Machine Parameter Variations Research Articles

Optimum active power tracking based control of brushless doubly-fed reluctance generator tied to renewable microgrid

Among all the existing wind energy generators (WEG), a brushless doubly-fed reluctance generator (BDFRG) is the better option in terms of maintenance cost and constructional simplicity. For more adaptability, the efficiency of the BDFRG can be improved through proper control mechanisms. Therefore, this paper presents an optimum active power tracking-based control technique for BDFRG for minimum losses. Copper loss of the machine is considered to be the objective function for the proposed optimization technique. However, one issue found to be the major in all the existing controllers is the accuracy of sensor less control of the BDFRG. Therefore, this paper proposes an accurate model reference adaptive system (MRAS) based sensor less mechanism for computation of BDFRG secondary winding flux position. The no sensitivity of the proposed technique to the machine parameter variation and hence the accuracy, catch-on-fly, and non-inclusion of integrator and differentiators and hence less burden on the processor makes the proposed scheme robust. The control scheme is practically implemented with a 2.5 kW BDFRG using MATLAB/Simulink platform.

Cross-domain tool wear condition monitoring via residual attention hybrid adaptation network

Intelligent models for tool wear condition monitoring (TWCM) have been extensively researched. However, in industrial scenarios, limited acquired monitoring signals and variations of machining parameters lead to insufficient training samples and data distribution shifts for the models. To address the issues, this research presents a novel residual attention hybrid adaptation network (RAHAN) model based on a residual attention network (ResAttNet) and a hybrid adaptation strategy. In the RAHAN model, by integrating a channel attention mechanism and deep residual modules, ResAttNet is designed as a feature extractor to acquire features from monitoring signals for tool wear conditions. Embedding subdomain adaptation into a condition recognizer while establishing separate adversarial learning in a domain obfuscator, the hybrid adaptation strategy is developed to eliminate global distribution shifts and align local distributions of each tool wear phase simultaneously. Six migration tasks under a laboratory and two factory machining platforms were conducted to evaluate the effectiveness of the RAHAN model. Compared with a baseline model, four ablation models, and six state-of-the-art transfer learning models, the RAHAN model achieved the highest average accuracy of 92.70% on six migration tasks. Furthermore, the RAHAN model shows clearer feature representations of each tool wear condition than other compared models. The comparative results demonstrate that the RAHAN model has superior transferability and therefore can be considered as a good potential solution to support cross-domain TWCM under different machining processes.

Fuzzy logic based vector control of multi-phase permanent magnet synchronous motors

This paper presents a fuzzy-logic-based vector control for a five-phase permanent-magnet synchronous motor (PMSM). The high-performance of the proposed fuzzy logic control (FLC)-based PMSM drive are investigated and compared with the conventional proportional-integral (PI) controller at different conditions, such as step change in command speed and load, and robustness against machine parameter variations. Finally, Simulation results shows that FLC presents better performances and high robustness compared to the conventional PI controller.

Assessment of a PLL‐ASMO position/speed estimator for sensor‐less control of rotor‐tied DFIG (RDFIG)

AbstractIn this paper, an adaptive sliding mode observer (ASMO) associated with a phase locked loop (PLL) is assessed for the sensor‐less control of a rotor‐tied doubly‐fed induction generator (RDFIG). In the proposed PLL‐ASMO estimator, the ASMO utilizes the stator current, the stator voltage, and the back electromotive force (EMF) as state variables. The proposed ASMO is used in order to estimate the back‐EMF from which the slip position/speed is extracted using a PLL. The design of the ASMO gains is based on the Lyapunov stability criteria to ensure the convergence of the proposed observer in a finite time. Therefore, the main contribution of this paper is to propose a PLL‐based ASMO estimator that aims to improve the estimation by reducing the chattering effect. A comparative study between the standard PLL‐SMO estimator and the PLL‐ASMO estimator is presented. Also, For the first time, an adaptive sliding mode observer is used for the sensor‐less control of a RDFIG. The performance of the proposed sensor‐less control strategy is validated through simulation and experimental measurements under various operating conditions. Furthermore, the estimator is shown to be robust to machine parameter variation.

