 4, 2011, pp. 513-516. doi:
10.4236/epe.2011.34062.
Conflicts of Interest
The authors declare no conflicts of interest.
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