Stator Currents Research Articles

An Improved Current-Source-Converter-Based Series-Connected Wind Energy Conversion System

The passive rectifier-based current source converter (CSC) used in the series-connected wind energy conversion system features low cost, high reliability, and simple control, but suffers highly distorted generator stator currents. A new CSC-based wind energy conversion system composed of a multi-phase generator, a passive rectifier, a modular medium-frequency transformer-based converter, and a CSC, is proposed. In addition, two versions of the proposed converter are developed for low- and medium-voltage turbine systems respectively. The operation principle is presented, controls are developed, and simulations and experiments are conducted. Both converters offer excellent generator stator current harmonics performance, while also retaining the advantages of existing CSC-based converters.

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.

Online Estimation of Three-Phase Induction Motor Parameters Using an Extended Kalman Filter for Energy Saving

In this paper, the online estimation of three-phase induction motor parameters using an extended Kalman filter for energy saving is proposed. The optimal value of the stator current on the d-axis is calculated to obtain the minimum power loss. Accurate motor parameters are required to calculate the optimal stator current value for energy saving. Hence, to estimate motor parameters in real time, an online estimator known as the extended Kalman filter is applied. The energy consumption results for the motor using the proposed approach (estimated parameters with extended Kalman filter) are compared with those obtained using the conventional approach and energy saving (fixed parameters without parameter estimation) approach. As revealed by the comparison results from implementation in a laboratory, the proposed approach can provide minimum power losses for the three-phase induction motor drive, and the maximum energy-saving percentage is 60.18% compared with using the conventional drive approach.

Exploring the effects of overvoltage unbalances on three phase induction motors: Insights from motor current spectral analysis and discrete wavelet transform energy assessment

This paper explores the diagnosis of faults related to 10 % and 20 % overvoltages in phase A of a three-phase induction motor. The study is based on numerical simulation results using MATLAB software. The primary objective of this research is to investigate the impact of such voltage unbalance on induction motors, principally on the three-phase stator currents and the electromagnetic torque. The results demonstrated that this voltage aberration causes significant harm to the motor. This is evidenced by the resulting highly unbalanced stator currents and oscillatory torque leading to subsequent mechanical disturbances.The study's second objective is to diagnose anomalies using Motor Current Spectral Analysis (MCSA), encompassing a comprehensive analysis of all three phases. The analysis proved to be effective in detecting such faulty circumstances. The direct current (Id) and the quadrature current (Iq) were also analyzed to reinforce the study. Providing an alternative perspective to explore the consequences of the imbalance. To enhance these findings and validate the previous analysis, the energy analysis based on Discrete Wavelet Transform (DWT) was applied to the three phases, Id and Iq currents.The assessment focused on energy analysis, enabled the detection of abnormal conditions, and demonstrated a noteworthy coherence with spectral analysis. The observed congruence provides evidence for the dependability and effectiveness of these methods in identifying faults, whether they are used in combination or separately.

Rotor speed estimation for half-broken bar detection in induction motors using Kalman filtering

This study addresses the early detection of half-broken bars in induction motors (IMs). Most research on fault detection focuses on current signature analysis because it can provide online and remote monitoring at a low cost. However, errors caused by spectral leakage around the fundamental frequency supply reduce its reliability for fault diagnosis at the incipient stages. The proposed detection technique is based on a spectral analysis of the motor speed. To detect a half-broken bar, measurements of stator voltages and currents are processed by an adaptive extended Kalman filter for system state estimation. Then, a spectral analysis is applied to the estimated motor speed to obtain its power spectral distribution. Finally, the fault harmonic amplitude at 2sfs is used as fault indicator for fault diagnosis. The proposed approach has three main advantages: (1) speed estimation is unaltered by spectral leakage around the main frequency supply, (2) the cost of speed sensors is avoided, and (3) the absence of connecting shaft couplings prevents corruption of speed data by mechanical interference. Numerical simulations and experimental results were carried out for the squirrel cage IM to provide encouraging validation of the proposed early fault detection technique.

