Measured Stator Currents Research Articles

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.

Current Sensor Fault Diagnosis of DFIG Wind Turbines Using an Extended Kalman Filter Observer: Experimental Validation

This article deals with the diagnosis of faults in rotor current sensors in wind systems based on the Doubly Fed Induction Generator (DFIG). An approach is developed to monitor rotor currents and identify faulty sensors, even during variable wind speeds. This method relies on the use of the Extended Kalman Filter (EKF) observer, which is based on the nonlinear model of the DFIG and a fault identification block in the abc reference frame. Firstly, the EKF estimates the rotor currents from stator current and mechanical speed measurements. Then, these estimates are processed in the localization block to precisely identify faulty sensors, whether it be single or multiple faults. This method has been implemented on a FPGA and has been experimentally validated. The experimental results confirm the effectiveness of this approach in monitoring rotor currents and identifying faulty sensors, in the case of single or multiple faults.

PMSM Inter-Turn Short Circuit Fault Detection Using the Fuzzy-Extended Kalman Filter in Electric Vehicles

To avoid damaging the motor and its surrounding equipment, detecting Inter-Turn Short Circuit (ITSC) faults in Permanent Magnet Synchronous Motors (PMSMs) applied in electric vehicles is a crucial task. In this paper, the detection of ITSC faults in stator winding for PMSMs is carried out by means of the Extended KALMAN Filter (EKF) algorithm combined with the Fuzzy Logic Estimator (FLE). To estimate the motor parameters, including the rotor position and speed, the EKF algorithm uses the measured stator currents and voltages beside the stator resistance, which is calculated in advance using fuzzy logic and fed to the EKF. The change behaviors of the estimated parameters were then used to detect short circuit faults in the PMSM. Using Matlab/Simulink, the proposed FL-EKF algorithm was implemented and tested on a faulty PMSM controlled by Field Oriented Control (FOC). The observation of a perfect estimation of the stator resistance through the simulation helps to precisely detect the failure, and that demonstrates the sensitivity and robustness of the proposed approach.

An improvement of direct torque controlled PMSM drive using PWM technique and kalman filter

The paper describes pulse-width-modulation (PWM) technique and Kalman filter (KF) process to improve performance of direct torque controlled permanent magnet synchronous motor (DTC-PMSM) drive. Performance of DTC methods are strongly affected by high stator current ripple. For lowering the ripple, high switching frequency space vector PWM and KF are utilized in the paper. Mathematical model of PMSM and calculations of important quantities of DTC applied to PMSM drive are presented in the first part. The second part shows computation process of space vector PWM and KF. Performance indices are utilized to evaluate the drive structures. Theorectical assumptions are validated via simulations with Gaussian noised stator current measurement.

An improvement of direct torque controlled PMSM drive using PWM technique and kalman filter

The paper describes pulse-width-modulation (PWM) technique and Kalman filter (KF) process to improve performance of direct torque controlled permanent magnet synchronous motor (DTC-PMSM) drive. Performance of DTC methods are strongly affected by high stator current ripple. For lowering the ripple, high switching frequency space vector PWM and KF are utilized in the paper. Mathematical model of PMSM and calculations of important quantities of DTC applied to PMSM drive are presented in the first part. The second part shows computation process of space vector PWM and KF. Performance indices are utilized to evaluate the drive structures. Theorectical assumptions are validated via simulations with Gaussian noised stator current measurement.

Analysis and Approximation of THD and Torque Ripple of Induction Motor for SVPWM Control of VSI

This article presents a harmonic analysis of the stator currents of a squirrel-cage induction motor fed by a voltage source inverter with PWM space vector control (SVPWM). The influence of PWM switching frequency and dead time (dead band) of controlled transistors on THD and electromagnetic torque ripple is shown. The aim is to determine the lowest switching frequency of the transistors for which the drive will operate correctly. Characteristics were determined as functions in the form of THD (fPWM), where the least square approximation was used for stator current measurements when the PWM switching frequency was changed. The approximations were realized for simulation and experimental results. To clarify the results, the operation of hardware PWM circuits in microcontrollers is analyzed.

Marine Current Turbine Multifault Diagnosis Based on Optimization Resampled Modulus Feature and 1-D-CNN

Blade and winding failure often occur in the marine current turbine (MCT), which is usually subject to marine biofouling effects. Realizing the accurate fault diagnosis of MCT is of great significance. This article proposes a novel fault diagnosis method to identify the fault states of MCT blades and windings by optimization resampled modulus feature (ORMF) and 1D-convolutional Neural Networks (1D-CNN). First, the raw stator current measurements are transformed into a modulus signal. Then, the ORMF is proposed to tackle the problem of faint characterization due to various marine currents. Afterward, the 1-D CNN model is adopted to learn the deeper features of the resampled modulus to conduct fault classification. The prediction accuracy of the proposed method is 99.86% for MCT prototype data sets. Meanwhile, experimental results demonstrate the effectiveness of the proposed method in MCT fault diagnosis, highlighting the superiority of our introduced framework.

