scholarly journals Artificial neural network vector controlled common high-side switch asymmetric converter fed switched reluctance motor drive

2021 ◽  
Vol 24 (2) ◽  
pp. 697
Author(s):  
Ashok Kumar Kolluru ◽  
Malligunta Kiran Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Induction Machine ◽  
Switched Reluctance Motor ◽  
Switched Reluctance ◽  
Reluctance Machine ◽  
Reluctance Motor ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Cost

<p>The best alternative machine for synchronous and induction machine is switched reluctance machine for various applications. An artificial neural network (ANN) based vector controller is implemented for novel converter to drive switched reluctance motor (SRM) in this paper. To reduce the cost and simplified the controller an effective configuration of converter is proposed with only 4 pulse-withmodulation (PWM) based switches. The 6 pole stator and 4 pole rotor machine is considered in this paper to present results based on MATLAB. The ripples in torque are reduced by proposing vector controller by using novel configuration of converter. Generally SRM machines are having high ripples in torque, hence less number of switches will be feasible solution to drive the machine in order to reduce ripples. The proposed controller can also help to operate system with less ripples in torque since the controller having both torque and flux hysteresis controllers. The extensive results are presented on Simulink platform to validate the proposed method under both steady state as well as transient conditions.</p>

scholarly journals Artificial Neural Network-Based Control of Switched Reluctance Motor for Torque Ripple Reduction

2020 ◽  
Vol 2020 ◽  
pp. 1-31
Author(s):  
Iqra Tariq ◽  
Raheel Muzzammel ◽  
Umar Alqasmi ◽  
Ali Raza
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Switched Reluctance Motor ◽  
Torque Ripple ◽  
Switched Reluctance Motors ◽  
Switched Reluctance ◽  
Reluctance Motor ◽  
Artificial Neural ◽  
Torque Ripples

Switched reluctance motor is acquiring major attention because of its simple design, economic development, and reduced dependability. These attributes make switched reluctance motors superior to other variable speed machines. The major challenge associated with the development of a switched reluctance motor is its high torque ripple. Torque ripple produces noise and vibration, resulting in degradation of its performance. Various techniques are developed to cope with torque ripples. Practically, there exists not a single mature technique for the minimization of torque ripples in switched reluctance motors. In this research, a switched reluctance motor is modelled and analysed. Its speed and current control are implemented through artificial neural networks. Artificial neural network is found to be a promising technique as compared with other techniques because of its accuracy, reduced complexity, stability, and generalization. The Levenberg–Marquardt algorithm is utilized in artificial neural networks due to its fast and stable convergence for training and testing. It is found from research that artificial neural network-based improved control shows better performance of the switched reluctance motor. Realization of this technique is further validated from its mean square error analysis. Operating parameters of the switched reluctance motor are improved significantly. Simulation environment is created in Matlab/Simulink.

scholarly journals Using Artificial Neural Network to Predict Rate of Penetration from Dynamic Elastic Properties in Nasiriya Oil Field

2020 ◽  
Vol 21 (2) ◽  
pp. 7-14
Author(s):  
Yasser A. Khudhaier ◽  
Fadhil S. Kadhim ◽  
Yousif K. Yousif
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Elastic Properties ◽  
Average Rate ◽  
Oil Field ◽  
Rate Of Penetration ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Cost ◽  
Dynamic Elastic Properties

   The time spent in drilling ahead is usually a significant portion of total well cost. Drilling is an expensive operation including the cost of equipment and material used during the penetration of rock plus crew efforts in order to finish the well without serious problems. Knowing the rate of penetration should help in speculation of the cost and lead to optimize drilling outgoings. Ten wells in the Nasiriya oil field have been selected based on the availability of the data. Dynamic elastic properties of Mishrif formation in the selected wells were determined by using Interactive Petrophysics (IP V3.5) software based on the las files and log record provided. The average rate of penetration and average dynamic elastic properties for the studied wells was determined and listed with depth. Laboratory measurements were conducted on core samples selected from two wells from the studied wells. Ultrasonic device was used to measure the transit time of compressional and shear waves and to compare these results with log records. The reason behind that is to check the accuracy of the Greenberg-Castagna equation that was used to estimate the shear wave in order to calculate dynamic elastic properties. The model was built using Artificial Neural Network (ANN) to predict the rate of penetration in Mishrif formation in the Nasiriya oil field for the selected wells. The results obtained from the model were compared with the provided rate of penetration from the field and the Mean Square Error (MSE) of the model was 3.58 *10-5.

scholarly journals Predicting the Land Value using Regression Techniques and Artificial Neural Network

2019 ◽  
Vol 9 (1S3) ◽  
pp. 431-436
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tamil Nadu ◽  
Research Work ◽  
Land Value ◽  
Regression Techniques ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Cost ◽  
Cost Of Construction

Land value can be an important factor which influences the cost of construction on working in the project. The land has socio-economic and environmental values and the confronted problems on land involves the increasing costs for developing the land such as built up, agricultural, residential, commercial and industrial areas. Hence this paper concentrates on prediction of land value by considering some important factors that affects it. The study area has been selected under Tirupur district, being a developing one in Tamil Nadu. The eleven areas in four different taluks under Tirupur district were chosen for research work. The average values of monthly variation are taken for the chosen factor for the years from 2001 to 2017. Using regression analysis and artificial neural network, the prediction has been done for the future land value. The performance of both the model executed good and fit for forecasting results. Though both the model showed better results, Artificial Neural Network (ANN) showed accuracy than regression method.

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