Phase Squirrel Cage Induction Motor Research Articles

DSP based space vector PWM control for solar electric vehicle

Solar Electric Vehicles (SEV) are considered the future vehicles to solve the issues of air pollution, global warming, and the rapid decreases of the petroleum resources facing the current transportation technology. However, SEV are still facing important technical obstacles to overcome. They include batteries energy storage capacity, charging times, efficiency of the solar panels and electrical propulsion systems. Solving any of those problems and electric vehicles will compete-complement the internal combustion engines vehicles. In the present work, we propose an electrical propulsion system based on three phase induction motor in order to obtain the desired speed and torque with less power loss. Because of the need to lightweight nature, small volume, low cost, less maintenance and high efficiency system, a three phase squirrel cage induction motor (IM) is selected in the electrical propulsion system. The IM is fed from three phase inverter operated by a constant V/F control method and Space Vector Pulse Width Modulation (SVPWM) algorithm. The proposed control strategy has been implemented on the texas instruments TM320F2812 Digital Signal Processor (DSP) to generate SVPWM signal needed to trigger the gates of IGBT based inverter. The inverter used in this work is a three phase inverter IRAMY20UP60B type. The experimental results show the ability of the proposed control strategy to generate a three-phase sine wave signal with desired frequency. The proposed control strategy is experimented on a locally manufactured EV prototype. The results show that the EV prototype can be propelled to speed up to 60km/h under different road conditions.

Artificial Intelligence to detection fault on three phase squirrel cage induction motors subjected to broken bar fault

Induction motors are widely used in various industrial applications due to their robustness, reliability, and low cost. However, they are also prone to various types of faults, such as broken rotor bars, bearing defects, stator winding faults, and eccentricity. These faults can cause performance degradation, energy loss, and even catastrophic failures if not detected and diagnosed in time. Therefore, condition monitoring and fault diagnosis of induction motors are essential for ensuring their safe and efficient operation. In this paper, we propose a novel fault diagnosis method for induction motors based on artificial intelligence, peak variation response (PVR), park vector approach (PVA), and standard deviation (SD). The proposed method consists of four steps: · Data acquisition and preprocessing, · Feature extraction using pvr and pva, · Feature selection using sd, and · Fault classification using artificial neural networks. The PVR and PVA are used to extract the amplitude and phase information of the stator current signals under different load conditions and fault types. The SD is used to select the most relevant features for fault diagnosis. The ANNs are used to classify the faults based on the selected features. The proposed method is validated by experimental results on a 1.5 kW three-phase induction motor with various simulated faults. The results show that the proposed method can effectively diagnose different types of faults with high accuracy and robustness.

Artificial Intelligence to detection fault on three phase squirrel cage induction motors subjected to stator winding fault

Induction motors are widely used in various industrial applications due to their robustness, reliability, and low cost. However, they are also prone to various types of faults, such as broken rotor bars, bearing defects, stator winding faults, and eccentricity. These faults can cause performance degradation, energy loss, and even catastrophic failures if not detected and diagnosed in time. Therefore, condition monitoring and fault diagnosis of induction motors are essential for ensuring their safe and efficient operation. In this paper, we propose a novel fault diagnosis method for induction motors based on artificial intelligence, peak variation response (PVR), park vector approach (PVA), and standard deviation (SD). The proposed method consists of four steps: · Data acquisition and preprocessing, · Feature extraction using pvr and pva, · Feature selection using sd, and · Fault classification using artificial neural networks. The PVR and PVA are used to extract the amplitude and phase information of the stator current signals under different load conditions and fault types. The SD is used to select the most relevant features for fault diagnosis. The ANNs are used to classify the faults based on the selected features. The proposed method is validated by experimental results on a 1.5 kW three-phase induction motor with various simulated faults. The results show that the proposed method can effectively diagnose different types of faults with high accuracy and robustness.

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).

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%.

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.

Particle Swarm Optimization Based Optimal Design of Six-Phase Induction Motor for Electric Propulsion of Submarines

Recent research reveals that multi-phase motors in electric propulsion systems are highly recommended due to their improved reliability and efficiency over traditional three phase motors. This research presented a comparison of optimal model design of a six phase squirrel cage induction motor (IM) for electric propulsion by using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). A six phase squirrel cage induction motor is designed and simulated by ANSYS Motor-CAD. In order to find the best fit method, simulation results are compared and applied to the motors for electric propulsion, considering the influence of design upon the motor performance. The six-phase squirrel cage induction motor is more energy efficient, reliable and cost effective for the electric propulsion compared to the conventional three phase motor. In this study, first the initial parameters of the six phase squirrel cage induction motor have been determined and then these parameters have been compared with optimized values by Genetic Algorithm (GA) and PSO optimization. The motor designed is optimized using efficiency and power losses as the fitness function. The six phase squirrel cage induction motor is designed using ANSYS Motor-CAD and the simulation results were also presented along with two-dimensional and three-dimensional geometry. The result shows that the weight and power loss are reduced to 161 kg and 0.9359 Kw respectively, while the efficiency and power factor are increased to 0.95 and 0.87 respectively when PSO is used. This shows that the result is promising.

