Squirrel Cage Research Articles

Performing Wind System with Rectifier with Near Sinusoidal Input Current

The RNSIC-1 (rectifier with near sinusoidal input currents converter), is the foundation of a wind generator system that is presented. The three inductances that make up a traditional RNSIC–1 converter are eliminated in the proposed arrangement, and the SCIG generator's leakage inductances fill their place. This is what makes it new. The wind system's role in the load currents is discussed. Lower power losses, smaller harmonic input currents, fewer EMI issues, excellent dependability, and cheaper costs are characteristics of the new wind system. This new wind system could be used for partial variable speed wind turbines (usually 70% to 100% synchronous speed) and lower hydro connector squirrel cage induction generators (SCIG).

Review of multilevel inverter for high-power applications

PurposeTraditional level inverter technology has drawbacks in the aspect of Total harmonic distortion (THD) and switching losses for higher frequencies. Due to these drawbacks, two-level inverters have become unprofitable for high-power applications. Multilevel inverters (MLIs) are used to enhance the output waveform characteristics (i.e. low THD) and to offer various inverter topologies and switching methods.Design/methodology/approachMLIs are upgraded versions of two-level inverters that offer more output levels in current and voltage waveforms while lowering the dv/dt and di/dt ratios. This paper aims to review and compare the different topologies of MLI used in high-power applications. Single and multisource MLI's working principal and switching states for each topology are demonstrated and compared. A Simulink model system integrated using detailed circuit simulations in developed in MATLAB®–Simulink program. In this system, a constant voltage source connected to MLI to feed asynchronous motor with squirrel cage rotor type is used to demonstrate the efficacy of the MLI under different varying speed and torque conditions.FindingsMLI has presented better control and good range of system parameters than two-level inverter. It is suggested that the MLIs like cascade-five-level and NPC-five-level have shown low current harmonics of around 0.43% and 1.87%, respectively, compared to two-level inverter showing 5.82%.Originality/value This study is the first of its kind comparing the different topologies of single and multisource MLIs. This study suggests that the MLIs are more suitable for high-power applications.

Classification of inter-turn short-circuit faults in induction motors based on quaternion analysis

Short-circuit in three-phase engines are highly destructive faults, which overheat and damage internal elements reducing efficiency and lifetime. New multi-class approaches are best trained with measurements from three-phase motors instrumented with short-circuit faults because it offers natural and physical signal behavior. This work overcomes the lack of datasets by acquiring current signals from an instrumented induction motor to create a dataset of inter-turn short-circuit (ITSC) faults at four levels per phase. The dataset generated consists of 13 categories with five repetitions per trial for a squirrel cage motor induction. The proposed classification method is based on quaternions that simultaneously model the three-phase signals as pure quaternions. Three statistical features are extracted from quaternions, and a decision tree classifier is trained per feature. Thereby, a boosting scheme is used to calculate the resulting category. Boosting method improves the classification results of decision tree models, showing fast, accurate, and robust performance.

Artificial Neural Network-Based Experimental Investigations for Sliding Mode Control of an Induction Motor in Power Steering Applications

Sliding mode control (SMC) of induction motor is a new concept in the current scenario, as it seeks to improve torque control accuracy and power steering efficiency through the use of pulse width modulation schemes. Furthermore, artificial neural network-based sliding mode control is applied to a squirrel cage induction motor, which is used in the steering control of automobiles. The artificial neural network (ANN)-based SMC is more popular due to its robustness and good stability in external parameter variation. Additionally, an SMC and an ANN-based SMC are employed to compute the torque and flux, improving performance for power steering applications. The performance of the designed model is validated through MATLAB/Simulink and experimental models with different controllers under various operating conditions. The controller has been embedded into a TMS320F28335 controller, and performances have been evaluated. The performance analyses of induction motor using different controllers are performed. The transient performances of induction motor such as delay time, rise time, settling time, and steady-state error are investigated. The proposed work is analysed by using a mathematical model and implemented in a test-bench model for validation.

