Internal Benchmarking of Thermal Power Plants оf Electric Power Systems

Improving the operational efficiency (OE) of thermal power plants is one of the most important problems of electric power systems (EPS). According to modern concepts, efficiency is the simultaneous consideration of three properties of objects, viz. economy, reliability and safety. The methodology of their joint assessment assumes that the service life of the main equipment does not exceed the standard value, but this condition is now met by less than half of the production enterprises of a lot of EPS. In order to increase OE, it is necessary, first of all, to learn how to objectively compare the performance of objects both of the same type – in a given time interval, and unique ones – in adjacent intervals. Existing methods for calculating integrated performance indicators do not fully take into account the random nature of technical and economic indicators (TEI). The article presents a new method for comparing the OE of EPS objects, the essence of which is to switch from joint consideration of TEI to analysis of their relative changes in comparison with the factory default value (nominal value). Relative values of indicators characterize the amount of wear or residual life. In this case, for example, the arithmetic mean of the relative values of the TEI determines the average wear of the object. This physical representation enlivens integral indicators, and their comparison and ranking ceases to be science-intensive. It is proposed to take into account also the degree of variation of relative deviations (wear), which is adequate to the object’s misalignment. It manifests itself in a significant change (deterioration) of one or (less often) two relative values of the TEI in the calculated time interval (month) and is characterized by such statistical indicators as the geometric mean and the coefficient of variation of relative deviations. Herewith, if the arithmetic mean value of the object’s wear is restored during major repairs, then the misalignment is eliminated much faster – during current repairs. A necessary condition for the feasibility of using these or those integral indicators is their functional and statistical independence. The results of the studies performed using the simulation method made it possible to establish that the smallest correlation occurs between the integral indicator calculated as the arithmetic mean of random variables and the integral indicator calculated as the coefficient of variation of the same random variables. Comparison of correlation fields clearly confirms these conclusions.

internal benchmarking, integrated indicator, operational efficiency, economy, safety, method, risk of the erroneous decision

1. Voropai N. I. et al. (2013) The Concept of Ensuring Reliability in the Electric Power Engineering. Moscow, Energiya Publ., 2013. 304 (in Russian).

2. Farkhadzade E. M., Muradaliev A. Z., Ismailova S. M., Yusifli R. F. (2019) Quantitative Assessment of Fire Hazard of Electric Power System Objects. Energetik, (8), 10–15 (in Russian).

3. Vasilieva L. (2017) Analysis of Methodical Approaches to the Development of Integral Economic Indicators. Ekonomicheskie Issledovaniya i Razrabotki = Economic Development Research Journal, (12). Available at: http://edrj.ru/article/18-12-17 (in Russian).

4. D'yakov A. F., Isamukhammedov Ya. Sh., Molodyuk V. V. (2014) Problems and Ways to Improve the Reliability of the UES of Russia. Metodicheskie Voprosy Issledovaniya Nadezhnosti Bol'shikh Sistem Energetiki. Vyp. 64. Nadezhnost' Sistem Energetiki: Dostizheniya, Problemy, Perspektivy [Methodological Problems in Reliability of Large Energy System. Is. 64. Reliability of Energy Systems: Achievements, Problems, Prospects]. Irkutsk: ISEM SB RAS, 8–16 (in Russian).

5. Farhadzadeh E. M., Muradaliyev A. Z., Farzaliyev Yu. Z. (2016) Quality Evaluation of the TPP Power Generating Units Wear Reconditioning. Energetika. Izvestiya Vysshikh Uchebnykh Zavedenii i Energeticheskikh Ob’edinenii SNG = Energetika. Proceedings of CIS Higher Education Institutions and Power Engineering Associations, 59 (1), 14–24. https://doi.org/10.21122/1029-7448-2016-59-1-14-24 (in Russian).

6. Farhadzadeh E. M., Muradaliyev A. Z., Calaqova E. I., Abdullayeva S. A. (2019) Method and Algorithm of Comparison Efficiency of Gas and Piston Power Stations of the Electro Power Systems. Izvestiya Rossiiskoi Akademii Nauk. Energetika [Proceedings of the Russian Academy of Sciences. Power Engineering], (2), 106–117 (in Russian).

7. Farhadzadeh E. M., Farzaliyev Yu. Z., Muradaliyev A. Z., Ismailova S. M. (2017) Methods and Algorithms for Comparing and Ranking the Reliability and Efficiency of EPS Facilities Based on Different Types of Data. Elektrichestvo, (8), 4–13. https://doi.org/10.24160/0013-5380-2017-8-4-13 (in Russian).

8. Falin G. N., Falin A. L. (2006) Inequality for Averages. Matematika [Mathematics], (10), 25–36 (in Russian).

9. Gnedenko B. V., Belyaev Yu. K., Solov'ev A. D. (2019) Mathematical Methods in Reliability Theory: Basic Characteristics of Reliability and their Statistical Analysis. 3rd ed. Moscow, URSS Publ. 584 (in Russian).

10. Farhadzadeh E. M., Muradaliyev A. Z., Rafieva T. K., Rustamova A. A. (2019) Improving the Operational Efficiency of Thermal Power Plant Units. Elektricheskie Stantsii = Electrical Stations, (8), 14–17 (in Russian).

11. Farhadzadeh E. M., Muradaliyev A. Z., Farzaliyev Yu. Z. (2015) Distribution of a Continuous Random Variable Sample. Èlektronnoe Modelirovanie = Electronic Modeling, (6), 69–82 (in Russian).

12. Farhadzadeh E. M., Muradaliyev A. Z., Farzaliyev Yu. Z., Rafiyeva T. K., Abdullayeva S. A. (2018) Minimization of Risk of the Erroneous Decision in the Assessment of the Importance of Statistical Relations of Technical and Economic Indicators of the Objects of Electric Power Systems. Energetika. Izvestiya Vysshikh Uchebnykh Zavedenii i Energeticheskikh Ob’edinenii SNG = Energetika. Proceedings of CIS Higher Education Institutions and Power Engineering Associations, 61 (3), 193–206. https://doi.org/10.21122/1029-7448-2019-61-3-193-206 (in Russian).

13. Kendall M. G., Stuart A. (1968) The Advanced Theory of Statistics. In 3 volumes. 2nd ed. New York, Hafner Publ. Co. 557.