Adaptive Linear Predictive Model of an Improved Predictive Control of Permanent Magnet Synchronous Motor Over Different Speed Regions

Model predictive control (MPC) strategy can provide significant benefits for controlling nonlinear systems over classical cascade field-oriented control (FOC). However, the MPC is still in the development stage for a high-performance predictive model. Therefore, the proposed MPC in this article updates the internal linear predictive model at each time step to accurately represent the nonlinear plant of a permanent-magnet synchronous motor (PMSM) plant over different speed regions. In other words, the adaptive discrete linear plant model (ADLPM) is designed to update the current operating conditions of the machine parameters and the equilibrium points of the measured stator currents, speed, and load torque. For the operation in the flux-weakening region, the proposed MPC depends on a performance control algorithm (PCA) to obtain high dynamic performance. In this PCA algorithm, the proposed MPC depends on the modified reference velocity rather than the original reference velocity, which can calculate the required <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -axis cur <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</u> ent directly. Moreover, the proposed cost function is designed directly in terms of the error values of the velocity and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -axis current, which fits the motor performance based on the further constraint of the maximum magnitude of the drawn stator current provided to control the acceleration of the rotor. Finally, comprehensive simulations and experiments have fully demonstrated that the proposed MPC can reduce the speed drop, and torque ripple in response to those of the FOC and traditional MPC strategies.

An observer design for the flux of line start permanent magnet synchronous motors

The permanent magnet flux is an important model parameter that determines the operation of the line start permanent magnet synchronous motor. It is degraded with operating time due to the permanent magnet demagnetization which is principally caused by the armature magnetic field interaction and operating temperature. This paper introduces an observer design study aimed at estimating the permanent magnet flux of the line start permanent magnet synchronous motor from the measurement of stator currents. Derived from the mathematical description in the dq0 reference frame, a model of the motor with state variables of stator currents, magnetic fluxes and rotor angular frequency is considered. Model analysis and with a new flux variable setting allow an estimation of the rotor flux through the open loops from the stator currents. The observer is then designed with a constant gain for the system with states of stator currents and permanent magnet flux. Simulation confirms the efficiency and robustness of the designed observer.

Filtered X least mean fourth‐driven intelligent control for power quality augmentation and dynamic stability reinforcement of grid intertie wind–photovoltaic systems

SummaryAn advanced topology comprising a wind‐turbine driven synchronous generator (SG) and solar photovoltaic (PV) array to address the power quality (PQ) concerns and the uncertainty in renewable energy generation is proposed in this paper. The fluctuations in wind speed and insolation are soaked up by utilizing successively interfaced power electronic converters (PECs), namely, SG side converter (SGC) and utility grid side converter (UGC) integrated with a DC link capacitor and a PV array. SGC is controlled by estimating the rotor position from the stator voltage and current measurement data and maintains unity power factor (UPF) at stator terminals. UGC is controlled by employing filtered X least mean fourth (FXLMF) control, which ensures superior convergence performance and reduced computational complexity. Besides, it performs operations like reactive power compensation, enhancement of PQ, load and power balancing during steady‐state and dynamic conditions. The notion of optimal fuzzy reasoning is instilled which generates a fuzzy scheme with inherent optimal‐tuning characteristics to bring about considerable improvement in system performance. A water cycle algorithm (WCA) tuned fuzzy logic controller (FLC) is employed for dynamic stability enhancement leading to the generation of accurate loss component, , which bolsters the ability of VSC to accurately extricate weight signals and the fundamental load current component. This improves regulation of DC link voltage during dynamic load, insolation and wind conditions. The veracity of the proposed model is verified under wind gusts and the bell‐shaped irradiance curve. Extensive simulation results obtained in MATLAB corroborate the efficacy of the system.