ADRC and IFOC Control of a Sensorless Induction Motor Driven by a Multilevel Converter Using SVM Approach and PV Generators

This paper offers a system for an electric vehicle. It consists of digitally controlling an induction motor without using a speed sensor. The machine is powered by a five-level cascading H-bridge inverter. The SVM control principle is used to manage the status of the five-level inverter; this removes harmonics. The H-bridge inverter converter is powered by photovoltaic sources via a serial converter, using the maximum power point tracker control principle. This structure can also reduce shading losses. In the absence of a mechanical sensor, a dynamic model of the asynchronous machine is utilized with the state variables defined in the stator reference frame. The state vector consists of the components of the rotor flux and stator current. The article provides a comparison of two methods widely used on an induction motor drive. The adaptive model-reference system method and Luenberger observer are evaluated using an active control strategy to reject disturbances to minimize the impact of disturbances. The operating principles of each method are described, and the mathematical models of training systems are developed. Both methods provide a promise for high-speed estimate applications in simulation environments. The simulation results obtained show the correct operation of both observers. Perfect decoupling between the velocity and flow control loops is observed, taking into account any disturbances that may affect the system.

Control of DFIG-based microgrid and seamless transition from stator connected and disconnected modes

This study aims to ameliorate the contribution capability of doubly-fed induction generator (DFIG) to participate in standalone microgrid operation. The islanded microgrid consists of a solar photovoltaic array for solar energy conversion and battery energy storage in addition to DFIG-based wind energy conversion system. Using a simplified control approach, the study describes multi-mode operation of a DFIG-based AC/DC microgrid using a stator-side solid-state transition switch (SSTS). Using SSTS operation, the DFIG stator can be seamlessly disconnected and reconnected from the point of common coupling without interrupting power to the loads in the microgrid. Additionally, non-ideal AC loads can be handled efficiently without the need for computationally exhaustive approaches to enhance stator voltages and currents.

Research of static and dynamic characteristics of a process system ‘electric drive-fluid-handling machine-pipeline’

<p>The paper offers the obtained quantitative assessment of the performance and energy parameters of a process system composed of an asynchronous motor, a fluid-handling machine and a pipeline when using two methods of performance control, in particular, throttling and speed control methods. Taking into account nonlinearity of mathematical representation of fluid-handling machines and asynchronous drive motors, the starting conditions were analysed using nonlinear differential calculus. The calculations for the models were performed using the MATLAB software package. Transient profiles of flow and head, stator current, angular frequency and torque of an asynchronous motor were obtained at pump startup and control of pump capacity. It has been found that the developed mathematical model of a process system composed of an asynchronous motor, a fluid-handling machine and a pipeline allows obtaining quantitative estimation of the performance and energy parameters of the unit when using two methods of the pump capacity control. The use of frequency method allows to decrease the pump rotation speed and significantly reduce the power consumed by the unit and provide energy-saving mode of operation, the economic efficiency of which depends on the range of feed control.</p>

Higher performance enhancement of direct torque control by using artificial neural networks for doubly fed induction motor

Recently Direct Torque Control is widely appreciated compared to other conventional control methods due to its numerous advantages, notably its speed and precision. However, despite its qualities, it often encounters torque ripples that limit its operational effectiveness. These variations can be attributed to the use of hysteresis comparators, leading to variable frequency operation and undesirable speed overshoots. To address these challenges and enhance overall motor control, this article introduces a new approach based on neural networks. Direct Torque Control method is specifically designed for Doubly Fed Induction Motors and utilizes an Artificial Neural Network. Unlike conventional methods, this approach eliminates the need for speed controllers, commutation tables, and hysteresis comparators, thus providing a more integrated and efficient solution. Simulations conducted in the Matlab/Simulink environment have demonstrated the significant advantages of this approach with a higher performance enhancement. Not only were torque ripples reduced, but speed overshoot was completely eliminated. Furthermore, significant reductions in Total Harmonic Distortion values of stator and rotor currents were achieved, indicating an overall improvement in system performance.

Entropy-Based Methods for Motor Fault Detection: A Review.

In the signal analysis context, the entropy concept can characterize signal properties for detecting anomalies or non-representative behaviors in fiscal systems. In motor fault detection theory, entropy can measure disorder or uncertainty, aiding in detecting and classifying faults or abnormal operation conditions. This is especially relevant in industrial processes, where early motor fault detection can prevent progressive damage, operational interruptions, or potentially dangerous situations. The study of motor fault detection based on entropy theory holds significant academic relevance too, effectively bridging theoretical frameworks with industrial exigencies. As industrial sectors progress, applying entropy-based methodologies becomes indispensable for ensuring machinery integrity based on control and monitoring systems. This academic endeavor enhances the understanding of signal processing methodologies and accelerates progress in artificial intelligence and other modern knowledge areas. A wide variety of entropy-based methods have been employed for motor fault detection. This process involves assessing the complexity of measured signals from electrical motors, such as vibrations or stator currents, to form feature vectors. These vectors are then fed into artificial-intelligence-based classifiers to distinguish between healthy and faulty motor signals. This paper discusses some recent references to entropy methods and a summary of the most relevant results reported for fault detection over the last 10 years.

