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RETRACTED ARTICLE: Internet of Things Based Industrial Automation Using Brushless DC Motor Application with Resilient Directed Neural Network Control FED Virtual Z-Source Multilevel Inverter Topology

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This article was retracted on 13 December 2022

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Abstract

Internet of Things (IoT) is a high-speed communication technology which has carnal substances or devices entrenched with sensors, system connectivity, which allows to receive and interchange data. Industrial Monitoring and Control is required to assemble all the material information, statistics, and data related to the various industrial processes, motors, machines and devices employed in industrial premises. The technological improvements, remote control and monitoring via communication techniques such as wireless sensor network techniques have been widely used in Industries. Competitive advantages over AC motors make for DC motors to replace other electrical engines in applications stretching from high-speed automation to electric motorbikes. BLDC drives are very popular in many industries, at present automation are added standard, Virtual Z-source multilevel is a respectable optimal that can boost the output voltage of the drive. A novel soft computing based Resilient Directed Neural network (RDNN) found Virtual Z-source multilevel inverter, for BLDC motor drive control to make the system balanced when the load is unbalanced and to reduce the electrical torque pulsation. In this work, the utilization of the RDNN to tackle the reduced harmonics issue in VZS-MLI converters is proposed. This strategy permits active voltage control of the crucial and besides concealment of a particular set of harmonics. The performance is evaluated in various emphasis levels of the different control models. The sensors monitor the technical motor parameters like greatest rise and fall time, topmost overextend and inaccuracy value of load current and voltage in BLDC machine. Then the measured values are sent to the processing unit, which will analyze and display the parameters, where the processing unit also communicates with Gateway module to send information to cloud database for remote monitoring. The system also presents the Automatic and manual control methods to stop or start the BLDC machine to avoid system failures.

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References

  1. Durgasukumar, D. G., & Pathak, M. K. (2012). Comparison of adaptive neuro-fuzzy-based space-vector modulation for the two-level inverter. International Journal of Electrical Power & Energy Systems, 38(1), 9–19.

    Article  Google Scholar 

  2. Charumit, C., & Kinnares, V. (2015). Discontinuous SVPWM techniques of three-leg VSI-fed balanced two-phase loads for reduced switching losses and current ripple. IEEE Transactions on Power Electronics, 30(4), 2191–2204.

    Article  Google Scholar 

  3. Zheng, P., Wang, P., Sui, Y., Tong, C., Wu, F., & Li, T. (2014). Near-five-vector SVPWM algorithm for five-phase six-leg inverters under unbalanced load conditions. Journal of Power Electronics, 14(1), 61–73.

    Article  Google Scholar 

  4. Fan, Y., Zhang, L., Cheng, M., & Chau, K. (2015). Sensorless SVPWM-FADTC of a new flux-modulated permanent-magnet wheel motor based on a wide-speed sliding mode observer. IEEE Transactions on Industrial Electronics, 62(5), 3143–3151.

    Article  Google Scholar 

  5. Mutanga, O., Adam, E., Adjorlolo, C., & Abdel-Rahman, E. M. (2015). Evaluating the robustness of models developed from field spectral data in predicting African grass foliar nitrogen concentration using worldview-2 image as an independent test dataset. International Journal of Applied Earth Observation and Geoinformation, 34, 178–187.

    Article  Google Scholar 

  6. Bhat, A. H., & Agarwal, P. (2009). Implementation of a neural-network-based space vector pulse-width modulation for a three-phase neutral-point clamped high-power factor converter. Electric Power Components and Systems, 37(2), 210–233.

    Article  Google Scholar 

  7. Piao, C., & Hung, J. Y. (2015). A simplified and unified space vector PWM algorithm for multilevel diode clamped VSI. In Proceedings of the IEEE international conference on industrial technology (pp. 2770–2776).

  8. Mehrizi-Sani, A., & Filizadeh, S. (2009). An optimized space vector modulation sequence for improved harmonic performance. IEEE Transactions on Industrial Electronics, 56(8), 2894–2903.

    Article  Google Scholar 

  9. Xueguang, Z., Wenjie, Z., Jiaming, C., & Dianguo, X. (2014). Deadbeat control strategy of circulating currents in parallel connection system of three-phase PWM converter. IEEE Transactions on Energy Conversion, 29(2), 406–417.

    Article  Google Scholar 

  10. Pan, S., Pan, J., & Tian, Z. (2012). A shifted SVPWM method to control DC-link resonant inverters and its FPGA realization. IEEE Transactions on Industrial Electronics, 59(9), 3383–3391.

    Article  Google Scholar 

  11. Liang, T. J., O’Connell, R. M., & Hoft, R. G. (1997). Inverter harmonic reduction using Walsh function harmonic elimination method. IEEE Transactions on Power Electronics, 12, 971–982.

