Abstract
A block diagram of the decision-making process for choosing the preferred alternative for the development of the power supply system of the district of the region is proposed. It consists of local and global levels. The algorithm of the decision-making process for the development of an object of the power supply system at the local level is presented. Its main stages and operational actions are outlined. The choice of a scenario for the development of an object of the power supply system is to be made based on the index of the technical condition of the equipment and the value of the maximum load of the equipment. Multi-criteria evaluation and ranking of alternatives for the development of objects of the power supply system are proposed to be carried out by means of an artificial neural network (ANN). The ANN is trained by the error back propagation algorithm. For this purpose, the ANN architecture based on the F-measure is defined. The best result was F=0.98 for the Levenberg -Marquardt training algorithm with the number of neurons in the three hidden layers 6, 18, and 26, respectively. The results of a software-implemented decision-making algorithm for the development of a power supply system facility are presented.
Keywords
Power supply system, alternative development, decision-making.
1. Ada S., Ghaffarzadeh M. Decision making based on management information system and decision support system. European Researcher. 2015. Vol. 93. Iss. 4. Pp. 260-269. doi:10.13187/er.2015.93.260.
2. Berdin A.S., Semenova L.A. Integration of the technocenological approach and the theory of fuzzy sets in the problems of optimization of power supply systems. IzvestiyaVuzov. Problemyenergetiki [Power engineering: research, equipment, technology], 2010, vol 3-4, pp. 151-156. (In Russian).
3. Leshchinskaya T.B., Glazunov A.A., Shvedov G.V. Algorithm for solving multicriteria optimization problems with uncertain information on the example of choosing the optimal power of high-voltage deep input. Elektrichestvo [Electricity], 2004, vol. 10, pp. 8-14. (In Russian).
4. Liao J., He L., Yuan X., Li H. Application of decision support system to three gorges cascade hydropower station. Second International Conference on Genetic and Evolutionary Computing. IEEE, 2008, vol. 2, pp. 500-503. doi:10.1109/WGEC.2008.80
5. Andervazh M.R., Javadi S., Aliabadi M.H. Decision support system for multicriteria reconfiguration of power distribution systems using CSO and efficient graph traversal and repository management techniques. International Transaction Electrical Energy Systems. 2018. Iss. 8. e2579. doi:10.1002/ETEP.2579.
6. Flores W.C., Mombell E.E., Jardini J.A. Expert system for the assessment of power transformer insulation condition based on type 2 fuzzy logic systems. Expert Systems with Applications: An International Journal. 2011. Vol. 38. Iss. 7. Pp. 8119-8127. doi:10.1016/j.eswa.2010.12.153
7. Khalyasmaa A.I., Dmitriev S.A., Kokin S.E., Eroshenko S.A. Fuzzy neural networks' application for substation integral state assessment. WIT Transactions on Ecology and the Environment. 2014. Vol. 190. Pp. 599-605. doi: 10.2495/EQ140581
8. Ram S., Chandel A., Singh G., Mondal M. UV Spectrophotometer Based AI Techniques for Remnant Life Estimation of Power Transformers. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE). 2012. Vol. 3. Pp. 46-55. doi:10.9790/1676-0334655
9. Khalyasmaa A.I., Dmitriev S.A., Kokin S.E. An automated system for taking decisions to assess the actual state of electrical equipment. Power technology and Engineering. 2016. Vol. 49. Pp. 389-392. doi:10.1007/S10749-016-0634-6
10. Khalyasmaa A.I., Dmitriev S.A. Power equipment technical state assessment principles. Applied Mechanics and Materials. 2014. Vol. 492. Pp. 531-535. doi:10.4028/www.scientific.net/AMM.492.531
11. Khalyasmaa A.I., Dmitriev S.A., Kokin S.E., Glushkov D.A., Kuzin P. Development of 110-220 kV power transformer model for equipment functional state assessment system. Advanced Materials Research. 2014. Vol. 960-961. Pp.1347-1351. doi:10.4028/www.scientific.net/AMR.960-961.1347
12. Barriquello C.H., Garcia V.J., Schmitz M., Bernardon D., Fonini J.S. A decision support system for planning and operation of maintenance and customer services in electric power distribution systems. System Reliability. IntechOpen, 2017. Pp.355-370. doi:10.5772/intechopen.69721
13. Jones L.E. Strategies and decision support systems for integrative variable energy resources in control centers for reliable grid operations. Washington DC, Alstom Grid, 2012. 222 p.
14. Ramsay B., McPherson A., Eastwood R., Ozveren C., Glare S., Oatley J. A decision support system for electricity distribution network refurbishment projects. Electric Power Systems Research. 1997. Vol. 40. Pp. 27-35. doi:10.1016/S0378-7796(96)01129-7
15. Altunin A.Y., Semukhin M.V. Modeli i algoritmy prinyatiya resheniy v nechetkikh usloviyakh [Decision Models and Algorithms in Fuzzy Conditions]. Tyumen: Publishing house of Tyumen State University, 2000, 352p. (In Russian).
16. Semenova N.G., Chernova A.D. Selecting the Preferred Solution on Development of Electrical Supply System Based on Technology of Neural Networks. VestnikYUrGU Seriya: Energetika [Bulletin of South Ural State University. Series “Power Engineering”], 2018, vol. 18, no 3, pp. 38-45. doi: 10.14529/power180305. (In Russian).
17. Prikaz Ministerstva jenergetiki RF ot 26 ijulja 2017 g. № 676 "Ob utverzhdenii metodiki ocenki tehnicheskogo sostojanija osnovnogo tehnologicheskogo oborudovanija i linij jelektroperedachi jelektricheskih stancij i jelektri-cheskih setej" [Order of the Ministry of Energy of the Russian Federation of July 26, 2017 No. 676 "On approval of the methodology for assessing the condition of the main technological equipment and power transmission of stations and electric networks"]. Available at: https://www.garant.ru/ products/ipo/prime/doc/71679722/ (accessed 12 April 2021)
18. Postanovlenie Pravitel'stva RF ot 19.12.2016 №1401 "O kompleksnom opredelenii pokazatelej tehniko-jekonomicheskogo sostojanija ob’ektov jelektrojenergetiki, v tom chisle pokazatelej fizicheskogo iznosa i jenergetiche-skoj jeffektivnosti ob#ektov jelektrosetevogo hozjajstva, i ob osushhestvlenii monitoringa takih pokazatelej" [Decree of the Government of the Russian Federation of December 19, 2016 No. 1401 "On the comprehensive determination of indicators of the technical and economic condition of electric power facilities, including indicators of physical deterioration and energy efficiency of power grid facilities, and on monitoring such indicators"]. Available at: http://www.consultant.ru/ document/cons_doc_LAW_209223/ (accessed 12 April 2021)
19. Chernova A.D. Programma rascheta kriteriev ocenki al'ternativ razvitija sistem jelektrosnabzhenija [Program for calculating criteria for evaluating alternatives for the development of power supply systems]. Computer program RF, no. 2017611536, 2017.