Effect of DC Link Capacitor on the Performance of Wind-Driven Double-Fed Induction Generator

In this paper, the starting performance of a double-fed induction generator (DFIG) operating at super-synchronous speed is investigated when varying the values of the DC link capacitor. The starting transients, the settling time, and the harmonics of the stator and rotor currents and voltages are investigated as a function of the DC link capacitor Cd. Curve fitting is applied to derive mathematical equations (polynomial) relating the variation of the stator current THD with the value of the DC capacitor. Similarly, polynomials are deduced to relate the THD of the rotor current and rotor voltage with the value of the DC link capacitor. These polynomials help to design the DC link parameters that lead to minimum current and voltage harmonics of the grid-connected DFIG. The minimum THD values of the stator current, rotor current, and rotor voltage are presented. These results are repeated at different rotor speeds to deduce the optimum Cd value within a wide speed range. Also, the effect of Cd on the DC link current and voltage ripples is demonstrated at different speeds.

Skewness Driven DFIG's DC Link Bus Short and Open Circuit Faults Diagnosis Using Rotor and Stator Currents

Skewness Driven DFIG's DC Link Bus Short and Open Circuit Faults Diagnosis Using Rotor and Stator Currents

Comparing Methods to Mitigate the Effect of Grid Voltage Sag and Frequency Variation on the Operation of Variable Speed Drives

In this paper, the effect of unsymmetrical grid voltage sag on the performance of a slip power recovery drive is studied, and two strategies to mitigate its effects are investigated. The first strategy consists of three phase parallel RL impedance connected to the rotor circuit. The second strategy consists of three phase LCL filter connected to the rotor circuit. A PI controller is tuned to test the drive at constant speed. Also, the effect of grid frequency drop on the drive performance is investigated with the same two protection strategies. The proposed systems are modeled with Matlab/Simulink and the simulation results are compared. The drive performance without any protection schemes are compared with its performance with the two proposed strategies. The compared results include the stator voltage and current, the rotor voltage and current, the DC link voltage and current, the electric torque, and the rotor speed.

Fault-Tolerant Control of a Triple Redundant PMA-SynRM for Minimum Torque Ripple

In this paper, a new fault-tolerant control (FTC) strategy based on minimum torque ripple is proposed for a triple redundant permanent magnet assisted synchronous reluctance motor under single-phase and double-phase faults. Different from removing the faulty modules (single six-phase (S6) operation or single three-phase (S3) operation), the proposed strategy makes full use of the healthy phase of the faulty modules. By precisely controlling the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d-q</i> axes currents of each module, the ripple of permanent magnet torque and reluctance torque can be suppressed simultaneously, thus achieving the minimum output torque ripple. Moreover, the ratio ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> ) of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d-q</i> axes current to the stator current can be determined under the constraint of minimum copper loss. In addition, by changing the ratio <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> , the proposed strategy can be combined with S6 or S3 strategy to achieve the minimum copper loss in a wide torque range. In summary, the proposed FTC strategy not only achieves the minimum torque ripple but also ensures the minimum copper loss. Finally, experiments are carried out to verify the accuracy and validity of the theory.

Using neural network super‐twisting sliding mode to improve power control of a dual‐rotor wind turbine system in normal and unbalanced grid fault modes

SummaryAccording to recent research work, increasing electric power generation is one of the significant advantages of the dual‐rotor wind turbine (DRWT) compared to the other types for the same wind speed. In this research work, a modified super‐twisting sliding mode control (STSMC) based on the neural network (NN) is suggested to regulate the stator powers of a DRWT‐based doubly‐fed induction generator (DFIG) in normal and unbalanced grid fault modes. The design of this strategy involves replacing the gains of conventional STSMC with the NN algorithm to enhance robustness, mitigate the impact of unbalanced grid voltage, and consequently improve the quality of the generated power of DRWT‐based DFIG. This forms the primary contribution of this work. The suggested strategy is compared with vector control (VC) and conventional STSMC in terms of reference tracking, power ripples, response dynamics, harmonic distortion of stator current, and the effect of an unbalanced grid fault. Finally, the utility and effectiveness of the designed controller are confirmed through computer simulations. Furthermore, when the grid is subjected to a 20% voltage drop, the results demonstrate that the suggested strategy reduced the total harmonic distortion (THD) value of the stator current by 12.92% compared to VC and by 9.29% compared to conventional STSMC.

