Stator Resistance Research Articles

Online Estimation of Stator Resistance in Induction Machines Using the Zero-Sequence Component of the Stator Current: Investigating Two Implementations

Online Estimation of Stator Resistance in Induction Machines Using the Zero-Sequence Component of the Stator Current: Investigating Two Implementations

Nonlinear Flux Linkage Observer with Model Reference Adaptive System for Improved Permanent Magnet Synchronous Motor Control

This paper addresses performance degradation in nonlinear flux linkage algorithms, arising from estimation errors in rotor flux linkage due to fluctuations in current and temperature. We introduce a parameter-adaptive flux linkage model using MRAS, which dynamically adjusts the rotor flux linkage, significantly minimizing estimation errors and improving control performance. When the rotor flux linkage of the motor undergoes sudden changes, the nonlinear flux linkage model shows a speed fluctuation of 5%, whereas the parameter-adaptive flux linkage model reduces the error to 0.6%. The algorithm demonstrates strong robustness when the stator resistance undergoes a sudden change. The control effectiveness under conditions such as load start and load mutation is excellent. Simulations and experiments demonstrate that the parameter-adaptive flux linkage model improves the estimation accuracy of the rotor position when motor parameters change.

Accuracy of Online Estimation Methods of Stator Resistance and Rotor Flux Linkage in PMSMs

Accuracy of Online Estimation Methods of Stator Resistance and Rotor Flux Linkage in PMSMs

Adaptive current decoupling control scheme based on online multi-parameter identification for high-speed permanent magnet synchronous motor in fuel cell

The precise control of centrifugal hydrogen compressor (CHC) driven by high-speed permanent magnet synchronous motor (PMSM) is the key to the stable and efficient operation of hydrogen fuel cell (HFC). In this paper, an adaptive current deviation decoupling control (CDDC) strategy based on variable step size affine projection algorithm (VSS-APAs) is proposed to solve the high-speed PMSM control problem caused by current cross-coupling and parameter perturbation under complex operating conditions. Firstly, the step size is dynamically adjusted by VSS-APA through a normalized gradient descent to respond to system fluctuations, achieving rapid parameter identification during dynamic changes and accurate tracking in steady states. Secondly, stator inductance, magnetic flux linkage, stator resistance, and disturbance torque are identified in two-time-scale based on the characteristics of parameter changes, overcoming the rank deficiency in motor mathematical equations. Finally, utilizing online-identified motor parameters, the gains of the CDDC are adjusted, and compensation is applied for back electromotive force (BEMF) and disturbance torque, thereby ensuring the control accuracy of the motor under parameter perturbations and system disturbances. Experimental results demonstrate that the proposed method enhances the responsiveness and accuracy of the control strategy through efficient parameters identification. Consequently, it guarantees high-efficiency and stable performance of the CHC across different working conditions, markedly advancing the overall functionality and dependability of hydrogen energy system.

Comparative analysis of uncontrollable angles in direct torque and stator flux control and rotor flux control strategies: A numerical and experimental study

AbstractThe uncontrollable angles (UAs) in direct torque control (DTC) algorithm is an important issue through which the effects of voltage vectors (VEs) on the magnetic flux and the torque are accurately determined. In this paper, a unique analysis of UAs is performed at different operating conditions, including parameters variations in two different strategies: Direct torque and stator flux control (DTC_SC) and (DTC_RC). Values of Those angles were accurately determined for wide speed, stator and rotor variations, and load changes. In addition, a detailed numerical comparison was performed in terms of these angles in the two strategies mentioned above for each operating condition. The comprehensive comparison showed the superiority of the DTC_RC strategy over its DTC_SC counterpart, being the maximum values of UAs in DTC_RC were 8°, 33°, and 21° versus 15°, 45°, and 38° in DTC_RC strategy for the following operations: Variable speed with variable stator resistance, variable speed with variable stator and rotor resistances, variable speed with variable load, respectively. MATLAB/Simulink results of the contributed analysis and comparisons were accomplished and validated. In addition, DS1103‐based experimental tests supported and verified the theoretical analysis.

Backstepping control of DFIG wind turbine system based rotor flux observer

This paper introduces a novel approach to controlling a wind energy system based on a doubly fed induction generator (DFIG) using backstepping control. The proposed control method allows for independent control of the active and reactive power of the stator. Unlike traditional methods that rely on vector control of the stator fluxes and neglect the stator resistance, this approach takes into account the nonlinearities and coupled nature of the system. By using a nonlinear model in the Park frame, the control is more accurate and closer to the behavior of a real machine. This method has yet to consider any estimation of the rotor flux model. Using a high-gain observer can resolve the issue of measuring rotor flux. The stability of the nonlinear observer is demonstrated through the application of Lyapunov theory. The simulation results indicate that the suggested control scheme has enhanced the dynamic performance of the system.

