Evaluating software for aging condition of main insulation based on artificial intelligence technology

The condition assessment of large generator main insulation is an important research subject in electrical engineering field, and the key destination is to assess the aging condition of insulation based on the nondestructive parameters. A new artificial intelligent assessment method based on the combination of fuzzy math theory and artificial neutral network (ANN) is proposed in order to overcome the disadvantages of traditional insulation condition assessment based on the threshold model. Firstly, the 3 layers of BP ANN are established with 4 fuzzy outputs, which are the degrees of membership to four fuzzy subsets of insulation condition respectively, and 28 inputs corresponding to 28 nondestructive parameters of insulation respectively. Secondly, the ANN with fuzzy outputs is trained by the Levenberg-Marquardt fast training algorithm with the goal error of 0.0001, and the ability of condition evaluation of the network is verified by five 18 kV/300 MW practical stator bars. Finally, the intelligent evaluating software of main insulation based on MATLAB is developed. The research results show that the technique could assess the aging condition of stator bar insulation effectively and accurately.