DEVELOPMENT OF A METHOD FOR DIAGNOSING AND FORECASTING POWER SUPPLY SYSTEMS FOR MINING ENTERPRISES

Kuanyshtaeva A. Kotov Y. Smagulova K. Abilzhanova F.
2026 Technology Center

Eastern-European Journal of Enterprise Technologies
2026 #1 Issue 8 (139)15 - 26 pp.

The object of the study is the power supply system of the mining enterprise NovaZinc LLP, located in Central region of Republic of Kazakhstan, which specializes in the extraction and enrichment of leadzinc ores. Reliable power supply in mining is challenged by heavy mechanical loads, vibration, and severe weather; this case study is used only as an example of conditions common in many mining regions worldwide. This study proposes a diagnostic-and-forecasting method for distribution power systems based on routinely available operational records and climatic indicators. The method was tested using outage data from 2020–2024. Using least squares, a multivariate regression model was obtained for feeder emergency outage duration as a function of cable damage (F5), transformer failure (F6), and the climatic factor (Climate). The model is significant overall (Ftest p < 0.01) and explains 68.7% of downtime variation (R² = 0.687); residual diagnostics indicate normality and no autocorrelation. The average marginal effects are 7.561 h for cable failures, 3.314 h for transformer failures, and 2.418 h for climatic impacts, highlighting cables as the dominant driver of prolonged outages. To assess energy performance, a separate model was built for the loss share in the power system as a function of outage duration, phase clashing (F3), and Climate. This loss model has low explanatory power (R² = 0.2013) and nonsignificant factor coefficients (p > 0.05). Finally, bivariate regressions show that Climate strongly affects phase clashing (F3) (R² = 0.793) and moderately affects ground faults (F1) and insulator chipping (F2) (R² = 0.533 each). The proposed method supports maintenance prioritization, climatemitigation measures, and continuous updating as new outage records are added, strengthening decisionmaking and system robustness Copyright

diagnostics , failure prediction , mining enterprise , power supply , regression analysis , reliability

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Department of Automation of Production Processes, Abylkas Saginov Karaganda Technical University, N. Nazarbayev ave., 56, Karagandy, 100000, Kazakhstan
Department of Automation of Production Processes, Abylkas Saginov Karaganda Technical University, N. Nazarbayev ave., 56, Karagandy, 100000, Kazakhstan
Department of Energy Systems, Abylkas Saginov Karaganda Technical University, N. Nazarbayev ave., 56, Karagandy, 100000, Kazakhstan

Department of Automation of Production Processes
Department of Automation of Production Processes
Department of Energy Systems

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