Design and Performance Optimization of Premium Induction Motors |Book Publisher International

International Efficiency (IE) is an up-and-coming phenomenon that refers to how energy-efficient motors are. The International Efficiency 1 and 2 classes are very well developed, while IE4 has been in the pipeline. The classification technique can improve the energy efficiency of the motor even more. The motor's baseline energy performance is determined using these specs.

 

The goal of this project is to conduct a conversion study of an induction motor of efficiency class IE2 to class IE3/IE4. The induction motor following International Efficiency 60034- 30-1 standard's efficiency ratings is considered.  The research will be carried out using dedicated computation programs, with experimental validation utilizing Altair's Fluxmotor software.

 

First, motor efficiency, rating, and application of the motor are established and necessary calculations are performed. Then, a motor reference torque-speed map is constructed based on the planned motor performance, along with the division of power losses and the application's power rating, once the efficiency has been determined. The calculated values are then fed into the Altair Flux motor software and a motor of IE2 efficiency class is obtained. This IE2-rated motor is then optimized to reach IE3 and IE4 standards. The performance parameters such as efficiency, speed, torque, temperature and flux distribution, etc. are obtained for various dimensions and structures of the motor. The design optimization technique achieves motor characteristics that adjust as much as feasible to the defined performance, and later adjust it to the characteristics of a water pump or an electric car. The results provide the IE2, IE3 and IE4 standard Induction motor design.

 

Author (s) Details

Dr. Femi R
Department of Electrical and Electronics Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur Campus, Chengalpattu District, Tamil Nadu - 603 203, India.

 

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-48119-90-2

Application of the Novel Wavelet Ann Method for Segregating Bearing Faults in Three-phase Induction Motor | Chapter 2 | Current Approaches in Engineering Research and Technology Vol. 10

Induction motors are a critical component of many industrial processes and are frequently integrated into commercially available equipment. Safety, reliability, efficiency, and performance are some of the major concerns of induction motors.

Three-phase induction motors are the ‘workhorses’ of industry and are the most widely used electrical machines. For this reason, the detection of motor failures is very important. Bearing problems are one of the major causes of drive failures. Early detection of bearing faults allows replacements of the bearings rather than replacement of the motor. The present contribution reports experimental results for monitoring bearing faults in induction motors. Motor line currents have been analyzed using modern signal processing and data reduction tools combining Park’s Transformation and Discrete Wavelet Transform (DWT). Feed Forward Artificial Neural (FFANN) based data classification tool is used for fault characterization based on DWT features extracted from Park’s Current Vector Pattern. An online algorithm is tested successfully on a three-phase induction motor and experimental results are presented to demonstrate the effectiveness of the proposed method which can reliably distinguish the inner race and outer race defects of the bearing. It is observed that for five processing elements in the hidden layer, 100% classification accuracy is achieved for healthy and faulty conditions.

Experimental results are presented to demonstrate the effectiveness of the proposed methodology. The study concluded that the proposed methodology is useful in identifying inner race and outer race defects of bearings in induction motors with 100   percent accuracy.

 

Author (s) Details

Anjali. U Jawadekar
Department of Electrical Engineering, S.S.G.M. College of Engineering Shegaon, India.

 

Mukesh Ravindra Chavan
Department of Electrical Engineering, S.S.G.M. College of Engineering Shegaon, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/caert/v10/2639

New Stable Control Structure for Nonlinear Systems: A Case Study of an Induction Motor Drive | Chapter 8 | Science and Technology: Developments and Applications Vol. 1

Induction motors (IMs) are robust, reliable, and widely used in industrial applications due to their low cost and minimal maintenance requirements. However, their control poses challenges, as it requires complex circuitry to handle variable frequency, intricate dynamics, and parameter variations. This article presents the design and experimental validation of a continuous nonlinear control system based on a novel control structure that utilizes a linear reference model. By applying Lyapunov's second method, the proposed structure ensures asymptotic stability. The key innovation in this control design lies in the use of an additional state variable, which enhances system information and control effectiveness. This structure is implemented for the angular speed control of an induction motor (IM) drive a complex higher-order nonlinear system. The developed control algorithm achieves zero steady-state deviation in the IM drive’s angular speed. Simulations and experiments across various operating conditions demonstrate the advantages of this new control structure. In addition to achieving the desired dynamics, the method ensures system stability, invariance to disturbances, and robustness against parameter variations. Compared to traditional vector control methods for IM drives, the proposed structure is simpler, does not require precise knowledge of system parameters, and avoids stability issues. The controller is suitable especially for any drive system including control of robotic systems control having a precise hierarchical control structure. Therefore, its broad utilization in industrial applications can be assumed. This approach promises broad applicability not only in systems with IM drives but also in a variety of industrial control applications.

