Resistance Online Identification Research Articles

Parameter Identification of Maritime Vessel Rudder PMSM Based on Extended Kalman Particle Filter Algorithm

To address the issue of system parameter variations during the operation of a maritime light vessel rudder permanent magnet synchronous motor (PMSM), an extended Kalman particle filter (EKPF) algorithm that combines a particle filter (PF) with an extended Kalman filter (EKF) is proposed in this paper. This approach enables the online identification of motor resistance and inductance. For highly nonlinear problems that are challenging for traditional methods such as Kalman filtering, this algorithm is typically a statistical and effective estimation method that usually yields good results. Firstly, a standard linear discrete parameter identification model is established for a PMSM. Secondly, the PF algorithm based on Bayesian state estimation as a foundation for subsequent research is derived. Thirdly, the advantages and limitations of the PF algorithm are analyzed, addressing issues such as sample degeneracy, by integrating it with the Kalman filtering algorithm. Specifically, the EKPF algorithm for online parameter identification is employed. Finally, the identification model within MATLAB/Simulink is constructed and the simulation studies are executed to ascertain the viability of our suggested algorithm. The outcomes from these simulations indicate that the proposed EKPF algorithm identifies resistance and inductance values both swiftly and precisely, markedly boosting the robustness and enhancing the control efficacy of the PMSM.

Inductance–Resistance Online Identification for Sensorless High-Speed PMSM Considering Resistive Characteristic of Invertor Nonlinearity

Inductance–Resistance Online Identification for Sensorless High-Speed PMSM Considering Resistive Characteristic of Invertor Nonlinearity

Parameter Identification of Permanent Magnet Synchronous Motor with Dynamic Forgetting Factor Based on H∞ Filtering Algorithm

To address system parameter changes during permanent magnet synchronous motor (PMSM) operation, an H∞ filtering algorithm with a dynamic forgetting factor is proposed for online identification of motor resistance and inductance. First, a standard linear discrete PMSM parameter identification model is established; then, the discrete H∞ filtering algorithm is derived using game theory reducing state and measurement noise influence. A cost function is defined, solving extremes values of different terms. A dynamic forgetting factor is introduced to the weighted combination of initial and current measurement noise covariance matrices, eliminating identification issues from different initial values. On this basis, a dynamic forgetting factor is added to weigh the combination of the initial measurement noise covariance matrix and the current measurement noise covariance matrix, which eliminates the influence of the discrimination error caused by the different initial values. Finally, the identification model is built in MATLAB/Simulink for simulation analysis to verify the feasibility of the proposed algorithm. The simulation results show the proposed H∞ filtering algorithm rapidly and accurately identifies resistance and inductance values with significantly improved robustness. The forgetting factor enables quick stable recognition even with poor initial values, enhancing PMSM control performance.

Study on speed sensorless system of permanent magnet synchronous motor based on improved direct torque control

The traditional direct torque control system of permanent magnet synchronous motor has many problems, such as large torque ripple and variable switching frequency. In order to improve the dynamic and static performance of the control system, a new torque control idea and speed sensorless control scheme are proposed in this paper. First, by deriving the equation of torque change rate, an improved torque controller is designed to replace the torque hysteresis controller of the traditional direct torque control. The improved direct torque control strategy can significantly reduce the torque ripple and keep the switching frequency constant. Then, based on the improved direct torque control and considering the sensitivity of the stator resistance to temperature change, a speed estimator based on the model reference adaptive method is designed. This method realizes the stator resistance on-line identification and further improves the control precision of the system. The performance of the traditional direct torque control and the improved direct torque control are compared by simulation and experiment under different operating conditions. The simulation and experimental results are presented to support the validity and effectiveness of the proposed method.

Comparative Analysis of Two Speed-Estimation Methods for Dual Three-Phase Induction Motor with Stator Resistance Online Identification

The dual three-phase induction motor (DTPIM) has gained wide attention in special applications, such as vessel propulsion, because of its advantages of less torque ripple and higher reliability. However, speed sensors are greatly affected and easily become faulty when used in harsh environments for a long time. In this paper, two model reference adaptive system (MRAS) speed-estimation methods are proposed, based on the double (α, β) coordinate system (DCS) and vector space decomposition method (VSD) of the two groups of the three-phase armature vectors, respectively. Both methods can be used for the speed sensorless control system of the DTPIM to improve reliability. The changing of the stator resistance value, caused by temperature variation, affects the accuracy of the speed-estimation. Two online resistance-identification algorithms, combining the DCS method and the VSD method, were proposed to reduce the effect of changes in stator resistance. Simulation results show that the dynamic speed-estimation error of the VSD method decreased greatly compared with the DCS method, which verifies the effectiveness of the theoretical analysis.