Condition Monitoring and Feature Extraction of Stator Current Phasors for Enhanced Fault Diagnosis in AC Drive

AC drives are employed mainly in process plants for various applications. In most industrial applications, Induction motor drives are preferred as they are robust, reliable, and efficient. Process industries have seen a paradigm shift from manual control to automatic control. Advancements in power electronics technology have led to smooth control of the induction motor using variable frequency drives over an entire speed range. Variable Frequency Drives (VFD) comprises of Voltage source inverter and a three phase squirrel cage induction motor. Various electric faults that are incipient in the VFD cause an abrupt change in circuit parameters resulting in insulation damage, reduced efficiency, and leading to catastrophic failure of the entire system. Hence, continuous monitoring of the system parameters such as stator current, speed, and the vibration of the machine is essential to diagnose incipient faults in the system. AI techniques have been effectively used in the fault diagnosis of electrical systems. In the proposed work, simulation results of machine learning-based fault diagnosis techniques are presented. Real-time IoT-based condition monitoring of the Variable Frequency Drive is also implemented for enhanced fault diagnosis of various incipient electrical faults in AC drives. The experimental results obtained are validated with the simulation data.

Modelling of Stator Coil–To–Ground Faults in Induction Motor

Three–phase squirrel cage induction motors are the most widespread types of electrical motors which can be found in both industrial and tertiary applications. There are some reasons why they are so often used, such as simple construction, nearly maintenance-free, advantageous price and the possibility to feed them directly from the AC network. Even if their reliability is very high, some unexpected breakdowns can occur during their operation. In this paper, coil–to–ground faults of motor stator winding have been taken into consideration. These failures have been modelled in several points of one stator phase using COMSOL software. As seen from simulation results these failures have a significant impact on current distribution in stator phases causing changes in rotor currents, motor inner torque, speed, etc., depending strongly on the actual point of the failure.

Design and Implementation of Constant Speed control System for the Induction motors Using Programmable logic Controller (PLC) and Variable Frequency Derive (VFD)

This paper presents an automatic speed control system on three phase squirrel cage induction motor using Programming Logic Circuit (PLC) and Variable Frequency Drive (VFD) techniques. The aim of this study is to obtain a constant speed of induction motors when these motors are exposed to variable loads. The required speed of the induction motor can be set as a numerical value by (PLC), while the actual speed of the induction motor can be measured by a tachometer which is equipped with (PLC) ) Through an Analog to Digital Converter (ADC). Then the two speeds are compared to the set point with minimum error to get the required constant speed. The control system designed to be tested with two stator poles number (two and four poles) of induction motors. The results have cleared that in spite of doubled the load 11 and 8 times respectively, but the yield average shaft speeds are almost constant 2891.15 rpm and 1402.45 rpm respectively.

Effects of Geometric Dimension Variations on Efficiency of 3-phase Squirrel Cage Induction Motors Considering Economic Evaluation

There are different approaches to improve the efficiency of induction motors. In this paper, the considered approaches include, design of the rotor slot, rotor slot shape, and stator and rotor diameters which are applied on a 30-kW 3-phase, 4-poles, 48-stator slots, and 44-rotor squirrel cage induction motor. Comparison of analytical, simulation and experimental results confirm the proposed FEM-based model of set-up induction motor. The sensitivity analysis of efficiency considering the variations of rotor slot dimensions, stator outer diameter, and stator and rotor diameters have been applied on the case-study. The results of the GA-based optimal design of the rotor slot dimensions showed a 0.32% improvement in motor efficiency. On the other hand, changing the motor core diameters has more effects on motor efficiency so that, when the stator and rotor outer diameters were increased simultaneously, the efficiency was increased by 0.55%. However, if the stator outer diameter just increased, motor efficiency increased by 0.76%. Finally, the economic evaluation was accomplished to validate the optimal approach of motor design for three cases of electricity consumption per kWh.

