Abstract
This paper is devoted to the development of scientific and methodological foundations of improvement the information support of decision at management of power network with distributed generation. It is proposed to consider the power quality index as the main criterion of management. Using the theory of fuzzy sets, the assessment of the conformity of power quality indicators to electric energy quality limits is done. A method for estimating the quality of electrical energy is proposed which represents the measured histogram as a fuzzy representation indicator of quality of electric energy in the form of fuzzy sets with a step membership function. The method of integral evaluation of electrical energy quality for different types of load is developed. The presented method allows to formulate rules for managing the operating modes of the distributed electrical network by the decision support system.
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References
Pakštas, A., Shulyma, O., Shendryk, V.: On defining and assessing of the energy balance and operational logic within hybrid renewable energy systems. Commun. Comput. Inf. Sci. 639, 151–162 (2016)
Shulyma, O., Shendryk, V., Parfenenko, Y., Shendryk, S.: The model for decision support on design of the hybrid renewable energy system. In: Proceedings of the 2017 IEEE 9th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2017, pp. 47–50 (2017)
EN 50160:2010 Voltage Characteristics of electricity supplied by public distribution networks
Guide to the Expression of Uncertainty in Measurement: First Edition, p. 101. ISO, Switzerland (1993)
Mauris, G., Lassere, V., Foulley, L.: A fuzzy approach for the expression of uncertainty in measurement. Measurement 29, 109–121 (2001)
Mauris, G., Berrah, L., Foulloy, L., Haurat, A.: Fuzzy handling of measurement errors in instrumentation. IEEE Trans. Measur. 1(49), 43–58 (2000)
Canha, L.N., Pereira, P.R., Milbradt, R., Da Rosa Abaide, A., Schmitt, K.E.K., De Abreu Antunes, M.: Intelligent voltage regulator to distributed voltage control in smart grids. In: 52nd International Universities Power Engineering Conference, UPEC 2017, 2017 January, pp. 1–6 (2017). https://doi.org/10.1109/upec.2017.8231977
Arcos-Aviles, D., Pascual, J., Marroyo, L., Sanchis, P., Guinjoan, F.: Fuzzy logic-based energy management system design for residential grid-connected microgrids. IEEE Trans. Smart Grid 9(2), 530–543 (2018). https://doi.org/10.1109/TSG.2016.2555245
Liu, X.N., Wei, J., Ye, S.Y., Chen, B., Long, C.: Research on uncertainty evaluation measure and method of voltage sag severity. In: IOP Conference Series: Earth and Environmental Science, vol. 108, no. 5 (2018). https://doi.org/10.1088/1755-1315/108/5/052098
Miroshnik, A.A., Tymchuk, S.A.: Uniform distribution of loads in the electric system 0,38/0,22 kV using genetic algorithms. Tech. Electrodynamics 4, 67–73 (2013)
Piegat, A.: Fuzzy Modeling and Control. Phisical-Verl, Heidelberg, New York (2001). 728
Tymchuk, S.A., Miroshnyk, A.A.: Quality assessment of power in distribution networks 0.38/0.22 kV in the fuzzy form. In: Global Science and Innovation. Materials of the II International Scientific Conference, vol. II, Chicago, USA, pp. 288–299 (2014)
Tymchuk, S.A., Miroshnyk, A.A.: Assess electricity quality by means of fuzzy generalized index. East Eur. J. Adv. Technol. 3/4(75), 26–31 (2015)
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Tymchuk, S., Miroshnyk, O., Shendryk, S., Shendryk, V. (2018). Integral Fuzzy Power Quality Assessment for Decision Support System at Management of Power Network with Distributed Generation. In: Damaševičius, R., Vasiljevienė, G. (eds) Information and Software Technologies. ICIST 2018. Communications in Computer and Information Science, vol 920. Springer, Cham. https://doi.org/10.1007/978-3-319-99972-2_7
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DOI: https://doi.org/10.1007/978-3-319-99972-2_7
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