Development of the PID-neurocontroller to compensate for the impact of damages and degradation of induction motor on operation of the electric drive system

Authors

DOI:

https://doi.org/10.15587/1729-4061.2018.136466

Keywords:

PID-neurocontroller, neural network, induction motor, diagnosis, control system, frequency converter, damages

Abstract

In order to synthesize adaptive control systems over asynchronous electric drive that includes a motor with defects or degradation, we proposed a structure and developed an algorithm for training a PID-neurocontroller based on a multilayer feedforward neural network. Such an approach makes it possible to operatively respond to a change in the characteristics of control object that occurs as a result of the emergence and development of damage and degradation of the motor. This, in turn, makes it possible to improve controllability of the motor, and, consequently, to prolong its operation life cycle and to enhance the energy efficiency of its operation. The proposed solutions, in contrast to the traditional, do not require the use of additional equipment for implementation. It is only needed to change a control program for the frequency converter based on the constructed algorithm. To implement the proposed solutions in practice, we synthesized an algorithm for training a neural network of the PID-neurocontroller with self-tuning. It enables the calculation of weights of the neurons that could be in the future used as the basis of software for a physical control system with the PID-neurocontroller. We mathematically modeled the operation of IM with breaks of the rotor bars and the short-circuited turns in the stator windings when using the proposed controller.

An analysis of modeling results showed that the proposed approach to control the electric drive with a damaged IM makes it possible to decrease the amplitude and the number of non-basic harmonics of current and power signals of IM while maintaining the preset parameters of the technological process. Thus, our paper demonstrates the effectiveness of applying the proposed approach to the tasks on maintaining the predefined parameters of the technological process for the case of a stochastic change in the characteristics of control object

Author Biographies

Dmytro Mamchur, Kremenchuk Mykhailo Ostrohradskyi National University Pershotravneva str., 20, Kremenchuk, Ukraine, 39600

PhD, Associate Professor

Department of Automation and computer-integrated technologies

Rostyslav Yatsiuk, Kremenchuk Mykhailo Ostrohradskyi National University Pershotravneva str., 20, Kremenchuk, Ukraine, 39600

