Research Article
BibTex RIS Cite

Design of Monitoring of Induction Motor Operating Parameters with SCADA

Year 2019, Special Issue 2019, 418 - 427, 31.10.2019
https://doi.org/10.31590/ejosat.638378

Abstract

Induction motors; Due to its durability, maintenance-free structure and low cost, it can be supplied directly from a single phase or three phase alternating current network. The biggest difference of asynchronous machines from synchronous machines is that the rotational speed is not constant. In an asynchronous motor operating as a motor, this speed is smaller than the synchronous speed. The machine is named asynchronous machine because of this feature. The fact that around 70% of the energy produced in the world is consumed in asynchronous motors shows the frequency and importance of these motors. Asynchronous motors are widely used in industry due to their simple, economical and robust structure, maintenance-free and superior operating conditions. Various control techniques are used for the control of commonly used asynchronous motors. Mathematical modeling of asynchronous motors according to these driving techniques is also an important subject of study. In order to investigate the dynamic performances of induction motors and to obtain mathematical models, it is necessary to calculate the parameters correctly. In this study, one of the biggest problems of induction motors is aimed to determine the parameters correctly. Due to the development of computer and microprocessor technology, determination of asynchronous motor parameters has become easier and more important.
In this study, the design of a SCADA system, which will enable reading of current, voltage, cos φ, torque, power values which are the operating parameters of commonly used asynchronous motors, has been realized. In this system, PLC, HMI and speed controller will be communicated and the operation parameters of the induction motor will be displayed on the HMI screen. Using the data displayed on the screen, the desired values of the asynchronous motor can be reached using calculation methods. In the SCADA program, an algorithm will be designed in which the asynchronous motor parameters will be calculated in the most accurate and fastest way. Accurate calculation of parameter values will provide a great improvement in determining the dynamic effects of the motor and designing the driving circuits.