 

Author (s) Details

 

Pavol Fedor
Department of Electrical Engineering and Mechatronics, Technical University of Kosice, Letna 9, 04200 Kosice, Slovakia.

 

Daniela Perdukova
Department of Electrical Engineering and Mechatronics, Technical University of Kosice, Letna 9, 04200 Kosice, Slovakia.

 

Peter Bober
Department of Electrical Engineering and Mechatronics, Technical University of Kosice, Letna 9, 04200 Kosice, Slovakia.

 

Marek Fedor
Department of Electrical Engineering and Mechatronics, Technical University of Kosice, Letna 9, 04200 osice, Slovakia.

 

Please see the book here:- https://doi.org/10.9734/bpi/stda/v1/3514

Application of the Novel Wavelet Ann Method for Segregating Bearing Faults in Three-phase Induction Motor | Chapter 2 | Current Approaches in Engineering Research and Technology Vol. 10

 

Induction motors are a critical component of many industrial processes and are frequently integrated into commercially available equipment. Safety, reliability, efficiency, and performance are some of the major concerns of induction motors.
Three-phase induction motors are the ‘workhorses’ of industry and are the most widely used electrical machines. For this reason, the detection of motor failures is very important. Bearing problems are one of the major causes of drive failures. Early detection of bearing faults allows replacements of the bearings rather than replacement of the motor. The present contribution reports experimental results for monitoring bearing faults in induction motors. Motor line currents have been analyzed using modern signal processing and data reduction tools combining Park’s Transformation and Discrete Wavelet Transform (DWT). Feed Forward Artificial Neural (FFANN) based data classification tool is used for fault characterization based on DWT features extracted from Park’s Current Vector Pattern. An online algorithm is tested successfully on a three-phase induction motor and experimental results are presented to demonstrate the effectiveness of the proposed method which can reliably distinguish the inner race and outer race defects of the bearing. It is observed that for five processing elements in the hidden layer, 100% classification accuracy is achieved for healthy and faulty conditions.
Experimental results are presented to demonstrate the effectiveness of the proposed methodology. The study concluded that the proposed methodology is useful in identifying inner race and outer race defects of bearings in induction motors with 100   percent accuracy.

 

Author(s)details:-

 

Dr. Anjali. U Jawadekar
Department of Electrical Engineering, S.S.G.M. College of Engineering Shegaon, India.

 

Mukesh Ravindra Chavan
Department of Electrical Engineering, S.S.G.M. College of Engineering Shegaon, India.

 

Please See the book here :- https://doi.org/10.9734/bpi/caert/v10/2

Analysing the Eccentricity of the Air Gap in an Induction Motor Using a Decision Tree Algorithm | Chapter 1 | Techniques and Innovation in Engineering Research Vol. 9

 This present study presents an air Gap Eccentricity Analysis in Induction Motor utilizing Decision Tree Algorithm. In this study, vibration listening system applied to posture fault study and experimental result shows that vibration and current is ranges of and rotating automobile like induction motor for various bearing weaknesses. The industry is very analogous to the induction motor. Due to allure simple control feature, it is also widely used. The bizarreness of three-phase inference motors is mismatched. We knowing speed pulsation, vibration-persuaded acoustic explosion, and friction issues between the stator and rotor on account of the eccentricity issue. The projected methodology is useful on Real- period data and achieves 90% real Value. The installation of miscellaneous Sensors in order to maintain the Good condition of the initiation motor is very damaging. Decision tree algorithm word that modifies a noun detects 90% accurate profit of air gap that is a gap between rotor and stator, also, the LabVIEW tools and capacity analyzer library is secondhand for searching the maximum correct parameter for achieving the result.