Sliding Input Output Control of Squirrel Cage Motor Based NPC Five Level Inverter

Speed squirrel cage motor control is an area of research that has been in evidence for some time now. In this paper, a nonlinear controller is presented for the squirrel cage motor drives, based on a combination between input-output feedback linearization control (IOLC) technique and sliding mode control (SMC) to create a new control which is sliding input-output linearization (SIOLC) control of squirrel cage motors which associates by an NPC five levels inverter PWM technical used for the variation of its speed, where the sliding mode control it used for controlling the speed of squirrel cage motor and the input-output linearization control applied for two input witch are flux and current. To test robustness and performance of sliding input-output linearization control (SIOLC) we created a variation of internal parameters of the motor. The simulation results show robustness the sliding input-output linearization control of squirrel cage motor responses.

Analysis and design of wind energy system based on nonlinear speed controller

This paper presents a control fuzzy robuste strategy for a squirrel cage Induction generator (SCIG)-based wind energy conversion system. Control strategies is presented along with the mathematical modeling of the employed configuration. The maximum power point extraction of the wind turbine is addressed along with the proposed strategy. The approach method is introduced to control the generator speed and regulate the flux. The wind system is then simulated in Matlab-Simulink and the developed model is used to illustrate the behavior of the system. The simulation results are presented and discussed at the end of this paper.

Condition monitoring through DWT-PSD-FLS approach of faulty wind generators

This work investigates the prospect of rotor broken bar defect diagnosis in squirrel cage induction generator-based wind turbine using fuzzy logic system (FLS) of the stator currents. The generator current signal is analyzed through the power spectral density (PSD) to diagnose the magnitudes and frequencies associated with various defects. These magnitudes and frequency components are used to apply the system of Fuzzy logic by simulation software.

Computational Design and Analysis of Three-Phase Induction Motor using Finite Element Method

Abstract: The increment in the cost of electrical energy and relevant development in the material technology, operating cost and some other performance related items, promoted the designers to improve the performance of the induction motors. Three Phase induction motor plays key role in industrial and commercial sectors, because of their numerous advantages. This paper attempts to model an optimal three phase squirrel cage induction motor by changing the rotor winding configuration and keeping all other parameters i.e. stator/rotor slots area, number of stator/rotor slots, stator winding, stator/rotor core material etc. remains unchanged. This work investigates the influence of the rotor winding on the performance of the machine. Aluminum and Copper are simultaneously used as rotor winding material. A standard model of 3-phase induction motor has been taken and two different models are designed by changing the rotor winding configuration and simulated in Finite Element Method. The results have been compared to find out the best optimal model. The Finite Element Analysis software used is FEMM (Finite Element Method Magnetics).

Harmonics in the Squirrel Cage Induction Motor Analytic Calculation - Part I: Differential Leakage, Attenuation, Asynchronous Parasitic Torques

Harmonics in the Squirrel Cage Induction Motor Analytic Calculation - Part I: Differential Leakage, Attenuation, Asynchronous Parasitic Torques

Parameters Variation to Estimate Performance Characteristics of 3-Phase Asynchronous Motor

The 3-phase asynchronous squirrel cage motors (SCIM) are main competitor machines placed instead of other motors in the commercial and industrial fields. The stator and rotor material selection and construction topology influence the electrical machine design. The results illustrated with the motor of a 15KW, variable controlled speed, for the constant frequency of 50 Hz or 60 Hz. The motor parameters created inside the simulation model must be matched with the value of a standard parameter for SCIM to achieve a high dynamic response. This work examines the effect of the parameters variation for synchronous motor on the performance characteristics at starting point and at a load change for different time and speed regions. Changing of the rotor power is taken into consideration, this occurs with dynamic change of the hydraulic pump load from the valve.In the industrial applications, production,and manufacturing, till present there are still struggles to find the most holistic environment of the SCIM to achieve its efficiency at the lowest cost and same time control the motor performance, so we predicted the method of reducing the most effective motor parameters to improve the efficiency.The offline method used the SCIM parameters to calculate the time-varying current, torque, and rotor speed. The results illustrated that this method was fully consistent with the experiment tests and the standard theoretical values.A MATLAB program is used to simulate this study. The simulation model proved the feasibility of the proposed method with encouraging performance.