Model Predictive Control of Counter-Rotating Motors for Underwater Vehicles Considering Unbalanced Load Variation

The propulsion system for underwater vehicles, driven by a counter-rotating permanent magnet synchronous motor (CRPMSM), can enhance the operational stability and efficiency of the vehicle. Due to the influence of complex underwater flows, the load imbalance of CRPMSM’s dual counter-rotating rotors may lead to severe issues of dual-rotor desynchronization rotation. Combining traditional vector control (VC) with master-slave control strategies can address the desynchronization problem when CRPMSM’s load changes. However, it results in significant speed fluctuations and a long transition time during the transition from load disturbance to synchronous rotation. This paper introduces a model predictive control (MPC) strategy to effectively resolve this issue. The incremental MPC model is established based on the mathematical model of CRPMSM in the dq coordinate system. The predictive control system forecasts the d- and q-axis components of stator currents for the next four control cycles. It selects the optimal control increments to minimize the cost function based on current predictions and different inverter voltage states. The obtained optimal d- and q-axis components of stator voltage are used to control CRPMSM under unbalanced load disturbances. Simulation results demonstrate that, compared to the VC strategy, CRPMSM utilizing the MPC strategy exhibits better dynamic performance with faster speed response and reduced torque fluctuations during load and speed variations.

Single open‐phase fault detection and tolerant control for wye‐connected three‐phase induction machine drives

SummaryThis paper proposes open‐phase fault detection (FD) and tolerant control strategies for wye‐connected three‐phase induction machines (WCTPIMs). The FD mechanism is based on the comparison between predicted and actual values of stator currents, and the control approach is derived from the field‐oriented control (FOC). In the proposed fault‐tolerant control (FTC) method, an asymmetrical current transformation is used for stator current components, and the conventional symmetrical voltage transformation is employed for stator voltage components. It is shown that using these transformations, a modified FOC can be employed for the faulty WCTPIM. The suggested scheme is able to control WCTPIM drives under normal and single open‐phase fault (SOPF) modes with minor adjustments. Experimental results achieved from a laboratory prototype confirm the possibility and good performance of the proposed open‐phase FD and tolerant control for the WCTPIM under different operating conditions.

Nonlinear Magnetic Model of IPMSM Based on the Frozen Permeability Technique Utilized in Improved MTPA Control

In this paper, the enhanced nonlinear magnetic model of the low voltage interior permanent magnet synchronous machine (IPMSM) is developed using the frozen permeability (FP) technique in finite element analysis (FEA) FEMM 4.2 software. The magnetic model is derived by obtaining flux saturation maps for a wide range of dq stator currents. Furthermore, the FEA FP technique accounts for the corresponding offset in the flux maps due to the excitation of the permanent magnets, and well as for fitting the coefficients for the curve-fitting procedure. In order to demonstrate the usefulness of the proposed magnetic model, a nonlinear control strategy based on the maximum torque per ampere (MTPA) optimal algorithm for IPMSM is employed. The magnetic model and the MTPA control strategy are validated through a variety of computer simulations based on FEMM 4.2 and MATLAB R2023a software, as well as on a real IPMSM electric vehicle (EV) traction drive experimental setup.

Diagnosis of Multiple Open-Circuit Faults in Three-Phase Induction Machine Drive Systems Based on Bidirectional Long Short-Term Memory Algorithm

Availability and continuous operation under critical conditions are very important in electric machine drive systems. Such systems may suffer from several types of failures that affect the electric machine or the associated voltage source inverter. Therefore, fault diagnosis and fault tolerance are highly required. This paper presents a new robust deep learning-based approach to diagnose multiple open-circuit faults in three-phase, two-level voltage source inverters for induction-motor drive applications. The proposed approach uses fault-diagnosis variables obtained from the sigmoid transformation of the motor stator currents. The open-circuit fault-diagnosis variables are then introduced to a bidirectional long short-term memory algorithm to detect the faulty switch(es). Several simulation and experimental results are presented to show the proposed fault-diagnosis algorithm’s effectiveness and robustness.

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.