Abstrait

Genetically Tuned Dual-ANFIS Model for Steam Turbine Fault Diagnosis and Treatment

D. N. Dewangan, Dr. Y. P. Banjare, Dr. Manoj Kumar Jha

Fault diagnosis of steam turbine is essential to predict further development and to anticipate it by taking appropriate measures. Fault diagnosis of modern industrial power plants by human inspection is time-consuming and expensive as well as fault diagnostic system modelling based on conventional mathematical tools is not suitable for ill defined and uncertain system. Therefore, it is necessary to develop a knowledge-based intelligent fault diagnostic and treatment system. The primary aim of the work is developing a fast and reliable fault diagnostic and treatment system to assist plant operators. Averaging error of ANFIS is opted for fitness function of the genetic program. In this diagnosis process, the fault diagnosis and treatment model has simulated using MATLab Simulink and obtain rules set extracted by original neural network, ANFIS structure and genetically tuned dual-ANFIS. The comparative result of fault diagnosis of different method shows that the mode of genetically tuned ANFIS has higher precision in comparison to other knowledge obtaining methods.