Flux linkage full-rank identification method for permanent magnet of PMSM

Abstract

The invention discloses a flux linkage full-rank identification method for a permanent magnet of a PMSM. According to the method, an unscented-Kalman-filter-algorithm-based parameter sensitivity analysis for the flux linkage identification precision of a permanent magnet of a PMSM is carried out; a permanent magnet flux linkage full-rank identification method of different rotating speed zones of a PMSM drive system is determined. With the unscented-Kalman-filter-algorithm-based permanent-magnet flux linkage full-rank identification method of different rotating speed zones of a PMSM drive system, influences on the permanent magnet flux linkage identification precision by the system measurement noises, PMSM parameter changing, and a degenerate-rank phenomenon of an identification model can be eliminated; linear error occurrence and complicated Jacobian matrix calculation during the state identification process of the traditional extended Kalman filter algorithm can be avoided; and the algorithm implementation difficulty can be reduce. Meanwhile, the number of to-be-identified parameters can be reduced at a high-speed operating zone in a PMSM drive system; the operand of the identification algorithm can be reduced; and reasonable consideration of the identification precision and the identification speed can be realized.

Description

Technical field [0001] The invention relates to the technical field of permanent magnet synchronous motors (PMSM), in particular to a method for identifying the full rank of the permanent magnet flux linkage of the PMSM. Background technique [0002] PMSM has the technical advantages of simple structure, low failure rate and high operating efficiency, and has gradually been widely used in industrial servo drives, new energy vehicles and other fields. However, for many application fields such as electric vehicles, PMSM has high power density, poor heat dissipation conditions, and complex operating conditions. It is mostly under the combined control of maximum torque current ratio and field weakening, and there is a strong armature response. Factors such as aging are prone to permanent magnets uniform demagnetization or partial demagnetization failure, resulting in a reduction in motor output torque and torque pulsation, which directly affects the torque control accuracy and operat...

AI Technical Summary

Problems solved by technology

Since surface-mounted permanent magnet synchronous motors mostly use i d =0 control, in order to realize ψ f , R s The synchronous full-rank identification requires injecting a d-axis disturbance current with reasonable amplitude and frequency, and does not consider the L during the operation of the PMSM drive system. s Variation vs. ψ f The influence of identification accuracy

For this reason, French scholar Underwood S J based on the embedded permanent magnet synchronous motor R s , L d , L q 、ψ f The different time scales of the four parameters are divided into fast-changing parameters and slow-changing parameters, and the least squares algorithm with two different time scales is used to realize the online identification of the above four parameters. Although this method can solve the low-rank problem of the identification model , but in order to ensure the convergence of the algorithm, it is still necessary to inject a disturbance current with reasonable amplitude and frequency on the d-axis to ensure the identification accuracy of the slow time scale least squares algorithm, and the identification results are easily affected by measurement noise

Artificial intelligence represented by evolutionary algorithm has a certain application in PMSM permanent magnet flux linkage identification due to its strong nonlinear processing ability, but how to reduce the calculation amount of its full-rank identification is still a key technical problem to be solved urgently

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