Project Number

18401098

References

  • Aminu, M. (2019). A parameter estimation algorithm for induction machines using Artificial Bee Colony (ABC) optimization. Nigerian Journal of Technology, 38(1), 193-201.
  • Atkinson, D. J., Acarnley, P. P., & Finch, J. W. (1991). Observers for induction motor state and parameter estimation. IEEE Transactions on industry applications, 27(6), 1119-1127.
  • Boglietti, A., Cavagnino, A., & Lazzari, M. (2010). Computational algorithms for induction-motor equivalent circuit parameter determination—Part I: Resistances and leakage reactances. IEEE Transactions on Industrial Electronics, 58(9), 3723-3733.
  • Cao, R., Lu, M., Jiang, N., & Cheng, M. (2019). Comparison Between Linear Induction Motor and Linear Flux-switching Permanent-Magnet Motor for Railway Transportation. IEEE Transactions on Industrial Electronics.
  • Cherifi, D., & Miloud, Y. (2019). Online Stator and Rotor Resistance Estimation Scheme Using Sliding Mode Observer for Indirect Vector Controlled Speed Sensorless Induction Motor. American Journal of Computer Science and Technology, 2(1), 1-8.
  • Cui, M., Khodayar, M., Chen, C., Wang, X., Zhang, Y., & Khodayar, M. E. (2019). Deep Learning Based Time-Varying Parameter Identification for System-Wide Load Modeling. IEEE Transactions on Smart Grid.
  • Dolinar, D., De Weerdt, R., Belmans, R., & Freeman, E. (1997). Calculation of two-axis induction motor model parameters using finite elements. IEEE Transactions on Energy Conversion, 12(2), 133-142.
  • Gastli, A. (1999). Identification of induction motor equivalent circuit parameters using the single-phase test. IEEE Transactions on Energy Conversion, 14(1), 51-56.
  • Haque, M. (2008). Determination of NEMA design induction motor parameters from manufacturer data. IEEE Transactions on Energy Conversion, 23(4), 997-1004.
  • Jabbour, N., & Mademlis, C. (2018). Online parameters estimation and autotuning of a discrete-time model predictive speed controller for induction motor drives. IEEE Transactions on Power Electronics, 34(2), 1548-1559.
  • Matsuo, T., & Lipo, T. A. (1985). A rotor parameter identification scheme for vector-controlled induction motor drives. IEEE Transactions on industry applications(3), 624-632.
  • Mirafzal, B., Skibinski, G. L., & Tallam, R. M. (2009). Determination of parameters in the universal induction motor model. IEEE Transactions on industry applications, 45(1), 142-151.
  • Moreira, A. F., Lipo, T. A., Venkataramanan, G., & Bernet, S. (2002). High-frequency modeling for cable and induction motor overvoltage studies in long cable drives. IEEE Transactions on industry applications, 38(5), 1297-1306.
  • Nangsue, P., Pillay, P., & Conry, S. E. (1999). Evolutionary algorithms for induction motor parameter determination. IEEE Transactions on Energy Conversion, 14(3), 447-453.
  • Pedra, J. (2008). On the determination of induction motor parameters from manufacturer data for electromagnetic transient programs. IEEE Transactions on Power Systems, 23(4), 1709-1718.
  • Pedra, J., & Corcoles, F. (2004). Estimation of induction motor double-cage model parameters from manufacturer data. IEEE Transactions on Energy Conversion, 19(2), 310-317.
  • Shaw, S. R., & Leeb, S. B. (1999). Identification of induction motor parameters from transient stator current measurements. IEEE Transactions on Industrial Electronics, 46(1), 139-149.
  • Toliyat, H. A., Levi, E., & Raina, M. (2003). A review of RFO induction motor parameter estimation techniques. IEEE Transactions on Energy Conversion, 18(2), 271-283.
  • Ursem, R. K., & Vadstrup, P. (2003). Parameter identification of induction motors using differential evolution. Paper presented at the The 2003 Congress on Evolutionary Computation, 2003. CEC'03.
  • Yamamoto, S., Hirahara, H., Tanaka, A., & Ara, T. (2018). A simple method to determine double-cage rotor equivalent circuit parameters of induction motors from no-load and locked-rotor tests. IEEE Transactions on industry applications, 55(1), 273-282.
  • Zhang, D., Liu, T., Zhao, H., & Wu, T. (2019). An Analytical Iron Loss Calculation Model of Inverter-fed Induction Motors Considering Supply and Slot Harmonics. IEEE Transactions on Industrial Electronics.
  • https://www.siemens.com.tr/sinamicsg120 (adresinden Haziran, 2019 tarihinde alınmıştır)
  • https://www.elektrikport.com/teknik-kutuphane/plc-s7-1200-nedir/15172#ad-image-2 (adresinden Haziran, 2019 tarihinde alınmıştır)
  • http://makrootomasyon.com.tr/6av2123-2gb03-0ax0/ (adresinden Haziran, 2019 tarihinde alınmıştır)
  • https://www.elit.ee/shelf.do?cmd=iv&pid=6ES7231-5PA30-0XB0 (adresinden Haziran, 2019 tarihinde alınmıştır)
  • http://www.inverter-plc.net/sens%C3%B6rler/pt100.html (adresinden Haziran, 2019 tarihinde alınmıştır)
  • https://www.elektrikde.com/encoder-nedir-kullanim-alanlari/ (adresinden Haziran, 2019 tarihinde alınmıştır)

Asenkron Motorun Çalışma Parametrelerinin SCADA ile İzlenmesinin Tasarımı

Year 2019, Special Issue 2019, 418 - 427, 31.10.2019
https://doi.org/10.31590/ejosat.638378

Abstract

Asenkron motorlar;   doğrudan
bir fazlı ya da üç fazlı alternatif akım şebekesinden beslenebilmesi,   dayanıklı,  
bakım gerektirmeyen yapısı ve düşük maliyetleri nedeniyle, hem sanayide
hem de ev aletlerinde en çok kullanılan motor türü haline gelmiştir. Asenkron
makinelerin, senkron makinelerinden en büyük farkı dönme hızının sabit
olmayışıdır. Motor olarak çalışan bir asenkron motorda bu hız, senkron hızdan
küçüktür. Makine bu özelliğinden dolayı, asenkron makine adını almıştır.
Dünyada üretilen enerjinin %70 civarındaki kısmının asenkron motorlarda
tüketiliyor olması bu motorların kullanım sıklığını ve önemini göstermektedir. Yapılarının
basit, ekonomik ve sağlam olmaları, bakım gerektirmemeleri ve her türlü ortam
koşullarında çalışabilmeleri gibi üstün özellikleri nedeniyle asenkron
motorlar, endüstride yaygın olarak kullanılmaktadır. Yaygın olarak kullanılan
asenkron motorların kontrolünde çeşitli sürme teknikleri kullanılmaktadır. Bu
sürme tekniklerine göre asenkron motorların matematiksel modellemesinin
gerçekleştirilmesi de önemli bir çalışma konusudur. Asenkron motorlarının
dinamik performanslarının incelenmesi ve matematiksel modellerinin
çıkartılabilmesi için parametrelerinin doğru olarak hesaplanması gerekmektedir.
Bu çalışmada asenkron motorların en büyük sorunlarından birisi olan
parametrelerinin doğru olarak belirlenmesi amaçlanmıştır. Bilgisayar ve
mikroişlemci teknolojisinin gelişmesine bağlı olarak asenkron motor
parametrelerinin tespiti hem kolaylaşmış hem de daha önem kazanmıştır.



