Research On Motor Stator Inter-turn Short-circuit Identification Based On Adaptive Kalman Filter

This paper has a background of the deep oil recovery in the late period of oil exploitation and focuses on the online identification of the inter-turn short-circuit(ITSC)faults of induction motors.The purpose is to study the early online identification of the inter-turn short-circuit fault of the stator winding of the submersible motor to avoid further damage caused by the fault,which is of great significance to the protection of the motor and oil exploitation.In order to identify the fault parameters of the motor online,an adaptive extended Kalman filter estimation algorithm is proposed.The LabVIEW-based motor fault diagnosis experimental platform is built to realize the on-line detection of the stator ITSC fault of the induction motor using the existing 1.5kw induction motor in the laboratory.According to the three-phase equivalent circuit topology of the induction motor with ITSC fault,two fault parameters that characterize the fault degree are introduced: short-circuit ratio and short-circuit resistance.A mathematical model of the induction motor with stator ITSC fault is established.Based on the mathematical model,the simulation model under MATLAB is built.The simulation is compared with the actual fault motor to verify the validity of the model.The corresponding relationship between the two fault parameters and the fault degree is explored through a large number of simulations.Based on the positive and negative sequence model of the faulted motor,the constraint relationship between the two fault parameters of the induction motor with the short-circuit resistance fault is derived.A basic extended Kalman filter estimation algorithm is proposed and studied.Based on the mathematical model of the faulty motor that has been established,the state space equation of the fault parameter with the short-circuit ratio is derived,which can further utilize the EKF state estimation algorithm to identify the fault parameters online.The deficiencies of the EKF identification method in practical application are analyzed.Using the EKF identification method,parameter changes and inaccurate models can lead to problems such as large estimation errors.Based on this,the dual extended Kalman filter(DKF)and the idea of self-adaptive are introduced to solve these problems in EKF.The fault motor model and the identification algorithm are simulated under MATLAB/Simulink,and the fault characteristics of the motor are analyzed and the feasibility of the improved EKF algorithm to identify the motor fault is verified.In order to verify the feasibility of the proposed AEKF algorithm to identify the inter-turn short-circuit fault of the induction motor stator winding,a LabVIEW-based experimental platform for fault diagnosis of induction motors was built in the laboratory.Under this experimental platform,the fault diagnosis experiments of different faults and different load motors are carried out to verify the applicability of the proposed method.The experimental results show that the load variation has little effect on the identification results,and the identification accuracy of the short-circuit turns ratio is good under different faults.The relative error is kept at 5%,and the identification accuracy of the short-circuit resistance is affected by the fault degree.The identification result can well reflect the initial fault state of the motor.