The Multifactorial Approach to Power Quality Analysis in Underground Mining

Abstract

This research presents the development of a multifactorial static multiplicative model for analysing power quality in underground mining power systems. The objective is to synthesize a generalized indicator of power quality by integrating key parameters such as voltage dips and sags, frequency deviations, harmonic distortion, and other critical indicators that influence the energy efficiency and reliability of the electrical network. The proposed model structure was developed using the synthesis method, with its parameters identified through a maladaptive approach based on the least squares method. To validate the model's accuracy, mathematical statistics techniques were employed. As a result, mathematical relationships were derived to evaluate a generalized power quality index using data on voltage drop, frequency deviation, and harmonic distortion. The model, characterized as static and multiplicative, requires full-spectrum quality data for parameter identification via a non-adaptive approach. Comparative accuracy analysis between a single-factor model and the proposed three-factor model revealed a correlation coefficient of 0.951 for the former and 0.923 for the latter. While the multifactor model demonstrates a 2.94% reduction in statistical accuracy, both models qualify as having "very high" reliability according to the Chaddock scale. This confirms the practical applicability of the multifactor approach in real-world mining energy systems. The scientific novelty lies in the improved multifactor model structure that synthesizes multiple quality indicators into a unified framework. Its practical value is evident in applications for managing power flow within industrial microgrids in underground mines, particularly those integrating local power generation sources.