Prediction of OES intensity ratios based on coating unit data in HPPMS processes by ANN

High power pulse magnetron sputtering (HPPMS) processes are characterized by high peak powers and peak voltages. This results in a high fraction of ionized metal ions within the coating plasma. In order to analyze the correlations between parameters of the coating unit and the intensities of the excited and ionized plasma species, optical emission spectroscopy (OES) can be used. In those experiments, several process parameters are varied in a single coating process. Currently, the prediction of plasma parameters based on coating unit data follows deterministic models which cannot describe the complexity in total. Therefore, not all correlations can be fully understood. Artificial neural networks (ANN) can be used to identify correlations between process parameters and plasma species. This enables prediction of OES data based on data of the coating machine. In the present study different coating processes containing the elements Al, Cr, Ti, N and O were investigated. Current and voltages of the cathodes, substrate bias, chamber pressure, gas flows and the target compositions were used as input parameter of the ANN. Time resolved OES data of metal and gas species were used as output data. To determine the most appropriate training algorithm for the current predictions, multiple algorithms were employed. A good prediction accuracy of OES intensity ratios based on coating unit data for industrial scale coating processes for TiAlN was obtained. For CrAlON the prediction of the intensity ratios of the gas species showed good results. Nowadays a high amount of coating machine parameter variations in physical vapor deposition processes is needed in order to achieve tailored coating parameters. By using plasma diagnostics, such as OES, cost intensive coating deposition processes can be reduced significantly. A further shortening of process development time is possible by using ANN, since plasma compositions can be determined based on coating unit data.

Probabilistic and Reliability Analysis of an Intelligent Power Control for a Doubly Fed Induction Generator-Based Wind Turbine System

The wind energy system's complicated characteristics have motivated many researchers to develop advanced and intelligent control strategies to ensure reliable and efficient system operation. Over the past few years, Artificial Neural Networks (ANN) have been widely employed in wind energy applications, and they are strongly recommended as a powerful and adequate tool for controlling wind turbine systems.The generator control block is a critical subsystem in the wind turbine chain, where a failed generator power control leads to an unstable operation process. The current work aims to verify the reliability of an ANN control of a DFIG integrated into a wind turbine system by considering the uncertainties in the machine parameters that affect the control's performance requirements. The major challenges encountered when applying reliability analyses are the system's high complexity and the expensive computational time required to achieve accurate results. In this context, the reliability assessment methodology adopted in this study combines Machine Learning techniques and advanced reliability approximation algorithms to optimize the computing time and ensure accurate results despite the system complexity.The obtained results demonstrate the reliability and effectiveness of ANN controllers in coping with machine parameter variations and maintaining the system at its optimal operating point. In addition, the results demonstrate the effectiveness of the reliability methodology implemented in this work by offering accurate approximations with a reasonable computing time.

Robust sensor-less sliding mode of second-order control of doubly fed induction generators in variable speed wind turbine systems based on a novel MRAS-ANFIS observer

The current study proposed a robust sensor-less sliding mode second-order based on a super twisting algorithm (STA-SMSO) approach using a new observer Model Reference Adaptive System-Adaptative Neuro-Fuzzy Inference System (MRAS-ANFIS). This model was applied to a doubly fed induction generator (DFIG) wind turbine running under variable wind speed and DFIG fed with a power voltage source without a speed sensor, while the control objective was used to regulate independently, the active and reactive power DFIG stator were decoupled by using the field-oriented control technique. Additionally, this process reduced the cost of the control scheme and the size of DFIG by eliminating the speed sensor (encoder). In order to improve the traditional MRAS, the MRAS-ANFIS observer was proposed to replace the usual PI controller in the adaptation mechanism of MRAS with an Adaptative Neuro-Fuzzy Inference System (ANFIS) controller. The estimation of rotor position was tested and discussed under varying load conditions in low, zero, and high-speed region. The results mentioned that the proposed observer (MRAS-ANFIS) presented an attractive feature, such as guarantees finite time convergence, good response on speed wind variations, high robustness against machine parameter variations, and load variations compared to the conventional MRAS observer and MRAS-Fuzzy. Hence, the estimated rotor speed converged to their actual value has the capacity for estimating position in deferent region (low/zero/high) of speed.

