Development of algorithms for software implementation of prediction models of technical electricity losses in 6-35 kV overhead power lines
DOI:
https://doi.org/10.15587/2313-8416.2016.85480Keywords:
electricity losses, overhead power lines, neural networks, model, software implementationAbstract
Software implementation of prediction model of technical electricity losses in 6-35 kV overhead power lines (OHPL) is done. The algorithm for software implementation of model is developed, description of input variable parameters and the logical relationship of database elements ("entity-relationship" model) are realized. Testing of the proposed software is done on new data and comparative analysis of calculating electricity losses in overhead power lines, calculated in the proposed software with the data of automated control systems and electricity metering (ACSEM) and other approaches
References
The Cabinet of Ministers of Ukraine (1997). A comprehensive state program of energy saving in Ukraine, 148. Available at: http://zakon3.rada.gov.ua/laws/show/148-97-%D0%BF
Krasovsky, P. Y. (2006). Factors affecting the dynamics of technical losses in power lines. Proceedings of the Dnipropetrovsk National Mining University, 3.
Miroshnik, A. A. (2010). Refined algorithms for calculating the energy losses in networks 0,38 kV in real time. Regional energy issues, 2 (13), 35–42.
Turbin, S. V. (2007). Improving methods for determining environmental loads on air routes taking into consideration subject topographical features of the area. Energy and Electrification, 10, 3–9.
Hamid, B. (1992). Automated load forecasting using neural networks. Proc. Amer. Power Conf. Chicago, 54, 1149–1153.
Gupta, P., Yamada, K. (1972). Adaptive Short-Term Forecasting of Hourly Loads Using Weather Information. IEEE Transactions on Power Apparatus and Systems, PAS-91 (5), 2085–2094. doi: 10.1109/tpas.1972.293541
Panuska, V. (1977). Short-term forecasting of electric power system load from a weather dependent model. IFAC Symp. autom. contr. and prot. electr. power syst. Melbourne, 414–418.
Vorotnitsky, V. E., Turkina, O. V. (2008). Estimation of variable energy losses error in overhead lines because of the weather conditions neglect. Energy systems and electrical networks, 10.
Levchenko, I. I., Satsuk, E. I. (2008). Сarrying capacity and monitoring of overhead power lines in extreme weather conditions. Electricity, 4, 2–8.
Zhelezko, Y. S. (2004). Loss of electric energy in electric grids, depending on weather conditions. Power station, 11.
Ministry of Energy and Coal Industry of Ukraine (2011). Method for determining the technological power consumption of transshaper and power lines, 532. Available at: http://www.mega-billing.com/files/metodika_vtrat.pdf
Osipov, D. S. (2005). Recording of heating of current-carrying parts in the calculation of the power loss and power at non-sinusoidal modes of electric power systems. Omsk: RGB.
Glebov, A. A. (2007). Model of short-term forecasting of power consumption by using neuro-fuzzy systems. Astrakhan: RGB, 21.
Bakulevskiy, V. (2016). Research into the influence of climatic factors on the losses of electric energy in overhead power transmission lines. Eastern-European Journal of Enterprise Technologies, 5/8 (83), 4–8. doi: 10.15587/1729-4061.2016.80072
Borovikov, V. P. (Ed.) (2008). Neural networks. Statistica Neural Networks. Methodology and technology of modern data analysis. Moscow, Hotline-Telecom, 392.
Neural networks. STATISTICA Neural Networks (2001). Moscow: Hotline-Telecom, 654.
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