    Article  Google Scholar 

  12. Langer, N., Bhat, A. H., & Agarwal, P. (2014). Neural-network-based space-vector pulse-width modulation for capacitor voltage balancing of three phase three-level improved power quality converter. IET Power Electronics, 7(4), 973–983.

    Article  Google Scholar 

  13. Swift, F., & Kamberis, A. (1993). A new Walsh domain technique of harmonic elimination and voltage control in pulse-width modulated inverters. IEEE Transactions on Power Electronics, 8, 170–185.

    Article  Google Scholar 

  14. Dahidah, M. S. A., & Agelidis, V. G. (2008). Reduced harmonics PWM control for cascaded multilevel voltage source converters: A generalized formula. IEEE Transactions on Power Electronics, 23, 1620–1630.

    Article  Google Scholar 

  15. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In ICNN’95 IEEE international conference on neural networks (Vol. 4, pp. 1942–1948).

  16. Azab, M. (2010). Particle swarm optimization-based solutions for selective harmonic elimination in single-phase PWM inverters. International Journal of Power Electronics, 2, 125–142.

    Article  Google Scholar 

  17. Debnath, S., & Ray, R. N. (2012). THD optimization in 13-level photovoltaic inverter using genetic algorithm. International Journal of Engineering Research and Applications, 2, 385–389.

    Google Scholar 

  18. Kavousi, A., Vahidi, B., & Salehi, R. (2012). Application of the bee algorithm for reduced harmonics strategy in multilevel inverters. IEEE Transactions on power Electronics, 27, 1689–1696.

    Article  Google Scholar 

  19. Dahidah, M. S. A., Agelidis, V. G., & Rao, M. V. (2008). Hybrid genetic algorithm approach for selective harmonic control. Energy Conversion and Management, 49, 131–142.

    Article  Google Scholar 

  20. Filho, F., Maia, H. Z., Mateus, H. A., Ozpineci, B., Tolbert, L. M., & Pinto, J. O. P. (2013). Adaptive selective harmonic minimization based on ANNs for cascade multilevel inverters with varying DC sources. IEEE Transactions on Industrial Electronics, 60, 1955–1962.

    Article  Google Scholar 

  21. Enjeti, C. B. P., Ziogas, P. D., & Lindsay, J. F. (1990). Programmed pwm techniques to eliminate harmonics: A critical evaluation. IEEE Transactions on Industry Applications, 26, 302–316.

    Article  Google Scholar 

  22. Cetin, A., & Ermis, M. (2009). VSC-based D-STATCOM with selective harmonic elimination. IEEE Transactions on Industry Applications, 45, 1000–1015.

    Article  Google Scholar 

  23. Cetin, A., & Ermis, M. (2007). VSC based D-STATCOM with selective harmonic elimination. In IEEE 42nd industry applications conference (IAS’07) (Vol. 1, pp. 936–948).

  24. Abu-Rub, H., Guzinski, J., Krzeminski, Z., & Toliyat, H. A. (2004). Predictive current control of voltage source inverters. IEEE Transactions on Industrial Electronics, 51(3), 585–593.

    Article  Google Scholar 

  25. Zeng, Q., & Chang, L. (2008). An advanced SVPWM-based predictive current controller for three-phase inverters in distributed generation systems. IEEE Transactions on Industrial Electronics, 55(3), 1235–1246.

    Article  Google Scholar 

  26. Peng, F. Z. (2003). Z-source inverter. IEEE Transactions on Industry Applications, 39(2), 504–510.

    Article  Google Scholar 

  27. Peng, F. Z. (2002). Z source inverter. In IAS annual meeting conference on industry applications conference, 13–18 Oct. 2002 (Vol. 2, pp. 775–781).

  28. Luo, Z, Alam, M., & Hong, S. H. (2015). A hardware-in-the-loop simulator for demand response energy management in industrial facilities. In Proceedings of 2015 IEEE workshop on modeling and simulation of cyber-physical energy systems (MSCPES) (Vol. 618).

  29. Wei, M., Hong, S. H., & Alam, M. (2016). An IoT-based energy-management platform for industrial facilities. Applied Energy, 164, 607–619.

    Article  Google Scholar 

  30. May, G., Barletta, I., Stahl, B., & Taisch, M. (2015). Energy management in production: A novel method to develop key performance indicators for improving energy efficiency. Applied Energy, 149, 46–61.

    Article  Google Scholar 

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Sivaranjani, S., Rajeswari, R. RETRACTED ARTICLE: Internet of Things Based Industrial Automation Using Brushless DC Motor Application with Resilient Directed Neural Network Control FED Virtual Z-Source Multilevel Inverter Topology. Wireless Pers Commun 102, 3239–3254 (2018). https://doi.org/10.1007/s11277-018-5365-6

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  • DOI: https://doi.org/10.1007/s11277-018-5365-6

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