New contributions for speed observation of asynchronous motor fed by multilevel inverter

ABSTRACT Control fulfilment of asynchronous motors requires accurate feedback of speed information. Previously, this function was attributed to mechanical sensors, however, due to the limited use restrictions, scientists have developed variety of speed observation algorithms. These latter are distinguished by their simplicity and sensitivity to machine parameter variations as well as their robustness even in a wide speed range. This paper proposes three new structures of observers in order to achieve an efficient behaviour at low and high speed. The first is a combined scheme of sliding mode observer and model reference adaptive system, the second consists in switching between the two aforementioned observers according to the operating speed, while the third consists in switching from sliding mode observer to the combined estimator. Moreover, the overall control scheme performance is enhanced by using sliding mode principle in a direct torque structure to control the motor fed by NPC multilevel inverter. Besides, a sliding mode controller is inserted in the external loop to regulate speed and provide a stable torque. To shed light on the efficiency of the proposed contributions, a critical and comparative study of simulation results has been conducted.

A Comprehensive Examination of Vector-Controlled Induction Motor Drive Techniques

This paper introduces a comprehensive examination of vector-controlled- (VC-) based techniques intended for induction motor (IM) drives. In addition, the evaluation and critique of modern control techniques that improve the performance of IM drives are discussed by considering a systematic literature survey. Detailed research on variable-speed drive control, for instance, VC and scalar control (SCC), was conducted. The SCC-based systems’ speed and V/f control purposes are clarified in closed and open loops of IM drives. The operations, benefits, and drawbacks of the direct and indirect field-oriented control systems are illustrated. Furthermore, the direct torque control (DTC) method for IMs is reviewed. Numerous VC methods established along with microprocessor/digital control, model reference adaptive control (MRAC), sliding mode control (SMC), and intelligent control (in terms of fuzzy logic (FL) and artificial neural networks (ANNs)) are described and examined. Uncertainties in the IM parameter are a considerable problem in VC drives. Therefore, this problem is addressed, and some studies that attempted to provide solutions are listed. Magnetic saturation and core loss impact are mentioned, as they are important issues in IM drives. Toward demonstrating the strengths and limitations of various VC configurations, a few experiments were simulated via MATLAB® and Simulink® that show the influence of machine parameter variation. Efforts are made to supply powerful guidelines for practicing engineers and researchers in AC drives.

An SMC-MRAS Speed Estimator for Sensor-Less Control of DFIG Systems in Wind Turbine Applications

A sliding mode control-based model reference adaptive system (SMC-MRAS) estimator for sensor-less control of doubly fed induction generator (DFIG) systems in wind turbine applications is proposed in this paper. The proposed SMC-MRAS estimator uses the rotor current as a variable of interest. The proposed SMC-MRAS estimator has the advantage of being immune to machine parameter variations. The SMC parameters are designed using the Lyapunov stability criteria. The performance of the proposed SMC-MRAS estimator is validated using simulations in MATLAB/SIMULINK. A comparative study between the proposed SMC-MRAS estimator and the PI-MRAS estimator is also conducted to demonstrate the superiority of the proposed SMC-MRAS estimator.

Short-Time Adaline Based Fault Feature Extraction for Inter-Turn Short Circuit Diagnosis of PMSM via Residual Insulation Monitoring

Inter-turn short circuit (ITSC) is a common fault in electrical machines, which may result in further devastation to the whole winding and magnets. In this article, a method for real-time residual insulation capacity monitoring of ITSC fault in permanent magnet synchronous machines (PMSMs) is proposed, which is robust to speed/torque variation and independent of machine parameters and extra sensors. It is based on the theory that the ITSC fault initiates as an insulation deterioration among adjacent turns and the fault severity can be described by the residual insulation capacity being relevant to the 2nd harmonic component in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> -axis currents. Thus, a short-time Adaline-based harmonic extraction method is proposed for the separation of the needed 2nd harmonic component from time-varying current signals, being used as the fault indicator afterward. Finally, the performance of the proposed method is experimentally evaluated on a PMSM and shows quite good performance in fast-tracking and detection of faults in various stages.

