Heuristic control of power consumption by up to 1000 V electrical loads at mining enterprises

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O.M.Sinchuk, orcid.org/0000-0002-9078-7315, Kryvyi Rih National University, Kryvyi Rih, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

M.V.Rogoza, orcid.org/0000-0002-2395-227X, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

O.Yu.Mykhailenko*, orcid.org/0000-0003-2898-6652, Kryvyi Rih National University, Kryvyi Rih, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

D.V.Kobeliatskyi, orcid.org/0009-0006-1308-7426, Kryvyi Rih National University, Kryvyi Rih, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

V.O.Fedotov, orcid.org/0000-0002-6536-5591, Kryvyi Rih National University, Kryvyi Rih, Ukraine

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2024, (1): 084 - 091



To develop a method for synthesizing the structure and algorithm of the system for automated control of power consumption by up to 1000 V electrical receivers at mining enterprises with iron ore underground mining methods. This enables direct control of the load connection to the industrial power grid to ensure minimum power costs depending on its cost per day ahead.

The problem of controlling power consumption of electrical receivers at iron ore underground mines is formalized as a binary form of mixed integer programming. To solve it, a binary implementation of the heuristic genetic algorithm is used. The mathematical modeling method analyzes the impact of genetic algorithm settings, such as the number of phenotypes in the population, the number of elite phenotypes that pass unchanged to the next generation, and the method of phenotype crossover on its quality.

As a result of the research, it is found that the most effective way to control the process of power consumption based on an evolutionary genetic algorithm is to use the Laplace crossover function and keep the percentage of elite phenotypes in the population at 10 %. Moreover, at the smallest population size, the best accuracy is observed when using the Laplace function, while at one- and two-point crossover functions, it worsens, but not significantly (no more than 0.2 %). However, as the number of elite phenotypes increases, the duration of the evolutionary search in the control process is reduced by almost a factor of two in the case of one- and two-point crossovers.

For the first time, the structure of a heuristic system for automated control of power consumption by underground electrical receivers with a supply voltage of up to 1000 V at iron ore underground mines has been developed on the basis of an evolutionary genetic algorithm. Depending on the designed volumes of ore production and the daily power cost per day, this allows determining the optimal power load schedule of underground distribution substations in advance, which, subject to the accepted limits on hourly and daily power, minimizes the cost of purchasing power, and thus reduces the cost of the final product.

Practical value.
The architecture of a heuristic system for controlling power consumption by electrical receivers with a voltage of up to 1000 V based on an evolutionary genetic algorithm is developed and recommended when optimizing the power load schedule of transformer substations of mining and metallurgical enterprises, in particular, of iron ore underground mines operating in this voltage class.

power, up to 1000 V electrical receivers, heuristic algorithm, genetic algorithm, underground mine


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