Search

In this paper, the way to compensate for flux error caused by estimated stator resistance error will be suggested when PLPF is utilized as flux estimator, which is used for sensorless vector control of induction motor. For the highly efficient sensorless control, flux estimation method using PLPF has been widely used. To accurately obtain flux using PLPF, it is significant to estimate synchronous angular velocity. Therefore, the way to make use of PLPF's phase lag error has been suggested; however, to be more accurate, flux magnitude error should be seriously considered and compensated. Error of flux magnitude usually takes place because of estimation error of stator resistance. For this reason, this paper works out a way to make up for the flux magnitude error while using PLPF and it would be proved with simulations and experimental results.

In this paper, the improved stator flux estimator has been proposed to ensure a Programmable Low Pass Filter (PLPF) to act as a pure integrator for sensorless control of induction motors. In a conventional PLPF, the estimated synchronous angular speed is used as a cutoff frequency of a PLPF whose gain and phase compensator is determined on the assumption of the estimated synchronous angular speed being coincident with the actual one. However, if the synchronous angular speed is not exactly estimated, the gain and phase compensation will not be appropriate. To overcome the mentioned problem, this paper analyzes the relation between the synchronous speed error and the phase lag error of the stator flux. Based on the analysis, the synchronous speed error compensation scheme is proposed. For a sensorless start-up, the current model is used as the stator flux estimator at the standstill. Therefore, a stable transition between the voltage model and the current model is required. This paper proposes the simple transition method which determines the initial values of the voltage model and the current model at the transition moment. The validity of the proposed schemes is proved through simulation results and experimental results.

In this paper, a sensorless control of an interior permanent magnet synchronous motors (IPMSMs) is proposed using an Extended Kalman Filter (EKF) based on the extended rotor flux concept. Also, simple parameter estimation method is proposed to improve the performance of sensorless drive method. The IPMSM sensorless drive system has nonlinearity characteristics and parameters variation. To overcome two main drawbacks, the estimation of Extended rotor flux using the EKF is suitable for high performance because of its ability to perform state estimation for nonlinear systems that involve random noise environment. The simple parameter estimation method is also adopted to reduce deterioration of estimation of variables. The experimental results illustrate the validity of the analytical approach and the efficiency of the proposed method.

In this paper, the improved stator flux estimator has been proposed to ensure A Programmable Low Pass Filter (PLPF) to act as a pure integrator for sensorless control of induction motors. Conventional PLPF uses the estimated synchronous speed as a cut-off frequency, with the magnitude and phase compensator determined on the assumption of the estimated synchronous speed being coincident with the actual one. However, if the synchronous speed is not exactly estimated, the magnitude and phase compensation will not be appropriate. Therefore, this paper has analyzed the relation between the synchronous speed error and the phase lag error of the stator flux. Based on this analysis, the synchronous speed error compensation scheme is proposed. In addition, this paper proposes the flux error compensation scheme because there still remain errors in spite of the speed error compensation. The validity of the proposed estimator has been proven using the simulation results.

In this paper, a sensorless control of an interior permanent magnet synchronous motors (IPMSMs) is proposed using an Extended Kalman Filter (EKF) based on the extended rotor flux concept. Also, simple parameter estimation method is proposed to improve the performance of sensorless drive method. The IPMSM sensorless drive system has nonlinearity charac-teristics and parameters variation. To overcome two main drawbacks, the estimation of Extended rotor flux using the EKF is suitable for high performance because of its ability to perform state estimation for nonlinear systems that involve random noise environment. The simple parameter estimation method is also adopted to reduce deterioration of estimation of variables. The experimental results illustrate the validity of the analytical approach and the efficiency of the proposed method.

In this paper, the improved stator flux estimator is proposed to ensure a programmable low pass filter (PLPF) to act as a pure integrator for sensorless control of induction motor. The estimated synchronous angular speed is used as a pole of a PLPF whose gain and phase compensator is determined on the assumption of the estimated synchronous angular speed being coincident with the actual one. However, if the synchronous angular speed is not exactly estimated, the gain and phase compensation will not be appropriate. To overcome the mentioned problem, this paper analyzes the relation between the synchronous speed error and the phase lag error of the stator flux. Based on the analysis, the synchronous speed error compensation scheme is proposed. For a sensorless start-up, this paper utilizes the current model as a flux estimator at the standstill. When the current model is switched to the voltage model, large fluctuations in the stator flux and the motor speed can be caused. Therefore, the simple method for a smooth transition between two models is proposed. Validity of the proposed schemes is verified through both simulation results and experimental results.

There are many trials to provide an optimal route for minimum energy consumption in a wireless sensor network. Currently, however, the mechanisms to find minimum energy property graph (MEPG) do not properly take into account the efficiency in time and storage, the optimality in results, and the feasibility in real systems. In this paper, we propose an efficient and first optimal algorithm to find the MEPG, in which all minimum energy paths are included, not only significantly reducing its total number of edges, but also obtaining an optimal result in O(VlogV+E). We also develop power aware data-centric routing protocol characterized by minimum energy consumption and longer lifetime.