Multi‐parameter identification method of induction motor based on coupling and small signal injection

AbstractSensorless induction motor control has been widely applied to the rail transit field. However, achieving a safe stop of a train using electric braking without applying air braking has been an urgent problem to be solved. The current research only considers the stability of the speed identification in the low‐speed region and does not consider the impact of inaccurate parameters on the stability, which cannot ensure the stable braking and parking of the train under all working conditions. To address this problem, the coupling relationship between the motor speed and the stator resistance is used and an adaptive rate of them is designed based on the Lyapunov stability design law. In addition, aiming to reduce the torque ripple, a torque ripple elimination link is designed to cancel the torque ripple caused by the small‐signal injection. Experiments show that the proposed parallel identification strategy of speed, stator resistance, and rotor resistance can ensure the system operation stability in the low‐ and zero‐speed regions without increasing the torque ripple.

A non-intrusive technique for estimating the efficiency of low voltage three-phase induction motors

This paper introduces a non-intrusive technique based on the air–gap torque (AGT) method to estimate the efficiency of induction motors (IMs). The technique uses a 3rd-degree equation to calculate the sum of windage and friction losses (FWL) and rotor stray load losses (SLLr), considering the number of IM poles. IM speed is estimated using motor current signature analysis, and a new procedure to estimate stator resistance is presented. Efficiency is then determined by applying the estimated rotor speed, stator resistance, and the sum of FWL and SLLr in the AGT equations. This method requires only nameplate information, IM line voltages, and currents. Simulations and experimental tests for balanced, unbalanced, and harmonic feeding modes show the proposed technique’s estimated efficiency values are very close to measured values, unlike the AGT method. This demonstrates the proposed technique’s high accuracy, making it suitable for non-intrusive efficiency estimation of in-service IMs in industrial programs.

Control Strategy of Flywheel Energy Storage System for Improved Model Reference Adaptive System Based on Tent-Sparrow Search Algorithm

This study addresses speed sensor aging and electrical parameter variations caused by prolonged operation and environmental factors in flywheel energy storage systems (FESSs). A model reference adaptive system (MRAS) flywheel speed observer with parameter identification capabilities is proposed to replace traditional speed sensors. The proposed method uses reference and adjustable models to identify the stator resistance and permanent magnet flux (PM Flux) to mitigate the adverse effects of electrical parameter changes on control performance. The Tent chaotic mapping-improved Sparrow Search Algorithm (SSA) optimizes the Proportional-Integral (PI) controller parameters for the dual closed-loop and MRAS speed adaptation laws of the flywheel motor. Moreover, a self-switching parameter identification (SSPI) scheme, which constructs a cost function based on the current, parameter identification, and speed errors, is proposed to prevent inaccuracies in parameter identification. The MRAS observer selects the appropriate PI adaptive mechanism based on the error values, thereby enhancing identification accuracy. Simulink simulations show significant improvements in the rapidity and accuracy of the Tent-SSA optimized MRAS flywheel speed observer, enhancing the stability and robustness of the flywheel rotor. Experimental validation on a constructed FESS platform confirms the feasibility of this method.

A Hybrid Analysis of Brushless DC Motor

Brushless DC (BLDC) motors and derives have gained popularity because of numerous advantages. This motor application is achievable since permanent magnet (PM) technology has advanced. It has higher efficiency, reliability, and power with less maintenance due to the absence of brushes. A higher torque-to-mass ratio and long life are two of the most attractive characteristics making it appropriate for high-performance applications. This study describes the hybrid methodology for analyzing a three-phase, four-pole, 1500 W, brushless DC (BLDC) motor with an inner rotor. PI controllers and PWM in Matlab /Simulink, and the chopped current control in Maxwell 2d ae used. BLDC motor modeled by rotation machine export (RMxprt) analytical software and analyzed with a finite element method (FEM) Maxwell 2D software to calculate motor performance, such as torque, speed, and efficiency. The FEM results were verified by comparing them with the results taken from the Matlab/Simulink program and with the aid of the RMxprt software to supply some missing data. The motor torque and efficiency results from Maxwell 2D and Matlab show good agreement. A hybrid FEM-analytical approach is successfully applied to study a BLDC motor using three software packages RMxprt, Maxwell 2D, and Matlab/Simulink despite the lack of motor test data. The RMxprt program offers missing data like stator resistance, stator inductance, and torque constant. It is provided in the design sheet to help us with motor modeling. The successful adoption of the proposed hybrid FEM-analytical methodology will provide a good starting point for future BLDC motor research work