Parameter Identification Based Online Noninvasive Estimation of Rotor Temperature in Induction Motors

This article proposes a rotor temperature estimation method for in-service induction machine (IM) based on parameter identification, which combines the advantage of recursive least squares (RLS) and model reference adaptive system (MRAS). The RLS with forgetting factor is firstly adopted to identify the parameters of motor inductances. Then, the online identification of rotor resistance can be realized via the MRAS based on the instantaneous reactive power (IRP) and the proportional integral (PI) regulation adaptive law designed by the Popov hyper-stable theory. Thereby, the rotor temperature is computed by the resistance-temperature relationship of metals. To achieve a fast convergence of the parameter identification, rotor slot harmonics are extracted from the stator current and used to determine the rotor speed in real time. Furthermore, to obtain a more accurate initial value of the stator and rotor leakage inductances and resistances, the first 5–15 cycles of the IM starting process are used to mimic the locked-rotor test condition. A merit of the proposed method is that it requires significantly less time to estimate the rotor resistance than the traditional methods. With a novel test bench, experimental validation is performed on a 22-kW IM. In the test, the rotor temperature is measured in three different ways: 1) wireless sensors inserted in the rotating rotor core and rotor end rings, 2) infrared sensor for rotor end ring temperature, and 3) PT100 installed in stator end winding. With this test bench, the real rotor temperature was revealed, and the effectiveness of the presented method is also verified.

Estimation of temperature in turbo-molecular pump based on motor resistance online identification

The temperature of the turbo-molecular pump (TMP) system has to be monitored in real-time for long stable operation in a high vacuum environment. Since the high-speed motor is the major heat source of the TMP system, the thermal sensors such as Pt100 are widely used to measure the temperature via being embedded in the motor stators. However, these thermal sensors require extra wires and signal conditioning circuits, which increase the complexity of installation. In order to solve this problem, a real-time online estimation method for the temperature of the TMP driven motor is proposed in this paper. First of all, the resistance identification model of the high-speed motor used in TMP system is built. After that, an improved recursive least squares (RLS) algorithm, which introduces a discount factor to overcome the problem of data saturation and weak dynamic tracking, is used to identify the resistance of the motor stator windings. Then, the temperature of the TMP could be estimated indirectly based on the linear relationship between the motor stator resistance and the temperature. At last, the proposed method is tested in a self-developed magnetically levitated turbo-molecular pump (MLTMP) system, and the experimental results verify its validity and reliability.

Second-Order Sliding-Mode MRAS Observer-Based Sensorless Vector Control of Linear Induction Motor Drives for Medium-Low Speed Maglev Applications

This paper presents a speed estimation scheme by combining a second-order sliding-mode observer (SMO) with the model reference adaptive system (MRAS) in the sensorless-vector-controlled linear induction motor (LIM) drives for medium-low speed maglev applications. A state space-vector model of the LIM considering the dynamic end effects is rearranged in order to be represented by the form of the super-twisting algorithm (STA). Then, an STA-SMO-based stator current observer of the LIM with Popov's hyperstability theory is designed, which is used to replace the reference model of the MRAS-based scheme. Correspondingly, the actual stator current model is regarded as the adaptive model of the MRAS for speed estimation. Meanwhile, because the variation of stator resistance associated with stator temperature may lead to a large estimated error and even system instability, a parallel stator resistance on-line identification scheme is adopted in the proposed speed estimation scheme to reduce the speed error and improve the stability of the sensorless control system. Compared to the speed estimation schemes based on the Luerberger observer with MRAS and the single-manifold SMO, the proposed speed estimation scheme exhibits better estimated performance. The effectiveness and feasibility of the proposed speed estimation scheme have been verified by simulation and hardware-in-the-loop tests.

Sliding‐mode observer‐based speed‐sensorless vector control of linear induction motor with a parallel secondary resistance online identification

This study proposes a speed estimation scheme for the sensorless-vector-controlled linear induction motor (LIM) drives for medium-low-speed maglev applications, which is composed of two parts: (i) a sliding mode model reference adaptive system observer for speed estimation; and (ii) a parallel secondary resistance online identification for achieving the improvements of the proposed speed estimation scheme performance. The sliding mode observer (SMO) is established on the basis of the state space-vector model of the LIM considering the dynamic end effect. Based on SMO, both speed and secondary resistance estimation algorithms are obtained by utilising Popov's hyperstability theory. Moreover, the Lyapunov stability theory is adopted for the stability analysis of the proposed speed estimation scheme. The effectiveness of the proposed speed estimation algorithm has been verified and compared with the performance of the conventional speed estimation scheme based on single-manifold SMO by the simulation and hardware-in-the-loop tests.