Investigation of the autorotation stability of a turbofan engine with a detached fan blade

One of the requirements for the aircraft being designed is the ability to continue flying and landing if one engine fails. One of the calculated cases of engine failure is the separation of the fan blade. This phenomenon causes large vibrations in both the engine itself and the aircraft structure. A calculation model and method for studying engine oscillations with damage in the form of blade separation have been developed. Numerical studies of the oscillations of the engine suspended on a pylon were conducted. The operation of an engine with an unbalance of the fan after switching off during the transition to autorotation is considered. Numerical simulation was performed using the ANSYS Workbench package. The front supports of the rotors are ball bearings installed in the elastic elements of the "squirrel cage". Ball bearings are modeled as a rigid hinge. There are two thin-walled shells, which are intermediate power elements outside the elastic element. With an increase in the imbalance of the fan rotor, the gap in the oil damper closes, the damper housing sits on the shells, switching on their rigidity to work. Thus the support stiffness characteristic is bilinear. The stiffness coefficients of the elastic element "squirrel cage" and the shells of the front support are determined by numerical simulation. The fan rotor is modeled as a rigid body on bearings. The motor stator is modeled by a rigid body on an elastic suspension. The pylon and elements of the elastic suspension of the engine are modeled by beams of variable section, working simultaneously in tension, torsion, and bending. Numerical analysis of the transient oscillations of the D-436-148FM engine mounted on the pylon of the AN-178 aircraft was performed. The amplitude-frequency response of oscillations is obtained in the frequency range below the fan speed in the cruising mode. The stability of engine oscillations at a resonant frequency close to the autorotation frequency has been studied. The results of the numerical simulation are presented in the form of diagrams. Orbits of the centers of gravity of the fan rotor and the motor casing in the resonant mode are constructed. Poincaré mappings of oscillations of the same points of the structure are also constructed.

A FEM-Based Comparative Study of the Effect of Rotor Bar Designs on the Performance of Squirrel Cage Induction Motors

Induction motors (IM) are the most frequently used type of motor in the industry. The number of rotor slots, bar geometry, and conductivity of bar material have a strong impact on the torque profile and efficiency characteristics of induction motors. This study focused on investigating the effect of different rotor bar designs on motor performance by the finite element method (FEM). The IMs have been designed using the same stator core, winding, and core lengths. The total rotor bar cross-section areas are also fixed throughout all designs. In addition to the change in the number of rotor bars and geometry, the effect of copper and aluminum bar materials on motor performance was also investigated, both for single and double-layered squirrel-cage structures. The results of the study indicate that the starting torque of the motor in a 36/30-slot aluminum single-cage structure was obtained as 96.26 Nm, while the starting torque of a 36/46-slot aluminum double-cage structure was found to be 115.34 Nm. It is also found that the starting torque of the initial design can be increased by up to 19.82% by changing only the rotor bar numbers and material with the same stator and rotor size. The efficiency of the motors was determined as 86.6% for both designs. In addition to efficiency, the output torque ripple has been decreased to 2.63, which equals a 67.32% decrease in the ripple of the initial design. The improved design has an approximately 8 °C lower T2 due to better cooling performance as a result of a higher number of rotor slots.

Probing for the Energy-Conserving Load Rates of the Asynchronous Motor by Harnessing the Efficiency and Power Factor Synergy