Input-output Feedback Linearization Control for SM-PMSM

This paper introduces a velocity control strategy for Surface-Mounted Permanent Magnet Synchronous Motors SM-PMSM using exact linearization and input-output decoupling techniques, which are rooted in the principles of differential geometry. The primary aim of this control approach is to establish a static state feedback mechanism and to convert the nonlinear PMSM model into a linear, decoupled, and controllable system. Initially, the state model that represents the PMSM dynamics within the d-q reference frame is defined. Subsequently, the process of designing the control through linearization and input-output decoupling is outlined. Lastly, the synthesis of the compensator is grounded in the pole placement method, aiming to drive the direct current towards zero and ensure optimal torque operation. Simulation outcomes conducted on Matlab/Simulink demonstrate the efficacy of the speed control strategy, which is facilitated by a straightforward algorithm for practical implementation. However, it is inadequate against variations in machine parameters and load torque disturbances.

Enhanced Torque Estimation in Variable Leakage Flux PMSM Combining High and Low Frequency Signal Injection

Torque measurement/estimation in Variable Leakage Flux Permanent Magnet Synchronous Motors (VLF-PMSMs) is preferred for accurate control. Torque measurement systems are expensive, require extra room, add weight, can introduce resonances into the system and can be sensitive to electromagnetic interference. Alternatively, torque can be estimated, accurate knowledge of machine parameters being often required in this case, which for the case of VLF-PMSMs can be a serious drawback due to the large variation of machine parameters with the operating condition. This paper proposes a torque estimation method for VLF-PMSMs based on stator flux linkage estimation. Machine parameters involved in stator flux linkage estimation are obtained from the response of the machine to a pulsating high frequency current signal and a low-frequency and small-magnitude, square-wave current signal. Both signals are injected on top of the fundamental excitation without interfering with the normal operation of the machine.

A Position-Sensorless Control of Doubly Fed Induction Machine by Stator-Equation-Based Adaptive Reduced-Order Observer

A position-sensorless control is developed for doubly fed induction machines (DFIM). In this article, an adaptive reduced-order observer is used to estimate both rotor speed and position. The proposed reduced-order observer is based on the stator equation, and four issues are explored. First, the stator equation is used to realize the adaptive reduced-order observer, by which the structure is simple without flux calculation. Second, the stability of adaptive reduced-order observer is analyzed by the linearization method, which paves the way for design procedure of observer's feedback gain. Third, the tracking performance of the rotor-position estimation is investigated through the acceleration/deceleration of rotor speed; an explicit guideline is provided to design the adaptation integral gain to satisfy the specified tracking response. Fourth, sensitivity to machine's parameter variations is studied; the robust stability property is enhanced by the observer's feedback gain; and the robust sensitivity to inductance variation is strengthened by the adjustment of mutual inductance with stator voltage accordingly. Experiment is conducted with a 4-kW DFIM drive system to verify the validation of the proposed theoretical concepts.

Second Harmonic Seamless Splicing Technique Based on Maximum Active-Voltage Vector for Online MTPA Tracking Control of SynRM

Maximum-torque-per-ampere (MTPA) control strategy is widely employed to achieve the optimal efficiency control of synchronous reluctance machines (SynRMs). However, the control efficiency of the conventional method that the optimal current distribution angle is directly calculated based on the knowledge of the machine parameters is seriously affected by the highly nonlinear and time-varying characteristics of SynRMs. Therefore, in order to effectively resolve this problem, a novel MTPA control scheme based on the second harmonic splicing technique is proposed in this article. Differing from the conventional method, the proposed solution utilizes the transient response current slopes of fundamental maximum active-voltage vector to splice a specific second harmonic signal that contains the optimal current distribution angle. After selecting two available and different splicing coefficients in each sector to guarantee the continuity of the spliced second harmonic signal, the optimal current distribution angle can be obtained accurately without any machine parameters through a simple signal demodulation, which significantly enhances the robustness to machine parameter variations. Furthermore, the influence of rotor position acquisition errors caused by the position sensor's limited accuracy and installation misalignment is analyzed in detail. Finally, the experimental results demonstrate the effectiveness and feasibility of the proposed MTPA control scheme.

