Doubly-fed induction motor parameter identification method based on adaptive extended Kalman filtering

The state space model is built through the adaptive extended Kalman filtering algorithm, which solves the problems of low parameter recognition accuracy and noise sensitivity of double-feed induction motors, and realizes high-precision online parameter recognition and anti-interference ability, meeting the needs of dynamic update of motor parameters.

CN120357789AActive Publication Date: 2025-07-22NANJING NORMAL UNIVERSITY
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Patent Information

Application Number
CN202510857441.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-25
Publication Date
2025-07-22
Estimated Expiration
2045-06-25

AI Technical Summary

Technical Problem

In the prior art, the double-feed induction motor has low parameter identification accuracy, is noise sensitive and has insufficient anti-interference ability, making it difficult to achieve efficient online parameter updates.

Method used

Based on the adaptive extended Kalman filtering method, a state space model with the stator and rotor dq axis current as the state variables is built. Through the adaptive extended Kalman filtering algorithm, dynamic online identification of stator resistance, rotor resistance, stator inductance, rotor inductance and stator rotor mutual inductance is realized. Combined with the adaptive noise covariance adjustment mechanism, parameter identification accuracy and robustness are optimized.

Benefits of technology

It significantly improves the recognition accuracy of resistors and inductors, simplifies model complexity, effectively suppresses measurement noise interference, and realizes online identification and dynamic update of motor parameters.

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Abstract

The invention discloses a doubly-fed induction motor parameter identification method based on adaptive extended Kalman filtering, and aims to solve the problems of low doubly-fed induction motor parameter identification precision and integrity, sensitive noise and the like in the prior art. According to a dq-axis flux linkage equation and a voltage equation of the doubly-fed induction motor, a state space model with dq-axis current of a stator and a rotor as state variables is built, the dimension of the state space model is expanded, the full-parameter observability of resistance Rs and Rr and inductance Ls, Lr and Lm of the motor is judged by utilizing a self-adaptive extended Kalman filtering theory and combining a rank criterion, and the accuracy and accuracy of the state space model are improved. All-parameter identification of inductance and resistance of the motor is realized, and the robustness of the system is improved through a method of adaptively adjusting noise covariance. The parameter coupling influence can be effectively eliminated, and the identification precision is improved; the self-adaptive noise suppression mechanism enhances the anti-interference capability, and meets the dynamic on-line parameter identification requirements of the doubly-fed motor.
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