In the light of the energy saving drive and environmental concerns in today’s world, the technical and procurement staff of industries are encouraged to invariably take into consideration, motor efficiency as well as power factor in their motor load analyses prior to embarking on motor installation/replacement decisions. It is believed that with this practice, which hinges on a tradeoff between loading at high operating power factor and loading at high operating efficiency, the motor-driven business may stand a better chance of being run greener and more economical. This article proposes a method that engineers in research or in charge of the operations and maintenance of the 3-phase squirrel cage induction motor (SCIM) in business, may deploy to arrive at the energy efficient load range/rates for the machines of interest. The method was developed with the aid of the following motor curves viz: the load/efficiency curve, the load/compensated power factor curve as well as the load/kilovolt ampere reactive (KVAR) curve; and demonstrated on three different SCIM ratings, to determine their respective most energy efficient bounds of loading. Field data, z-score analysis and the power loss simulations were used for validating the proposed method. The energy efficient load range/rate was found to be machine-specific and the width of this load range seemed to be jointly governed by the high efficiency span of the load-efficiency curve as well as the load-wide profile of the power factor over the most part of the SCIM loading, up to full load. The mean optimal loading for the machines investigated, ranged from 66% - 73%, with standard deviations not exceeding 15.85%.

A novel deep convolutional neural network and its application to fault diagnosis of the squirrel-cage asynchronous motor under noisy environment

The intelligent classification achieved through the utilization of deep learning networks, which possess the capability to automatically extract essential features from data, has garnered significant attention within the domain of fault diagnosis research. Nevertheless, within the industrial production process, the data collected inevitably suffers from noise contamination, thereby adversely affecting the network’s diagnostic results. To enhance the denoising prowess and mitigate the risks associated with overfitting in deep learning networks, this paper introduces the input gate structure of long short-term memory and an attention module into the convolutional neural network to propose a novel architecture known as the gate convolutional attention neural network (gate-CANN), which subsequently finds application in the domain of squirrel-cage asynchronous motor fault diagnosis. Firstly, the sensor-acquired time domain vibration undergo conversion into two-dimensional time–frequency images through the employment of continuous wavelet transform (CWT). Subsequently, the CWT images in two directions are put into gate-CANN for feature extraction, respectively. Finally, feature fusion and fault diagnosis are achieved in the end of network. To validate the effectiveness of the proposed method, it undergoes verification using the fault diagnosis testbed specific to squirrel cage asynchronous motors. The obtained results demonstrate that, in comparison to alternative diagnostic methods, the proposed approach exhibits superior capabilities in terms of noise resistance and generalization.

Building a Digital Twin Powered Intelligent Predictive Maintenance System for Industrial AC Machines

Predictive maintenance is a system’s competency in distinguishing future scenarios where the machine is likely to fail and schedule repairs just prior to this happening. A heuristic technology to enable efficient predictive maintenance is digital twin technology. The development of a twin system between real-time machinery and the virtual world is made possible by digital twin technology, which is ideal for predictive maintenance. Induction motors, which are the core of industrial machinery, are sparsely represented in the digital twin domain. Therefore, this study created a digital twin of a squirrel cage induction motor, utilizing data-driven modeling and multiple physics, and integrated it with a custom predictive maintenance system. The purpose of this study is to implement digital twin technology for induction motors for fault diagnosis and predictive maintenance. This framework can extrapolate running parameters to presciently detect motor remaining useful lifetime as well as erratic fault diagnosis. The experimental setup for the 2.2 kW squirrel cage induction motor has been integrated into the digital workspace via the dSPACE MicroLabBox controller to allow frequent calibration and reference signal setup. The resultant digital framework deployed on MATLAB Simulink provided high accuracy without placing a great computational load on the processor. The proposed model’s commercial application may open the way for computational intelligence in Industry 4.0 adoption of induction motors.

An optimized stator and rotor design of squirrel cage induction motor for EMU train

This paper aims to objectively calculate the 480-kW squirrel cage induction motor (SCIM) design for the electric multiple unit (EMU) trains. This is done by optimizing the stator slot and rotor slot design to get efficiency and power factor targets. The stator slot design is achieved by limiting the width and height of the stator slot pitch according to the specified range. A depth-to-width ratio is used according to the range to optimize the design of the rotor bar slot. The design process of the induction motor consists of three steps: it determines the specification design target, calculates the specified parameters of the induction motor, and simulates the design to obtain the most optimal motor design using ANSYS Maxwell. The simulation performance values obtained an efficiency of 92.547 % and a power factor of 0.915. This value is obtained from the optimization of the rotor slot and has met the minimum requirements of efficiency and power factor in designing a SCIM. The design proposed in this paper can be developed and applied in the Indonesian domestic railway manufacturing industry.