Rotor Design and Optimization of a Three-Phase Line-Start Synchronous Reluctance Motor

Three-phase line-start synchronous reluctance motor (LS-SynRM) is started by asynchronous torque generated by the rotor conductor bars, and runs synchronously by reluctance torque generated by the inductance difference. In this article, the rotor structure of an LS-SynRM is designed and optimized, based on the theory and characteristics of LS-SynRMs during starting and steady-state operation. First, the influence of the number of rotor flux barriers and the flux barriers ratio on the dq-axis inductance is simulated, and a better structure of rotor flux barriers is obtained. Second, according to the transient theory, a method of separating each torque during the starting process of LS-SynRMs is proposed, and the main reason for the poor starting ability of the initial LS-SynRM is analyzed. Based on the optimized structure of rotor flux barriers, the influence of the area and distribution of the rotor conductor bars on the starting ability of the LS-SynRM is studied, and the structure of rotor conductor bars under the condition of starting with heavy load (constant load) is determined. Finally, through the prototype test, the rated efficiency of the LS-SynRM is 2.8% higher than that of the SCIM is replaced by three phase squirrel cage induction motor (SCIM), which verifies the accuracy of the simulated results. At the same time, the starting current and the pull-in torque under a specific inertia are tested, and the improved effect of the synchronization ability of the improved LS-SynRM was verified.

Comparative analysis of PID and neural network controllers for improving starting torque of wound rotor induction motor

Unlike 3-phase squirrel cage induction motor, starting-up of 3-phase wound rotor counter part can be improved by adding an external resistance to the rotor circuit.Thus, leads to reduce starting current and increase starting torque. In this paper two controllers for 3-phase wound rotor induction motor have been proposed include conventional proportional integral derivative (PID) controller and the other based on artificial neural network (NARMA-L2). A comparison between these controllers has been conducted. It has been shown that starting torque of the motor has been improved, when utilizing the neural network controller compared to the conventional counter part. It should be noted that MATLAB/SIMULINK has been used to implement both controllers.

Study and analysis the effect of variable applied voltage on SCIM performances based on FEA

Study and analysis the effect of variable applied voltage on SCIM performances based on FEA is presented. Three phase squirrel cage induction motor SCIM has been investigated and numerically simulated using finite element method (FEM) with the aid of ANSYS software (RMxprt and Maxwell 2D/3D). This research presents study and analysis of the effects of the voltage variation on performance and efficiency of the three-phase induction motor of the squirrel cage type. The Finite Elements Analysis Method FEA is used as one of the best methods for analysis and simulation of electrical motors in addition to the possibility of dealing with nonlinear equations, Since the induction motor is a complex electromagnetic reaction, the researchers used the ANSYS program to represent and analyze the performance of the motor under variable supply voltage. The case studied in this research is three phases, 380V, 50Hz, 2.2kW, induction motor that widely use in industrial application. The aim of this research is to study the effect of voltage variation on efficiency, current value, power factor and torque of SCIM. The RMxprt software has been used for modeling and simulating the induction motor and calculating the values of phases currents, input and output power in additional of overall efficiency at steady state condition. The next stage of the research is creating Maxwell 2-D design from the base model of RMxprt software, Maxwell 2-D model has the ability to computing the distribution of magnetic field and explaining the performance under steady-state operation. The obtained results show significant reduction of motor performance due to the effect of variation of apply voltage.

Non-Invasive Vibration Measurement for Diagnosis of Bearing Faults in 3-Phase Squirrel Cage Induction Motor Using Microwave Sensor

The vibration is an important feature of the motor to diagnose the different faults. The existing invasive and non-invasive methods to capture and analysis vibration signals have many limitations. Thus, this work proposes a technique to capture and process the motor vibrations non-invasively to diagnose the multiple bearing faults using a microwave signal and software low pass filter respectively. The proposed method uses a high-frequency signal from microwave sensor (handheld Ultra-Wide Band (UWB) radar) projected on the Squirrel Cage Induction Motor (SCIM) and the reflected signal captured. The signal obtained is filtered with Software Phase Locked Loop (Low pass filter (SPLL)) and analyzed with a signal processing algorithm like Wavelet Transform to identify the faults in the motor. In this paper multiple bearing faults under no-load and full-load and a combination of bearing and rotor bar faults are diagnosed with the proposed method using Rational Dilation Wavelet Transforms (RDWT). The various bearing fault signal's energy at the sub-band-7 compared under normal and fault conditions. The signal energy at the fault frequency sub-band under no-load increases by 2.11%, 23.5% and 42.5% compared with the no-fault condition with the increase in the number of bearing faults from 1 to 3. The signal energy variation indicates the severity of the defects and the accuracy of the proposed method is verified with the contact method using a vibration sensor (accelerometer). The other faults analyzed are the combination of the bearing and rotor bar faults with the variation of the signal energy at sub-band 7 & 6. The variation of the signal energy for bearing and rotor bar faults are verified with the theoretical calculation and the proposed method detects the faults with the accuracy of approximately 93%. On the other hand, the proposed method is simple and cost-effective compared with the existing methods.