Type‐2 Fuzzy Logic controller‐based stator current Model reference adaptive system speed observer for a hybrid electric vehicle to improve transient response during limp home mode

SummaryIn this paper, a stator current‐based model reference adaptive system (SCMRAS) for indirect vector control of induction motor fed to hybrid electric vehicle (HEV) for improving the transient response during limp home period has been proposed. In the proposed SCMRAS, the measured stator currents are employed in voltage model to eliminate the integrator in reference model. The stator currents are estimated and are compared with actual current components to estimate the rotor speed. Further, to improve the performance of SCMRAS during limp home period, the PI controller in the adaptation mechanism is replaced with type 2 fuzzy logic controller (T2FLC). The prototype model of the proposed SCMRAS using dSPACE DS 1104 R&amp;D controller board has been developed for implementing speed sensorless indirect vector control of induction motor drive. The performance of SCMRAS and proposed SCMRAS using T2FLC estimators during limp home period is compared.

A new Bernard–Praly-like observer for sensorless IPMSMs

A slight extension of the global Bernard–Praly gradient adaptive observer – originally presented for nonsalient-pole surface Permanent Magnet Synchronous Motors (PMSMs) and recently proved to own local exponential convergence properties under well-known observability conditions – is here designed and proposed for Interior Permanent Magnet Synchronous Motors (IPMSMs) (with relatively small saliency) in the case in which the q-axis current is constant. It guarantees local exponential estimation – from stator current and voltage measurements – of both the stator fluxes and the sinusoidal/co-sinusoidal functions of the motor electrical angle, with no modification of the previous persistency of excitation condition requirements. Such an extension complements – at least locally – a previous analysis concerning IPMSMs under constant d-axis current. Applications to the sensorless control of IPMSMs (with realistic simulations) are included.

Virtual Sensor Using a Super Twisting Algorithm Based Uniform Robust Exact Differentiator for Electric Vehicles

The highly efficient Interior Permanent Magnet Synchronous Motor (IPMSM) is ubiquitous choice in Electric Vehicles (EVs) for today’s automotive industry. IPMSM control requires accurate knowledge of an immeasurable critical Permanent Magnet (PM) flux linkage parameter. The PM flux linkage is highly influenced by operating temperature which results in torque derating and hence power loss, unable to meet road loads and reduced life span of electrified powertrain in EVs. In this paper, novel virtual sensing scheme for estimating PM flux linkage through measured stator currents is designed for an IPMSM centric electrified powertrain. The proposed design is based on a Uniform Robust Exact Differentiator (URED) centric Super Twisting Algorithm (STA), which ensures robustness and finite-time convergence of the time derivative of the quadrature axis stator current of IPMSM. Moreover, URED is able to eliminate chattering without sacrificing robustness and precision. The proposed design detects variation in PM flux linkage due to change in operating temperature and hence is also able to establish characteristics of fault detection. The effectiveness and accuracy in different operating environments of the proposed scheme for nonlinear mathematical IPMSM model with complex EV dynamics are verified thorough extensive simulation experiments using MATLAB/Simulink.

Enhanced Direct Power Control Strategy of a DFIG-Based Wind Energy Conversion System Operating Under Random Conditions

The main objective of this paper is the performances analysis of an Enhanced Direct Power Control (EDPC), applied to Doubly Fed Induction Generator (DFIG) driven by variable speed Wind Turbine (WT). This control strategy uses hysteresis regulators and switching table for active and reactive powers control. These latter are estimated using rotor currents and grid voltages instead of a traditional measurement of stator currents. In addition, the EDPC switching table is based on the position of the rotor flux instead of the stator flux in order to have better regulation accuracy because the rotor voltage vector directly influences the rotor flux and has a proportional relationship with the active and reactive powers. All the operating modes (sub-synchronous, super-synchronous, synchronous and over-speed) of the variable speed WT-DFIG system and the possibility of local reactive power compensation are reported and discussed in this paper. Depending on the operating zone of the WT, Maximum Power Point Tracking (MPPT) technique and pitch angle control are considered to optimize the wind energy efficiency. The validation of the proposed EDPC strategy has been performed through simulation tests under MATALB/Simulink, the obtained results show robustness and good performances with low THD of the generated currents.

Some Permanent Magnet Synchronous Motor (PMSM) Sensorless Control Methods based on Operation Speed Area

This paper compares some sensorless Permanent Magnet Synchronous Motor (PMSM) controls for driving an electric vehicle in terms of operating speed. Sensorless control is a type of control method in which sensors, such as speed and position sensors, are not used to measure controlled variables. The controlled variable value is estimated from the stator current measurement. Sensorless control performance is not as good as a sensor-based system. This paper aims are to recommend a control method for the PMSM sensorless controls that would be used to drive an electric vehicle. The methods that we will discuss are divided into four categories based on the operation speed area. They are a startup, low speed, high speed, and low and high-speed areas. The low and high-speed area will be divided into with and without switching. If PMSM more work at high speed, the most speed area that is used, we prefer to choose the method that works at high speed, that is, the modification or combination of two or more conventional methods.