An accurate dynamical model of induction generator utilized in wind energy systems

Due to the advantages of a self-excited induction generator (SEIG), it plays a main role in sources of renewable energy, such as wind turbines (WT). The regulation of terminal voltage and frequency is poor under variable rotor speed and load conditions at stand-alone operation mode. The generator terminal voltage depends on the excitation capacitance which can be controlled by a capacitor bank and static voltage compensator. The dynamical model of the machine is described by differential equations in D-Q axes transformations of the synchronously rotating frame. Many models of analysis are proposed in the literature. In those models, several approximations are used to simplify the process of calculations, such as neglecting the iron core resistance, stray load resistance, stator and rotor leakage reactance, and magnetic saturation. In this work, a comprehensive dynamic model of the SEIG-WT is performed to analyze the system performance under transient and steady-state conditions. This dynamic model considers the effect of all machine parameters variation. New analytical formulas are used for to accurate calculation of minimum and maximum values of excitation capacitance and generator rotor cut-off and maximum speed. The dynamic model results are partially compared with experimental results, and accurate agreement is shown.

Sensor-less Monitoring of Induction Motor Temperature with an Online Estimation of Stator and Rotor Resistances Taking the Effect of Machine Parameters Variation into account

Sensor-less Monitoring of Induction Motor Temperature with an Online Estimation of Stator and Rotor Resistances Taking the Effect of Machine Parameters Variation into account

Robust Predictive Stator Current Control Based on Prediction Error Compensation for a Doubly Fed Induction Generator Under Nonideal Grids

Traditional model-based predictive current control (MBPCC) for a doubly fed induction generator (DFIG) is easily affected by machine parameter variations. Recently, a model-free predictive current control (MFPCC) was proposed to enhance the parameter robustness of DFIG systems by using the past current difference to predict the future current difference under the same switching state. However, this approach has the problems of high sampling frequency, variable switching frequency, and high steady ripples. To solve these problems, this article proposes a robust predictive stator current control (RPCC) method for DFIGs. Different from the prior MFPCC, the current error between the measured value and the predicted value is not only used in the stage of the stator current prediction, but also in the calculation of rotor voltage vector. Hence, the performance deterioration caused by machine parameter variations is significantly alleviated. Furthermore, a support vector machine can be combined with the proposed RPCC. In this way, the proposed method ensures strong parameter robustness and small steady-state power ripples. Finally, by recalculating the current reference value, the method can also achieve a good performance even under nonideal grids. The proposed RPCC is compared with MBPCC and MFPCC and its effectiveness is confirmed by the presented simulation and experimental results.

Machinability of Ti6Al4V as influenced by cutting velocity, tool feed and cutting depth

Due to its application versatility, machining of Ti6Al4V alloy has been considered an important topic in the field of industrial manufacturing. To avoid machining-induced detrimental effects on the environment, dry machining of this difficult-to-machine alloy is a trending subject of matter. Therefore, the present study intends to explore different machinability indices such as tangential cutting force components, temperature evolved at tool-tip and wear of uncoated carbide inserts under the variations of machining parameters. A detailed study on tool wear modes and micro-morphology characteristics of evolved chips are also carried out for the present experimental analysis.

Model Predictive Current Control With Online Parameter Estimation for Synchronous Reluctance Machine Controlled by High-Frequency Signal Injection Position-Sensorless

Accurate machine parameters and rotor position information are essential in vector-controlled motor drive systems. However, machine parameter variations by various factors such as the current and the temperature degrade the performance of vector control. Also, a position sensor such as an encoder and a resolver increases the drive system cost. This paper proposes model predictive current control (MPCC) with the online parameter estimation for synchronous reluctance machines controlled by a high-frequency signal injection position-sensorless method. This approach removes the need for accurate knowledge about the system and eliminates the need for the position sensor. The proposed method adopts a recursive least-square (RLS) to estimate the electrical machine parameters in real-time. The estimated parameters are used for the deadbeat continuous control set (CCS) MPCC and the position-sensorless control. The high-frequency signal injection method is modified to be suitable for the proposed CCS-MPCC method, ensuring stable operation in the low-speed regions. Simulation and experimental results are provided to verify the performance of the proposed control method.