Department of Automation and computer-integrated technologies

References

  1. Bessonov, L. А. (2001). Theoretical foundations of electrical engineering. Electric circuits. Moscow: Gardariki, 638.
  2. Al-Mashakbeh, A., Mamchur, D., Kalinov, A., Zagirnyak, M. (2016). A diagnostic of induction motors supplied using frequency converter basing on current and power signal analysis. Przegląd Elektrotechniczny, 1 (12), 7–10. doi: https://doi.org/10.15199/48.2016.12.02
  3. Zagirnyak, M., Mamchur, D., Kalinov, A. (2014). A comparison of informative value of motor current and power spectra for the tasks of induction motor diagnostics. 2014 16th International Power Electronics and Motion Control Conference and Exposition. doi: https://doi.org/10.1109/epepemc.2014.6980549
  4. Palamar, M. I. (2006). Control of following antennas with uncertain dynamic parameters for tracking low-altitude spacecrafts. Automatics, Measurement and Control. Proceedings of “Lvivska Polytechnica”, 401, 32–38. Available at: http://ena.lp.edu.ua/bitstream/ntb/11748/1/8_keruvannya.pdf
  5. Lavrenov, E. O. (2016). Compensation methods of electrical asymmetry effect on induction motor moment. Bulletin of the Tomsk Polytechnic University. Geo Аssets Engineering, 1, 72–78. Available at: http://earchive.tpu.ru/bitstream/11683/9008/1/bulletin_tpu-2016-v327-i1-08.pdf
  6. Zagirnyak, M., Maliakova, M., Kalinov, A. (2015). Analysis of operation of power components compensation systems at harmonic distortions of mains supply voltage. 2015 Intl Aegean Conference on Electrical Machines & Power Electronics (ACEMP), 2015 Intl Conference on Optimization of Electrical & Electronic Equipment (OPTIM) & 2015 Intl Symposium on Advanced Electromechanical Motion Systems (ELECTROMOTION). doi: https://doi.org/10.1109/optim.2015.7426958
  7. Zagirnyak, M., Maliakova, M., Kalinov, A. (2015). Compensation of higher current harmonics at harmonic distortions of mains supply voltage. 2015 16th International Conference on Computational Problems of Electrical Engineering (CPEE). 2015. doi: https://doi.org/10.1109/cpee.2015.7333388
  8. Al-Mashakbeh, A. S., Zagirnyak, M., Maliakova, M., Kalinov, A. (2017). Improvement of compensation method for non-active current components at mains supply voltage unbalance. Eastern-European Journal of Enterprise Technologies, 1 (8 (85)), 41–49. doi: https://doi.org/10.15587/1729-4061.2017.87316
  9. Zagirnyak, M., Kalinov, A., Chumachova, A. (2013). Correction of operating condition of a variable-frequency electric drive with a non-linear and asymmetric induction motor. Eurocon 2013. doi: https://doi.org/10.1109/eurocon.2013.6625108
  10. Zagirnyak, M., Kalinov, A., Melnykov, V., Kochurov, I. (2015). Correction of the operating modes of an induction motor with asymmetrical stator windings at vector control. 2015 International Conference on Electrical Drives and Power Electronics (EDPE). doi: https://doi.org/10.1109/edpe.2015.7325303
  11. Dong, Z., Duan, S., Hu, X., Wang, L., Li, H. (2014). A Novel Memristive Multilayer Feedforward Small-World Neural Network with Its Applications in PID Control. The Scientific World Journal, 2014, 1–12. doi: https://doi.org/10.1155/2014/394828
  12. Mustapha, U. A., Shamsu, S. K., Haruna, A. I. (2015). Determination of the performance of neural PID, fuzzy PID and conventional PID controllers on on tank liquid level control systems. International Journal of Advanced Research in Engineering and Science, 3, 791–799. Available at: http://ijates.com/images/short_pdf/1443711235_1009D.pdf
  13. Yu, W., Rosen, J. (2013). Neural PID Control of Robot Manipulators With Application to an Upper Limb Exoskeleton. IEEE Transactions on Cybernetics, 43 (2), 673–684. doi: https://doi.org/10.1109/tsmcb.2012.2214381
  14. Nguyen, D. H., Widrow, B. (1990). Neural networks for self-learning control systems. IEEE Control Systems Magazine, 10 (3), 18–23. doi: https://doi.org/10.1109/37.55119
  15. Haykin, S. S. (1999). Neural networks: A comprehensive foundation. Prentice Hall, 842.
  16. Zagirnyak, M., Maliakova, M., Kalinov, A. (2015). Analysis of electric circuits with semiconductor converters with the use of a small parameter method in frequency domain. COMPEL – The international journal for computation and mathematics in electrical and electronic engineering, 34 (3), 808–823. doi: https://doi.org/10.1108/compel-10-2014-0260
  17. Zagirnyak, M., Kalinov, A., Maliakova, M. (2013). Analysis of instantaneous power components of electric circuit with a semiconductor element. Archives of Electrical Engineering, 62 (3). doi: https://doi.org/10.2478/aee-2013-0038
  18. Prus, V., Nikitina, A., Zagirnyak, M., Miljavec, D. (2011). Research of rnergy processes in circuits containing iron in saturation condition. Przegląd Elektrotechniczny (Electrical Review), 87 (3), 149–152. Available at: http://pe.org.pl/articles/2011/3/39.pdf
  19. Zagirnyak, M. V., Rodkin, D. I., Korenkova, T. V. (2014). Estimation of energy conversion processes in an electromechanical complex with the use of instantaneous power method. 2014 16th International Power Electronics and Motion Control Conference and Exposition. doi: https://doi.org/10.1109/epepemc.2014.6980719
  20. Zagirnyak, M., Kalinov, A., Maliakova, M. (2011). An algorithm for electric circuits calculation based on instantaneous power component balance. Przegląd Elektrotechniczny (Electrical Review), 87 (12), 212–215. Available at: http://pe.org.pl/articles/2011/12b/59.pdf

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Published

2018-07-20

How to Cite

Mamchur, D., & Yatsiuk, R. (2018). Development of the PID-neurocontroller to compensate for the impact of damages and degradation of induction motor on operation of the electric drive system. Eastern-European Journal of Enterprise Technologies, 5(2 (95), 66–77. https://doi.org/10.15587/1729-4061.2018.136466