An improved voltage model based on stator resistance estimation for FOC technique in the induction motor drive

An improved voltage model based on stator resistance estimation for FOC technique in the induction motor drive

Improved control of permanent magnet synchronous motors using adaptive nonlinear control with Deadbeat Observer

Permanent Magnet Synchronous Motor (PMSM) control optimization is crucial for a variety of applications, including electric vehicles, industrial automation, and renewable energy systems (RESs). However, traditional control methods often struggle to adapt to the dynamic and non-linear nature of PMSMs, leading to sub-optimal performance. To address these challenges, this paper proposes a hybrid adaptive nonlinear control strategy with a deadbeat observer (DO) designed to improve the performance of PMSMs. This approach aims to improve the accuracy and robustness of the control while taking into account the system parameters variations and disturbances. Simulation tests comparing the proposed method with an adaptive nonlinear control (ANLC) are presented, highlighting its superior effectiveness in controlling PMSMs under varying load conditions and speed fluctuations. The proposed strategy achieves a maximum relative speed error around of 6% at 0.4 s under gradually varying load torque disturbances, which is better than the ANLC with 8%. Furthermore, under large speed variations, the suggested method maintains a maximum relative speed error of 0.82% at 0.85 s. Furthermore, the robustness assessment under system parameters variations, stator resistance, and inductance, shows extraordinary performances of the proposed scheme. These results highlight the effectiveness and robustness of the proposed strategy in achieving precise PMSM control while dynamically adapting to changing conditions. This research underscores the potential of our approach to advance key technologies, including electric vehicles, industrial automation, and renewable energy systems. By optimizing PMSM control, this strategy contributes to increased efficiency, reliability, and adaptability, facilitating broader adoption of electric propulsion systems and sustainable energy solutions.

Static voltage stability considering reactive power limit of salient synchronous generator and active power loss of exciter

With increasing load, it is difficult to maintain power system static voltage stability (SVS). The existing SVS analysis based on the reactive power limit (RPL) of the non-salient synchronous generator (SG) and ignoring the exciter’s loss is inaccurate. This paper proposes an SVS model with exact description to the SG. The novelties are: (1) Seeing that the SVS is deteriorated when the SG reaches the RPL, a solution to the RPL of the salient SG with different reactances along the direct and quadrature axis is newly proposed. (2) The active power loss of the exciter of the SG is newly included in the power flow and the SVS analysis. (3) By deriving the sensitivity of the L index with respect to the load, an index is newly defined to quantify the dependency of the SVS on the RPL. The simulation results show that the RPL of the salient SG considering the stator resistance is narrower. The active power loss of the exciter has more obvious impact on the power flow under heavier loading conditions. When the proposed index is positive, the RPL instead of the L index is more critical to maintain the SVS.

Novel Advanced Artificial Neural Network-Based Online Stator and Rotor Resistance Estimator for Vector-Controlled Speed Sensorless Induction Motor Drives

This paper presents a new approach for the online estimation of stator and rotor resistance of induction motors for speed sensorless vector-controlled drives, using feed-forward artificial neural networks with advanced adaptive learning rates. For the rotor resistance estimation, a neural network model based on rotor speed and stator currents is developed. The rotor flux linkages acquired from the voltage model are compared with the neural network model. The feed-forward neural network employs an adaptive learning rate as the function of the obtained error during training for quick convergence with minimal estimation error. A two-layered neural network model based on the stator voltage and current equations is developed for the stator resistance estimation. The d-q axes stator currents obtained from the developed model are compared with the acquired d-q axes stator currents. For the fast convergence with minimal estimation error, an adaptive learning rate as the function of error is adopted during training. Furthermore, the neural network estimates the induction motor’s speed. The simulation and experimental results justify that the developed algorithms track variation in the resistances quickly and precisely along with the speed as compared with the conventional constant learning rate algorithm, leading to reliable operation of the drive.

Analysis of low saliency ratio and torque characteristics of the fractional slot concentrated winding surface mounted motors

In recent years, fractional slot concentrated winding permanent magnet synchronous motors (FSCW PMSMs) have become a hotspot in the research field. Due to the unique inductance characteristics of the FSCW PMSM, a fast and accurate calculation of the d/q-axis inductance and saliency ratio is necessary. In this paper, a method is proposed to calculate the d/q-axis reactance of the FSCW SPMSM, which constructs the equivalent magnetic circuit model of the d/q-axis armature reaction flux separately, and the saliency ratio characteristics of the FSCW SPMSM were demonstrated. In addition, to meet the high requirements of the modern industries, especially in servo systems, accurate consideration of the effect of stator resistance on torque and electromagnetic performance is important and more applicable. According to the relationship between the vector parameter, the explicit expression of the d/q-axis currents that consider the stator resistance is obtained, and the prediction of load angle at maximum electromagnetic torque is achieved. Then, combined with the finite element method, the influence mechanism of stator resistance on the motor steady-state performance is revealed. Finally, the experimental data are compared with the calculation data, and the correctness of the models and analysis was verified.