Author(s) Details:

Rama Mishra,
Sarvepalli Radhakrishnan University, Bhopal, Madhya Pradesh, India.

E. Vijay Kumar,
Department of Electrical & Electronics, Engineering, Sarvepalli Radhakrishnan University, Bhopal, Madhya Pradesh, India.

Please see the link here: https://stm.bookpi.org/TAIER-V9/article/view/9981

Assessment of Artificial Neural Network-based Induction Motor Fault Classifier Using Continuous Wavelet Transform| Chapter 12 | Novel Perspectives of Engineering Research Vol.7

 Due to their great benefits over other types of electric motors, induction motors are widely employed in industrial, commercial, and household applications. These motors are subjected to a wide range of operating stresses, which can lead to failure. The most common repeated failures in induction motors are bearing faults, stator interturn faults, and fractured rotor bars. For reliable and cost-effective operation, early diagnosis of induction motor defects is crucial. Induction motor faults and failures can cause long periods of downtime and significant maintenance and revenue losses. The cost of purchasing and installing equipment is often less than half of the entire cost of maintenance over the machine's lifetime. Maintenance costs range from 15% to 40% of the overall cost, and they might reach as high as 80% of the whole cost. The failure of a heavily loaded equipment can often bring an entire industry process to a standstill. The demand for automated manufacturing systems with effective monitoring and control capabilities has grown in response to the growing demand for high-quality, low-cost production.

An induction motor's condition monitoring and fault diagnosis are crucial in the production process. By enabling for the early detection of catastrophic failures, it can reduce maintenance costs and the danger of unanticipated breakdowns. Vibration monitoring, heat monitoring, chemical monitoring, and acoustic emission monitoring are just a few of the condition monitoring methods available, but they all require expensive sensors or specialised instruments. Current monitoring, on the other hand, does not necessitate the purchase of additional expensive sensors because basic electrical quantities such as voltage and current are easily measured by voltage and current transformers, which are always provided as part of the protective system. As a result, current monitoring is non-intrusive and can be used even if the motor is located far away from the control centre. As a result, MCSA demonstrates that it is a low-cost online nondestructive fault diagnostic and detection system that can accurately identify motor defects.

The findings of experiments using signal processing and artificial neural networks to identify numerous faults in induction motors are presented in this chapter. Motor line currents recorded under various fault circumstances were analysed using the continuous wavelet transform. For fault characterisation, a feedforward neural network was employed with fault features retrieved using the continuous wavelet transform.

Author(s) Details:

A. U. Jawadekar,
Department of Electrical Engineering, S. S. G. M. College of Engineering Shegaon, (M.S.), India.

G. M. Dhole,
Department of Electrical Engineering, S. S. G. M. College of Engineering Shegaon, (M.S.), India.

S. R. Paraskar.
Department of Electrical Engineering, S. S. G. M. College of Engineering Shegaon, (M.S.), India.

S. S. Jadhao,
Department of Electrical Engineering, S. S. G. M. College of Engineering Shegaon, (M.S.), India.

Please see the link here: https://stm.bookpi.org/NPER-V7/article/view/5909

Study of FPGA Based Vector Control of Induction Motor | Chapter 7 | New Approaches in Engineering Research Vol. 16

 To get the desired response of the sensorless Vector controlled Induction Motor (SVC-IM) by the experimental setup, the controller implementation for the creation of PWM signals is crucial. It is now conceivable, thanks to the tremendous growth of the electronic sector, which includes high-speed digital signal processors (DSPs) and microcontrollers. The various SVC-IM algorithms, notably KF, PI, GA, and PSO, were previously implemented using DSP technologies. As a result, it generates issues with time delay and PWM signal execution, among other things, as the system becomes more sophisticated. As a result, a new technique to addressing the issue of execution time is introduced, namely Field Programmable Gate Array (FPGA) processors, which are currently on the market. In addition, the programming is done in VHDL, a high-speed hardware description language.

Author(S) Details

G. Srinivas
GITAM (Deemed to be University Hyderabad), India.

View Book:- https://stm.bookpi.org/NAER-V16/article/view/4060