Notice of RetractionStudy on Turbine Through-Flow Model Based on Self-Adaptive BP Network

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

Turbine through-flow model is the great part of the performance analysis model and fault diagnose. However, the performance analysis and fault diagnosis in power plants is depend on plenty of thermal parameters of which the reliability and accuracy are very important for real-time monitoring and automatically control. The results of traditional models would be impacted by the inaccurate parameters usually. A novel through-flow model is presented to calculate the parameters of turbine extraction steam based on Artificial Neural Network (ANN) only with the extrinsic parameters of power unit. Compared with common models, the presented model is simple with fewer inputs, and the results are accurate and reliable for thermal system analysis. The relative errors of the model results and traditional models are less than 0.90% that showed the satisfactory generalization ability. The model presented by this paper can be widespread application for data management system in power plants.