Acta Mechanica Slovaca 2017, 21(4):34-39 | DOI: 10.21496/ams.2017.034

Study of Closed-Loop Model Reference Adaptive Control of Smart MicroGrid with QNU and Recurrent Learning

Vladimír Malý1,5, Martin Veselý2,5, Peter M. Beneš3,5, Petr Neuman4, Ivo Bukovský5,*
1 VM ENGINEERING s.r.o. Klášterec nad Ohří, Czech Republic
2 FMV-DESIGN s.r.o. Trutnov, Czech Republic
3 Corporate Technology Development Center, SIEMENS s.r.o. Praha, Czech Republic
4 Neureg sdr., Praha, Czech Republic
5 CTU in Prague, Faculty of Mechanical Engineering, Czech Republic

An adaptive quadratic polynomial neural unit (QNU) controller for optimization of a conventional Smart Microgrid control loop is studied and proposed. The parameters associated with the studied grid plants are considered to be known in this study, with the fact that the load is unknown and time-variant. A sample-by-sample real-time recurrent learning algorithm of an additional QNU controller is derived, with its performance tested and discussed as a result of this paper.

Keywords: Smart Grids, Microgrids, Diesel Engine Generators (DEG), Photovoltaic panels (PV), Wind Turbine Generators (WTG), Flywheel Energy Storage System (FESS), Battery Energy Storage System (BESS), QNU Controller, Linear and Non-linear Controllers

Published: October 31, 2017  Show citation

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Malý, V., Veselý, M., Beneš, P.M., Neuman, P., & Bukovský, I. (2017). Study of Closed-Loop Model Reference Adaptive Control of Smart MicroGrid with QNU and Recurrent Learning. Acta Mechanica Slovaca, 21(4), 34-39. doi: 10.21496/ams.2017.034
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    References

    1. Bevrani, H., Habibi, F., Babahajyani, P., Watanabe, M., Mitani, Y., 2012. Intelligent Frequency Control in an AC Microgrid: Online PSO-Based Fuzzy Tuning Approach. IEEE Trans. Smart Grid 3, 1935-1944. doi:10.1109/TSG.2012.2196806 Go to original source...
    2. Bukovsky, I., Benes, P., Slama, M., 2015. Laboratory Systems Control with Adaptively Tuned Higher Order Neural Units, in: Silhavy, R., Senkerik, R., Oplatkova, Z.K., Prokopova, Z., Silhavy, P. (Eds.), Intelligent Systems in Cybernetics and Automation Theory, Advances in Intelligent Systems and Computing. Springer International Publishing, pp. 275-284. Go to original source...
    3. Bukovsky, I., Homma, N., Smetana, L., Rodriguez, R., Mironovova, M., Vrana, S., 2010. Quadratic neural unit is a good compromise between linear models and neural networks for industrial applications, in: 2010 9th IEEE International Conference on Cognitive Informatics (ICCI). Presented at the 2010 9th IEEE International Conference on Cognitive Informatics (ICCI), pp. 556-560. doi:10.1109/COGINF.2010.5599677
    4. Chowdhury, S., Chowdhury, S.P., Crossley, P., 2009. Microgrids and active distribution networks, IET renewable energy series. Institution of Engineering and Technology, Stevenage.
    5. Gibson, T.E., Annaswamy, A.M., Lavretsky, E., 2013. Adaptive Systems with Closed-loop Reference Models: Composite control and observer feedback. IFAC Proc. Vol. 46, 440-445. doi:10.3182/20130703-3-FR-4038.00144 Go to original source...
    6. Gupta, M.M., Jin, L., Homma, N., 2003. Static and dynamic neural networks: from fundamentals to advanced theory. Wiley, New York. Go to original source...
    7. Madan M. Gupta, Ivo Bukovsky, Noriyasu Homma, Ashu M. G. Solo, Zeng-Guang Hou, 2013. Fundamentals of Higher Order Neural Networks for Modeling and Simulation, in: Ming Zhang (Ed.), Artificial Higher Order Neural Networks for Modeling and Simulation. IGI Global, Hershey, PA, USA, pp. 103-133. Go to original source...
    8. Mitani, Y., Kudo, T., Satake, A., Basri, K.H., 2014. Monitoring the Wide Area Power System Dynamics by Phasor Measurement Units Based on Campus WAMS Strategy. IFAC Proc. Vol. 47, 2273-2278. doi:10.3182/20140824-6-ZA-1003.00771 Go to original source...
    9. Mori, H., Awata, A., 2006. A Hybrid Method of Clipping and Artificial Neural Network for Electricity Price Zone Forecasting. IEEE, pp. 1-6. doi:10.1109/PMAPS.2006.360234 Go to original source...
    10. Narendra, K.S., Valavani, L.S., 1979. Direct and indirect model reference adaptive control. Automatica 15, 653-664. doi:10.1016/0005-1098(79)90033-5 Go to original source...
    11. Neuman, P., Pokorny, M., Weiglhofer, W., 2014. Principles of Smart Grids on the generation electrical and thermal energy and control of heat consumption within the District Heating Networks. Proc. of 19th IFAC World Congress 2014, Cape Town, South Africa. Go to original source...
    12. Neuman, P., 2012. Smart Grids applications for improved Power System Control. Proc. of IFAC Symposium 2012, Toulouse, France. Go to original source...
    13. Neuman, P., 2011. Smart Grids methods of Transmission System Operators for improved Power System Control. Proc. of 18th IFAC World Congress 2011, Milan, Italy.

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