Bu çalışmayla
yaygın olarak kullanılan asenkron motorların çalışma parametreleri olan akım,
gerilim, cos φ, tork, güç değerlerinin operatör panelinde okunmasının
sağlanacağı bir SCADA sisteminin tasarımı gerçekleştirilmiştir. Bu sistemde
PLC, HMI ve hız kontrol cihazı haberleştirilerek asenkron motorun çalışma
parametreleri HMI ekranında görüntülenebilecektir. Ekranda görüntülenen veriler
kullanılarak asenkron motorun istenilen değerlerine hesaplama yöntemleri
kullanılarak ulaşılabilecektir. SCADA programında asenkron motor
parametrelerinin en doğru ve en hızlı şekilde hesaplanacağı bir algoritma
tasarlanacaktır. Parametre değerlerinin doğru olarak hesaplanması motorun
dinamik etkilerinin belirlenmesinde ve sürme devrelerinin tasarlanmasında büyük
bir gelişme sağlayacaktır.

Supporting Institution

Konya Teknik Üniversitesi ve Selçuk Üniversitesi Bilimsel Araştırma Koordinatörlüğü

Project Number

18401098

Thanks

Bu çalışma Konya Teknik Üniversitesi ve Selçuk Üniversitesi Bilimsel Araştırma Koordinatörlüğü 18401098 numaralı proje ile desteklenmiştir.