Sensorless control of salient PMSM with adaptive integrator and resistance online identification using strong tracking filter

This article presents a sensorless control approach of salient PMSM with an online parameter identifier. Adaptive Integrator is proposed and utilised for the estimation of active flux and rotor position. As a result, integrator overflow caused by DC offset is avoided. Meanwhile, an online stator resistance identification algorithm using strong tracking filter is employed, and the identified stator resistance is fed back to the estimating algorithm. Thus, the estimating algorithm can calculate the rotor position correctly. Simulations and experimental results validate the feasibility of both adaptive integrator and the parameter identification method.

Rotor Resistance Online Identification of Vector Controlled Induction Motor Based on Neural Network

Rotor resistance identification has been well recognized as one of the most critical factors affecting the theoretical study and applications of AC motor’s control for high performance variable frequency speed adjustment. This paper proposes a novel model for rotor resistance parameters identification based on Elman neural networks. Elman recurrent neural network is capable of performing nonlinear function approximation and possesses the ability of time-variable characteristic adaptation. Those influencing factors of specified parameter are analyzed, respectively, and various work states are covered to ensure the completeness of the training samples. Through signal preprocessing on samples and training dataset, different input parameters identifications with one network are compared and analyzed. The trained Elman neural network, applied in the identification model, is able to efficiently predict the rotor resistance in high accuracy. The simulation and experimental results show that the proposed method owns extensive adaptability and performs very well in its application to vector controlled induction motor. This identification method is able to enhance the performance of induction motor’s variable-frequency speed regulation.

Rotor Speed and Stator Resistance Identification of Direct Torque Control for Induction Motor Based on Mutual MRAS

Based on the dynamic mathematical model of induction motor, the mutual model reference adaptive system method (MRAS) for rotor speed identification is proposed to implement a speed sensorless direct torque control of induction motor. The model reference adaptive theory is flexibly used in the rotor speed and the stator resistance online identification. The reference model and adjustable model used in the mutual MRAS scheme are interchangeable. Therefore, The induction motor speed sensorless direct torque control system can obtain high-precision speed identification. Computer simulations and experimental results show that the method can solve the problems of speed control accuracy and system stability under the influence of motor parameter variation. The low speed performance of DTC is also improved.

Life-Cycle Monitoring and Voltage-Managing Unit for DC-Link Electrolytic Capacitors in PWM Converters

A novel life-cycle monitoring and voltage-managing device for dc-link electrolytic capacitors in pulsewidth modulation converters is presented. The system performs online identification of the capacitor's equivalent series resistance (ESR) in order to detect the life-cycle status and permit preventive maintenance. The ESR detection is based on the capacitor's ac losses calculated from voltage/current measurements using a simple low-cost microcontroller. The unit is designed as small printed circuit board located directly at the capacitor's screw terminals in order to simplify the required temperature sensing and to minimize wiring effort. The minimized energy consumption allows a power supply taken out of the capacitor to be tested. Besides life-cycle monitoring, the unit further facilitates energy-efficient voltage balancing for capacitors in series arrangements avoiding any power resistors typically used in balancing circuits. Instead, the unit controls the capacitor voltage by influencing its power consumption. Each individual monitoring unit (one per each power capacitor of the converter) transfers the ESR results to the converter's main controller and receives control commands via a common optoisolated fieldbus. Alternatively, this data transfer is performed using wireless near-field communication leading to a completely autonomous monitoring unit without any wiring for power supply and data transmission.

Rotor Speed Adaptive Identification of Direct Torque Control for Induction Motor

Based on the dynamic mathematical model of induction motor, the mutual model reference adaptive system method (MRAS) for rotor speed identification is proposed to implement a speed sensorless direct torque control. The model reference adaptive theory is flexibly used in the rotor speed and the stator resistance online identification. The reference model and adjustable model used in the mutual MRAS scheme are interchangeable. Therefore, the control system can obtain high-precision speed identification. Simulation results show the method can solve the problems of speed control accuracy and system stability under the influence of motor parameter variation. The low speed performance of DTC is also improved.

Performance Improvement of IPMSM Position Sensorless Vector Control System by the Online Identification of Stator Resistance and Permanent Magnet Flux

Performance Improvement of IPMSM Position Sensorless Vector Control System by the Online Identification of Stator Resistance and Permanent Magnet Flux