Common approaches in vector control design of induction motor and permanent-magnet syncronous motor

The article describes a way to generalize current control loop structure for AC drives, by implementing general PI gains calculation block. Article contains block diagram of generalized current control loop for smooth-air-gap and salient-pole machines. Electromagnetic torque estimator is represented, whose design based on stator voltage and current measurements. Results of simulation modeling of electromagnetic torque estimator in pair with field-oriented control are represented.

Performance Analysis of a Full Order Sensorless Control Adaptive Observer for Doubly-Fed Induction Generator in Grid Connected Operation

This paper focuses on the performance analysis of a sensorless control for a Doubly Fed Induction Generator (DFIG) in grid-connected operation for turbine-based wind generation systems. With reference to a conventional stator flux based Field Oriented Control (FOC), a full-order adaptive observer is implemented and a criterion to calculate the observer gain matrix is provided. The observer provides the estimated stator flux and an estimation of the rotor position is also obtained through the measurements of stator and rotor phase currents. Due to parameter inaccuracy, the rotor position estimation is affected by an error. As a novelty of the discussed approach, the rotor position estimation error is considered as an additional machine parameter, and an error tracking procedure is envisioned in order to track the DFIG rotor position with better accuracy. In particular, an adaptive law based on the Lyapunov theory is implemented for the tracking of the rotor position estimation error, and a current injection strategy is developed in order to ensure the necessary tracking sensitivity around zero rotor voltages. The roughly evaluated rotor position can be corrected by means of the tracked rotor position estimation error, so that the corrected rotor position is sent to the FOC for the necessary rotating coordinate transformation. An extensive experimental analysis is carried out on an 11 kW, 4 poles, 400 V/50 Hz induction machine testifying the quality of the sensorless control.

A new simplified fundamental model‐based sensorless control method for surface‐mounted permanent magnet synchronous machines

For sensorless control of surface-mounted permanent magnet synchronous machines (SPMSMs), the major issue is in zero- and low-speed ranges. Since back-electromotive force (EMF) is proportional to speed, back-EMF based methods fail at zero and low speed. A solution considering the starting process and low speed sensorless control is presented. A simplified fundamental model-based method is proposed. Based on the simplified model, the measured stator currents in the stationary reference frame can be directly utilised for position estimation so that the sensorless control performance at low speed and starting is improved. Moreover, with the knowledge of rotor initial position sector information, a stable and reliable starting performance is achieved with the proposed method. The effectiveness of the proposed method is verified through experimental results.

Current and Speed Sensor Fault Diagnosis Method Applied to Induction Motor Drive

The paper proposes a novel approach based on a current space vector derived from measured stator currents to diagnose speed and current sensor failures in the field-oriented control of induction motor drives. A comparison algorithm between the reference and measured rotor speed is used to detect the speed sensor faults. A counter is added to eliminate the influence of the encoder noise in the diagnosis method. In this approach, estimated quantities are not used in the proposed speed sensor fault diagnosis strategy, which increases the independence between the diagnosis stages in the fault-tolerant control (FTC) method. Moreover, in order to discriminate between the speed sensor faults and the current sensor faults, a new approach combining the current space vector and a delay function is proposed to reliably determine the current sensor failures. The MATLAB-Simulink software was used to verify the idea of the proposed method. Practical experiments with an induction motor drive controlled by DSP TMS320F28335 were performed to demonstrate the feasibility of this method in practice. The simulation and experimental results prove the effectiveness of the proposed diagnosis method for induction motor drives.

Artificial neural network-based current control of field oriented controlled induction motor drive

A hysteresis current controller (HCC) is commonly used in high-performance AC motor drives to control the current directly. Recently, the predictive current controller (PCC) is also used as an alternative to the classical current controller for speed and torque regulation of induction motor (IM) drives. However, PCC has drawbacks of large flux and torque ripples, large total harmonic distortions (THDs) in current and voltage and dependency on parameters. This paper proposes current control with artificial neural network (ANN) for a field-oriented controlled induction motor (FOCIM) drive. The ANN has input current error between the reference and the measured stator currents. The output function of neuron is a hyperbolic tan (or tan-sigmoid) function to apply error Levenberg–Marquardt (L–M) back propagation as learning rule because of its fast convergence. The proposed method is based on a new approach in which hysteresis band is replaced by ANN comparator to improve the performance of the FOCIM drive. It minimizes torque ripples, flux ripples, voltage and current THDs over the existing HCC and PCC methods. The superiority of the proposed method compared to existing methods is established by simulation and experimental results.