PENGGUNAAN VARIABLE SPEED DRIVE PADA MOTOR INDUKSI UNTUK PENGHEMATAN KONSUMSI ENERGI LISTRIK

Variable speed drive (VSD) is used not only for controlling the speed but also for saving the electric energyconsumption. In this research VSD is applied to 3 phase squirrel cage induction motor used for water pumping inthe industry. VSD is needed to be applied to induction motors because these motors are the largest electricconsumed. Measurement and calculation are performed in the two conditions, before and after attaching VSD inthe motor. Electric quantities are obtained by measurement in the input and in the output of VSD. While energyconsumption and consumption cost are obtained by calculation. Finally, calculation of energy saving and costsaving are perfomed by comparing those two conditions. Aplication of VSD in the induction motor has advantagesfor saving electric energy consumption dan making it last longer. The energy saving and cost saving per year result1.003.984,8 kWh and IDR 732.908.904 respectlively. The longer life is caused by reduction of energy consumption.

Induction motor broken rotor bar faults diagnosis using ANFIS-based DWT

ABSTRACT This paper proposes a new method for diagnosis Broken Rotor Bar (BRB) faults in three phase squirrel-cage induction motors. The proposed method is based on the stator current signature analysis using Discrete Wavelet Transform (DWT) and Adaptive Neural Fuzzy Inference System (ANFIS) artificial intelligence approach. The DWT technique plays an important role for signal feature extraction. The abnormal transient signals can be applied to recognize the BRB faults by DWT. The DWT is considered to identify fault features accurately. The dataset is established by feature vectors are applied as input pattern in the training and identification process. Furthermore, the ANFIS is proposed to classify and identify the BRB fault. The fault diagnosis is verified experimentally on 1.5 Hp three phase induction motor under different fault conditions and different load conditions. The experiment results demonstrate that this technique is valid and effective for the BRB faults diagnosis.

Experimental Analysis of Performance and Thermal Capability of Three Phase Squirrel Cage Induction Motor Using Plastered Composite Conductors

Background: This article deals with the analysis on improved performance and efficiency of induction motor by using nano composites for stator winding. Methods: The nanocomposites are added with different enamel. Enamel is mostly preferred for induction motors’ winding, due to three main reasons: adhesion, infusion and plaster. To predetermine the plaster and nanocomposite conductor’s behavior when they are used for transmitting AC currents and developing AC magnetic field, a numerical analysis is performed. The total heat losses are determined by the heat run test. Open circuit and short circuit tests are used to analyze the performance and efficiency of the proposed induction motor. Results: The AC losses of composite and plaster conductors having good accord are compared with previous solid and hollow conductors. Analysis of the coil by a composite and plaster conductor shows that the AC losses in low current are lower than the coil, which is wrapped by a solid, and hallow conductors. Due to this reason, composite and plaster conductors are considered advantageous for low and medium power motors. Conclusion: Adding nano composites with the plaster material will help to improve electrical, thermal and mechanical characteristics. The property of enamel can change the lifetime of induction motor. The induction motor winding makes use of nano composites SiO2 and TiO2 with enamel coated.

Efficiency Improvement of Induction Motor Through Altered Design

Three phase squirrel cage induction motors are widely used in industries and hence in this paper we have aimed to increase a motor’s efficiency by augmenting its cooling process. The motor chosen was of 3 HP rating. Motor structure was altered to improve its efficiency and its performance characteristics were analysed to compare with the conventional motor design. A dynamic model was constructed in Matlab Simulink to observe the changes in speed and phase currents on varying load. After successful dynamic modelling, the motor is simulated using Motor Solver and various performance parameters are observed and monitored. This is followed by improving the efficiency by using Aluminium as material for the stator housing and rotor bar and end ring. Silicon steel is used for stator back iron. High cost of Aluminium used to design the stator casing is compensated by the nullification of high maintenance and operational costs

Indirect vector control for induction motor drive using two level and five level inverter

This paper represent modelling & simulation of indirect vector control for induction motor drive using two level and five level inverter. To overcome of lower order harmonics, THD & speed torque characteristics of three phase squirrel cage induction motor. This paper provides solution through indirect vector control & SPWM technique for multilevel inverter. A comparison and performance of two level and five level inverter are used in motor to control speed, current and torque. Matlab simulation environment are taken to simulate indirect vector control for induction motor drive using two level and five level inverter, so torque ripple minimization, lower order harmonics are reduced, constant speed, constant torque and variable speed variable torque are determined through simulation results. The applications under operating conditions are performed better THD, better reliability in AC drives.