This article is devoted to the analysis of the possible influence of impedance asymmetry on the efficiency of electricity transmission and distribution in the electricity system in Slovakia, at a voltage level of 110 kV -400 kV, using synchronic phasor monitoring results. For simplicity of calculations, in practice, the impedance imbalance from mutual interfacial inductive capacitances bonds is neglected. In this way, the 3-phase network is interpreted as symmetrical in the calculations. In this case, it is possible to determine only some components of losses (ohmic losses, corona loss, leakages, etc). The influence of impedance asymmetry can be quantified by calculation using the results of the monitoring of the synchronous phasors of selected electricity system elements (OHL, transformer, choke) or by 3-phase modelling of real system elements. frequency to test the transformer for induced over voltage test, and its characteristics is analysed.

The submitted contribution is devoted to the issue of the existence of latent losses on the overhead lines. Quantification of latent losses of electricity is a very complicated task, which has been solved in practice for several years. In their assessment, however, often appear some inconsistencies which cause is often unknown. The magnitude of these losses in some cases cannot be negligible. From the point of view of maintaining an efficient and reliable operation of distribution and transmission systems in the future, attention will still need to be paid to the problem of latent loss analysis.

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This paper addresses the regression modeling of local environmental pollution levels for electric power industry needs, which is fundamental for the proper design and maintenance of high-voltage transmission lines and insulators in order to prevent various hazards, such as accidental flashovers due to pollution and the resultant power outages. The primary goal of our study was to increase the precision of regression models for this application area by exploiting additional input attributes extracted from satellite imagery and adjusting the modeling methodology. Given that thousands of different attributes can be extracted from satellite images, of which only a few are likely to contain useful information, we also explored suitable feature selection procedures. We show that a suitable combination of attribute selection methods (relief, FSRF-Test, and forward selection), regression models (random forest models and M5P regression trees), and modeling methodology (estimating field-measured values of target variables rather than their upper bounds) can significantly increase the total modeling accuracy, measured by the correlation between the estimated and the true values of target variables. Specifically, the accuracies of our regression models dramatically rose from 0.12–0.23 to 0.40–0.64, while their relative absolute errors were conversely reduced (e.g., from 1.04 to 0.764 for the best model).

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