References

  • Aminu, M. (2019). A parameter estimation algorithm for induction machines using Artificial Bee Colony (ABC) optimization. Nigerian Journal of Technology, 38(1), 193-201.
  • Atkinson, D. J., Acarnley, P. P., & Finch, J. W. (1991). Observers for induction motor state and parameter estimation. IEEE Transactions on industry applications, 27(6), 1119-1127.
  • Boglietti, A., Cavagnino, A., & Lazzari, M. (2010). Computational algorithms for induction-motor equivalent circuit parameter determination—Part I: Resistances and leakage reactances. IEEE Transactions on Industrial Electronics, 58(9), 3723-3733.
  • Cao, R., Lu, M., Jiang, N., & Cheng, M. (2019). Comparison Between Linear Induction Motor and Linear Flux-switching Permanent-Magnet Motor for Railway Transportation. IEEE Transactions on Industrial Electronics.
  • Cherifi, D., & Miloud, Y. (2019). Online Stator and Rotor Resistance Estimation Scheme Using Sliding Mode Observer for Indirect Vector Controlled Speed Sensorless Induction Motor. American Journal of Computer Science and Technology, 2(1), 1-8.
  • Cui, M., Khodayar, M., Chen, C., Wang, X., Zhang, Y., & Khodayar, M. E. (2019). Deep Learning Based Time-Varying Parameter Identification for System-Wide Load Modeling. IEEE Transactions on Smart Grid.
  • Dolinar, D., De Weerdt, R., Belmans, R., & Freeman, E. (1997). Calculation of two-axis induction motor model parameters using finite elements. IEEE Transactions on Energy Conversion, 12(2), 133-142.
  • Gastli, A. (1999). Identification of induction motor equivalent circuit parameters using the single-phase test. IEEE Transactions on Energy Conversion, 14(1), 51-56.
  • Haque, M. (2008). Determination of NEMA design induction motor parameters from manufacturer data. IEEE Transactions on Energy Conversion, 23(4), 997-1004.
  • Jabbour, N., & Mademlis, C. (2018). Online parameters estimation and autotuning of a discrete-time model predictive speed controller for induction motor drives. IEEE Transactions on Power Electronics, 34(2), 1548-1559.
  • Matsuo, T., & Lipo, T. A. (1985). A rotor parameter identification scheme for vector-controlled induction motor drives. IEEE Transactions on industry applications(3), 624-632.
  • Mirafzal, B., Skibinski, G. L., & Tallam, R. M. (2009). Determination of parameters in the universal induction motor model. IEEE Transactions on industry applications, 45(1), 142-151.
  • Moreira, A. F., Lipo, T. A., Venkataramanan, G., & Bernet, S. (2002). High-frequency modeling for cable and induction motor overvoltage studies in long cable drives. IEEE Transactions on industry applications, 38(5), 1297-1306.
  • Nangsue, P., Pillay, P., & Conry, S. E. (1999). Evolutionary algorithms for induction motor parameter determination. IEEE Transactions on Energy Conversion, 14(3), 447-453.
  • Pedra, J. (2008). On the determination of induction motor parameters from manufacturer data for electromagnetic transient programs. IEEE Transactions on Power Systems, 23(4), 1709-1718.
  • Pedra, J., & Corcoles, F. (2004). Estimation of induction motor double-cage model parameters from manufacturer data. IEEE Transactions on Energy Conversion, 19(2), 310-317.
  • Shaw, S. R., & Leeb, S. B. (1999). Identification of induction motor parameters from transient stator current measurements. IEEE Transactions on Industrial Electronics, 46(1), 139-149.
  • Toliyat, H. A., Levi, E., & Raina, M. (2003). A review of RFO induction motor parameter estimation techniques. IEEE Transactions on Energy Conversion, 18(2), 271-283.
  • Ursem, R. K., & Vadstrup, P. (2003). Parameter identification of induction motors using differential evolution. Paper presented at the The 2003 Congress on Evolutionary Computation, 2003. CEC'03.
  • Yamamoto, S., Hirahara, H., Tanaka, A., & Ara, T. (2018). A simple method to determine double-cage rotor equivalent circuit parameters of induction motors from no-load and locked-rotor tests. IEEE Transactions on industry applications, 55(1), 273-282.
  • Zhang, D., Liu, T., Zhao, H., & Wu, T. (2019). An Analytical Iron Loss Calculation Model of Inverter-fed Induction Motors Considering Supply and Slot Harmonics. IEEE Transactions on Industrial Electronics.
  • https://www.siemens.com.tr/sinamicsg120 (adresinden Haziran, 2019 tarihinde alınmıştır)
  • https://www.elektrikport.com/teknik-kutuphane/plc-s7-1200-nedir/15172#ad-image-2 (adresinden Haziran, 2019 tarihinde alınmıştır)
  • http://makrootomasyon.com.tr/6av2123-2gb03-0ax0/ (adresinden Haziran, 2019 tarihinde alınmıştır)
  • https://www.elit.ee/shelf.do?cmd=iv&pid=6ES7231-5PA30-0XB0 (adresinden Haziran, 2019 tarihinde alınmıştır)
  • http://www.inverter-plc.net/sens%C3%B6rler/pt100.html (adresinden Haziran, 2019 tarihinde alınmıştır)
  • https://www.elektrikde.com/encoder-nedir-kullanim-alanlari/ (adresinden Haziran, 2019 tarihinde alınmıştır)
There are 27 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Hakan Terzioğlu This is me 0000-0001-5928-8457

Abdullah Cem Ağaçayak This is me 0000-0002-9285-5764

Gökhan Yalçın This is me 0000-0003-4491-0228

Süleyman Neşeli This is me 0000-0003-1553-581X

Project Number 18401098
Publication Date October 31, 2019
Published in Issue Year 2019 Special Issue 2019

Cite

APA Terzioğlu, H., Ağaçayak, A. C., Yalçın, G., Neşeli, S. (2019). Asenkron Motorun Çalışma Parametrelerinin SCADA ile İzlenmesinin Tasarımı. Avrupa Bilim Ve Teknoloji Dergisi418-427. https://doi.org/10.31590/ejosat.638378