scholarly journals Desensitized Ensemble Kalman Filtering for Induction Motor Estimation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 78029-78036 ◽  
Author(s):  
Xiao-Liang Yang ◽  
Guo-Rong Liu ◽  
Nan-Hua Chen ◽  
Tai-Shan Lou
Keyword(s):  
Induction Motor ◽  
Kalman Filtering ◽  
Ensemble Kalman Filtering

scholarly journals Modeling transient groundwater flow by coupling ensemble Kalman filtering and upscaling

2012 ◽  
Vol 48 (1) ◽  
Author(s):  
Liangping Li ◽  
Haiyan Zhou ◽  
Harrie-Jan Hendricks Franssen ◽  
J. Jaime Gómez-Hernández
Keyword(s):  
Groundwater Flow ◽  
Kalman Filtering ◽  
Ensemble Kalman Filtering

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.

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.

scholarly journals Accelerating the spin-up of Ensemble Kalman Filtering

2010 ◽  
Vol 136 (651) ◽  
pp. 1644-1651 ◽  
Author(s):  
Eugenia Kalnay ◽  
Shu-Chih Yang
Keyword(s):  
Kalman Filtering ◽  
Ensemble Kalman Filtering

Ensemble Kalman filtering

2005 ◽  
Vol 131 (613) ◽  
pp. 3269-3289 ◽  
Author(s):  
P.L. Houtekamer ◽  
Herschel L. Mitchell
Keyword(s):  
Kalman Filtering ◽  
Ensemble Kalman Filtering

scholarly journals The 10,240-member ensemble Kalman filtering with an intermediate AGCM

2014 ◽  
Vol 41 (14) ◽  
pp. 5264-5271 ◽  
Author(s):  
Takemasa Miyoshi ◽  
Keiichi Kondo ◽  
Toshiyuki Imamura
Keyword(s):  
Kalman Filtering ◽  
Ensemble Kalman Filtering
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