FEM-based analysis of rotor cage material and slot geometry on double air gap axial flux induction motors

The aim of this study is to research the effect of rotor slots geometry and squirrel cage material on motor performance in Axial Flux Induction Motors (AFIM) by analysing with finite element method. It is aimed to present a motor design with higher efficiency and low torque ripple, especially for use in electric vehicles. This study aims to design a double stator AFIM with a rated power of 50 kW. Aluminium, and copper cage motors with same stator structure but with 6 different rotor slots geometries are compared. Saturation of cores and current density of coils are considered as limit parameters. Using copper bars with rotor slot1, 92.12% efficiency was obtained with 5.77% torque ripple.

INPUT-OUTPUT LINEARIZATION CONTROL BASED ON THE SLIDING MODE OF THE SQUIRREL CAGE MOTOR

Speed squirrel cage motor control is an area of research that has been in evidence for some time. In this paper, a nonlinear controller is presented for the squirrel cage motor drives, based on a combination between input-output feedback linearization control (IOLC) technique and sliding mode control (SMC) to create a new control which is sliding input-output linearization (SIOLC) control of squirrel cage motors, where the sliding mode control is used for controlling the speed of squirrel cage motor and the input-output linearization control applied for two input witch are flux and current. To test the robustness and performance of sliding input-output linearization control (SIOLC) we created a variety of internal and external parameters of the motor. The simulation results are done using Matlab/Simulink, which shows the robustness of the sliding input-output linearization control of squirrel cage motor responses.

An intelligent wind turbine with yaw mechanism using machine learning to reduce high-cost sensors quantity

In this paper, with the assistance of some tools and a machine learning model, a smart wind turbine was formed that eliminates some expensive sensors and reduces sensor complexity. Squirrel cage induction generator (SCIG) and six rotor blades make up the proposed design, and depending on the wind's direction, the turbine itself can rotate the rotor hub to produce energy more effectively. Additionally, two stepper motors are coupled to the yaw mechanism with the aid of the rotor hub, and the entire controlling procedure will depend on the direction of the wind. The rotor hub must continuously revolve in the same direction as the wind to maximize wind energy utilization. Additionally, to correctly predict wind degrees, a machine learning model was deployed. Random forest regression was used to train and predict the wind direction. The model is deployed in Raspberry Pi, where the incoming sensor values are being stored. Using the generated data, machine learning model was trained and it can be concluded that the model can potentially replace some of the expensive sensors to reduce cost. The model can be used for similar weather conditions only based on machine learning model and fewer sensors.

Detection of Simultaneous Bearing Faults Fusing Cross Correlation With Multikernel SVM

Detection of simultaneous bearing faults for condition monitoring (CM) of bearings using time-domain analysis is quite challenging and open area particularly in noisy environment. This work presents a new scheme for simultaneous bearing fault detection using vibration signal, in cases where single-point localized bearing fault and multiple-point compound fault coexist. Bearings of a 415 V, 3kW, 3-phase Squirrel Cage Induction Motor (SCIM) have been used for data collection, while the loading arrangement is done using a 110V, 4kW DC generator connected with a load box and coupled to the motor. A cross-correlation-based time-domain feature extraction approach has been introduced. The neighborhood component analysis (NCA) technique has been applied to the cross-correlation-based features to reduce the complexity of the proposed model. Furthermore, the selected features have been fed into a multi-kernel support vector machine (MKSVM) to classify simultaneous bearing faults. This method has also been tested on signals contaminated with white Gaussian noise to verify reliability in the industrial environment. It is found that with only five features, the proposed model yields 100% classification performance metrics for raw signal and under noisy environments with signal-to-noise ratio (SNR) of 20 dB to 50 dB for both full load and no-load conditions. Whereas at 10 dB SNR value, performance decreases slightly, still an overall classification performance metrics of more than 99% is achieved by this method. Furthermore, this method has enhanced performance when compared to earlier studies with publicly available databases for localized bearing failure identification.