Xiangdong Sun

A Speed and Flux Observer of Induction Motor Based on Extended Kalman Filter and Markov Chain

To improve the performance of sensorless induction motor (IM) drives, an adaptive speed and flux estimation method based on the multiple-model extended Kalman filter (EKF) with Markov chain for IMs is proposed in this paper. In this algorithm, the multiple model EKF for speed and flux estimation is established, and the transition of the models obeys the Markov chain and the estimation value is obtained by mixing the outputs of different models in different weightings, and the calculation of the weighting is researched. Simultaneously, the transition probability can be continuously self-tuned by the residual sequence, the prior information is modified by the posterior information, and the more accurate transition among the models is obtained. Therefore, the proposed method improves the model adaptability to the actual systems and the environmental variations, and reduces the speed estimation error. The correctness and the effectiveness of the proposed method are verified by the simulation and experimental results.

An Adaptive Speed Estimation Method Based on a Strong Tracking Extended Kalman Filter with a Least-Square Algorithm for Induction Motors

To improve the performance of sensorless induction motor (IM) drives, an adaptive speed estimation method based on a strong tracking extended Kalman filter with a least-square algorithm (LS-STEKF) for induction motors is proposed in this paper. With this method, a fading factor is introduced into the covariance matrix of the predicted state, which forces the innovation sequence orthogonal to each other and tunes the gain matrix online. In addition, the estimation error is adjusted adaptively and the mutational state is tracked fast. Simultaneously, the fading factor can be continuously self-tuned with the least-square algorithm according to the innovation sequence. The application of the least-square algorithm guarantees that the information in the innovation sequence is extracted as much as possible and as quickly as possible. Therefore, the proposed method improves the model adaptability in terms of actual systems and environmental variations, and reduces the speed estimation error. The correctness and the effectiveness of the proposed method are verified by experimental results.

On-line identification methods of parameters for permanent magnet synchronous motors based on cascade MRAS

Motor parameters should be on-line estimated to realize precise control of PMSM in sensorless vector control system. In this paper, an on-line identification method for PMSM parameters based on cascade MRAS is proposed by analyzing the conventional MRAS. By means of Popovs hyper-stability theory, the model of motor parameters identification is built in synchronous d-q coordinates, and PMSM stator voltage, stator current and their errors are used to obtain the adaptive laws of motor parameters, and it is realizable to estimate rotor speed, stator resistance and rotor flux at the same time. The simulation results demonstrate the correctness and effectiveness of the proposed method.

A speed estimation method for induction motors based on Strong Tracking Extended Kalman Filter

A speed estimation method for induction motors based on Strong Tracking Extended Kalman Filter (STEKF) is proposed in this paper, implementing optimization of speed sensorless vector control. With this method, the fading factor is introduced into the covariance matrix of the predicted state, which forces the residual sequences orthogonal to each other and tunes the gain matrix online. The estimation error is adjusted adaptively, and the mutational state is tracked fast. The proposed method shows more robust against the model uncertainties or the time-varying parameter systems, and it has better tracking ability to the mutations and the slow changes. Therefore, the proposed method improves the model adaptability to the actual systems and the environmental variations, and reduces the speed estimation error. The correctness and the effectiveness of the proposed method are verified by the simulation and experimental results.

Research on stability performance of speed estimation for induction motors based on adaptive full-order observer

To assure the stability performance of the speed estimation in the speed sensorless induction motor drives, the design strategy of feedback gain matrix for an adaptive full-order observer is a necessary issue. In this paper, the major cause of instability phenomenon based on adaptive full-order observer is analyzed, and a novel design of observers feedback gain matrix is proposed to achieve the stability over the whole operating range especially in the low speed region, including the regenerating mode. Besides, the Popov hyper-stability theory is used to prove the stability improvement based on the proposed method. Some simulation results are given to demonstrate the effectiveness of the proposed solution.

A speed and flux estimation method of induction motor using fuzzy extended kalman filter

A speed and flux estimation method of induction motors using fuzzy extended kalman filter(FEKF) is proposed in this paper, which is used to make lower impact of time varied statistic of measurement noise. It reaches a better speed estimation accuracy of induction motors than the extended kalman filter(EKF). The proposed algorithm modifies the measurement noise covariance of extended kalman filter recursively by monitoring if the ratio between filters innovation and actual innovation is near 1, and chooses a fuzzy factor to make its noise model close to real noise model adaptively. The speed estimated error and the flux fluctuation of FEKF under gross external disturbance and unknown measurement noises are compared with EKF. Simulation and experimental results show that FEKF provides better performance and faster convergence than EKF under gross external error and unknown measurement noises.

Research on a Novel Three-Level PWM Rectifier

A novel three-phase three-level asymmetry-arm neutral-point-clamped switching mode PWM rectifier is proposed, the mathematical model and working mode of the rectifier are derived, and the fixed frequency control scheme is analyzed in detail. Two control loops are used in the proposed control scheme. In the outer control loop, a proportional integral voltage controller is used to regulate the DC-link voltage. A phase lock loop circuit is adopted to generate a sinusoidal waveform in phase with mains voltage to achieve power factor correction. In the inner control loop, a carrier-based current controller is used to track the line current command. The simulation results show that the harmonics is suppressed effectively, unit power factor is achieved, and bidirectional flowing of the energy is implemented