Performance analysis of a robust MF-PTC strategy for induction motor drive

The load model-based conventional predictive torque control (PTC) strategy performs as a high-performance controller at transient and steady-state conditions. However, its performance is poor on the issue of parameters variation. A robust model-free PTC (MF-PTC) strategy has been proposed in this paper to overcome the aforementioned drawback. An auto-regressive exogenous (ARX) model instead of a load model has been used to establish a model-free controller. This model is usually formed based on their input-output transfer function. A recursive least square algorithm has been employed to estimate the unknown parameters of the ARX model. Then, an observable canonical state-space model uses those estimated parameters to achieve an accurate prediction of the control variables. The performance of the proposed scheme can be affected by the variation of resistance. A resistance estimator based on the model reference adaptive system observer has been applied to improve the robustness of the system against variation of the stator and rotor resistance, and inductance measurement uncertainty. Simulation results show that the proposed MF-PTC scheme is robust against parameters uncertainty and works well at transient and steady-state conditions.

Compensation of Current Sensor Faults in Vector-Controlled Induction Motor Drive Using Extended Kalman Filters

In electric drive systems, one of the most common faults is related to measurement equipment, including current sensors (CSs). As information about the stator current is crucial to ensure precise control of AC drives, such a fault significantly affects the quality and security of the entire system. For this reason, a modified extended Kalman filter (EKF) has been presented in this paper as an algorithmic solution to restore stator current in the event of CS failure. In order to minimize the impact of rotor and stator resistance variations on the quality of the estimation, the proposed model includes an estimation of the general coefficient of their changes. In contrast to solutions known in the literature, the presented model considers changes in both resistances in the form of a single coefficient. This approach allows us to maintain a low order of the estimator (fifth) and thus minimize the tendency to system instability and decrease computation burden. Extensive simulation tests have shown a significant improvement in the accuracy of stator current estimation under both motor and regenerating modes, a wide speed range (1–100%), and changes in motor parameters.

Sensorless Control With Adaptive Speed Observer Using Power Winding Information for Dual-Stator Winding Induction Starter/Generator

This paper presents a speed-senserless control strategy with an adaptive speed observer using power winding (PW) information for dual-stator winding induction starter/generator (DWIG), in order to improve speed estimation precision and starting-generating performances. Based on the principle of model reference adaptive system (MRAS), an improved rotor-flux-based reference model is proposed by utilizing the electromagnetic link between the control winding (CW) and PW, the PW voltages are used to estimate rotor flux without necessity of stator resistance and filter parameters. The improved speed observer is less parameter-sensitive and can reduce the estimation error, especially in low-speed starting process with load. The parameter design method of the adaptive mechanism of proposed speed observer based on stability analysis is also given. The distribution of dominant pole of the speed observer is modified to obtain good tracking ability. Based on the improved speed observer, a speed-sensorless starting-generating control strategy is investigated, in which the estimated speed is not only used in speed control in starting process but also in staring-to-generating switching process. The simulation and experimental results verify the validity of the proposed speed observer and the speed-sensorless control strategy.

Stability Analysis of LC-Equipped High-Speed Permanent Magnet Synchronous Motor System Considering Stator Resistance

Stability Analysis of LC-Equipped High-Speed Permanent Magnet Synchronous Motor System Considering Stator Resistance

ROBUST SENSORLESS SPEED CONTROL OF AN INDUCTION MOTOR DRIVE USING A SYNERGETIC APPROACH

This paper uses a model reference adaptive system (MRAS) to build an improved sensorless rotor-field oriented control (RFOC) induction motor (IM) drive in all speed ranges. However, in the presence of external disturbances and parameter variations, the linear tuning method of the Proportional Integrator (PI) controller in the MRAS adaptation mechanism degrades motor performance. An advanced MRAS-based synergetic technique is proposed to develop two suitable adaptation mechanisms that generate slip speed and stator resistance estimates. The reference and adjustable models are developed in the synchronous rotation coordinate system (d-q frame). The effectiveness of the proposed control algorithm was evaluated under various operating conditions using a specific sensorless IM benchmark and the MATLAB/Simulink software environment.