scholarly journals Kalman Filtering Applied to Induction Motor State Estimation

2020 ◽  
Author(s):  
Yassine Zahraoui ◽  
Mohamed Akherraz
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Induction Motor ◽  
Kalman Filtering ◽  
Speed Estimation ◽  
Stochastic Algorithm ◽  
State Variables ◽  
Extended Kalman Filtering ◽  
Filtering Theory ◽  
State Observation

This chapter presents a full definition and explanation of Kalman filtering theory, precisely the filter stochastic algorithm. After the definition, a concrete example of application is explained. The simulated example concerns an extended Kalman filter applied to machine state and speed estimation. A full observation of an induction motor state variables and mechanical speed will be presented and discussed in details. A comparison between extended Kalman filtering and adaptive Luenberger state observation will be highlighted and discussed in detail with many figures. In conclusion, the chapter is ended by listing the Kalman filtering main advantages and recent advances in the scientific literature.

Research on Stator Flux and Rotor Speed Estimation for Induction Motor Based on Extended Complex Kalman Filter

2014 ◽  
Vol 644-650 ◽  
pp. 709-713
Author(s):  
Yue Dou Pan ◽  
Ying Wei Guo ◽  
Hua De Li
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Induction Motor ◽  
Estimation Method ◽  
Torque Control ◽  
Speed Estimation ◽  
Rotor Speed ◽  
Extended Complex ◽  
Stator Flux ◽  
Complex Valued

This paper proposes a stator flux and rotor speed estimation method for induction motor (IM) based on extended complex Kalman filter (ECKF). A complex-valued model is adopted that allows a simpler and more effective state estimation of IM. And a third order ECKF has been designed for IM model by neglecting the mechanical equation, which is a valid hypothesis when the motor is operated at a constant rotor speed. The stator flux and rotor speed estimation obtained from ECKF are applied for direct torque control (DTC) of induction motor. This method for state estimation is more effective and easier than the one performed on the corresponding real-valued model, as it obtains observability conditions directly in terms of stator current and flux complex-valued vectors. The proposed ECKF achieves a good performance in DTC of IM. Simulation experiment results in Matlab/Simulink environment validate the proposed method.

scholarly journals Iterated extended Kalman filter based state estimation of diode circuit

2021 ◽  
Vol 2070 (1) ◽  
pp. 012092
Author(s):  
Amit Kumar Gautam ◽  
Sudipta Majumdar
Keyword(s):  
Performance Evaluation ◽  
Kalman Filter ◽  
Root Mean Square Error ◽  
State Estimation ◽  
Mean Square Error ◽  
Extended Kalman Filter ◽  
Kalman Filtering ◽  
Mean Square ◽  
Extended Kalman Filtering ◽  
Iterated Extended Kalman Filter

Abstract This paper presents the state estimation of diode circuit using iterated extended Kalman filter (IEKF). The root mean square error (RMSE) based performance evaluation gives the superiority of the IEKF based estimation over extended Kalman filtering (EKF) based method.

A new metaheuristic unscented Kalman filter for state vector estimation of the induction motor based on Ant Lion optimizer

2018 ◽  
Vol 37 (3) ◽  
pp. 1054-1068 ◽  
Author(s):  
Marouane Rayyam ◽  
Malika Zazi ◽  
Youssef Barradi
Keyword(s):  
Kalman Filter ◽  
Induction Motor ◽  
Kalman Filtering ◽  
Unscented Kalman Filter ◽  
Dynamic Performance ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Content Type ◽  
Ant Lion Optimization ◽  
Ant Lion

PurposeTo improve sensorless control of induction motor using Kalman filtering family, this paper aims to introduce a new metaheuristic optimizer algorithm for online rotor speed and flux estimation.Design/methodology/approachThe main problem with unscented Kalman filter (UKF) observer is its sensibility to the initial values of Q and R. To solve the optimal solution of these matrices, a novel alternative called ant lion optimization (ALO)-UKF is introduced. It is based on the combination of the classical UKF observer and a nature-inspired metaheuristic algorithm, ALO.FindingsSynthesized ALO-UKF has given good results over the famous extended Kalman filter and the classical UKF observer in terms of accuracy and dynamic performance. A comparison between ALO and particle swarm optimization (PSO) was established. Simulations illustrate that ALO recovers rapidly and accurately while PSO has a slower convergence.Originality/valueUsing the proposed approach, tuning the design matrices Q and R in Kalman filtering becomes an easy task with a high degree of accuracy and the constraints of time cost are surmounted. Also, ALO-UKF is an efficient tool to improve estimation performance of states and parameters’ uncertainties of the induction motor. Related optimization technique can be extended to faults monitoring by online identification of their corresponding signatures.

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