Overhead Power Line Failure Rates in the Power System Backbone Networks: Forecasts and Facts
DOI:
https://doi.org/10.24160/0013-5380-2026-7-25-31Keywords:
overhead lines, failure flow parameter, chaoticity, largest Lyapunov exponent, wavelet spectrum, neural networks, forecastingAbstract
The article compares the forecast estimates of the failure rates of 500 kV overhead lines (OLs) in a vast region, obtained by the authors in 2018 for the period 2019–2023 using spectral singular analysis and neural network methods, with actual reporting data. The approximately two-fold predicted growth in the power line failure rates within the above-mentioned time interval is confirmed. It is shown that the fuzzy neural network gave the most accurate prediction of the growth in the failure rate. The organizational structure of 500 kV overhead line failures in different time intervals has been analyzed, and the following observations have been made. Previously, social and natural factors had approximately equal influence on the failure rate of overhead lines, whereas nowadays the social sphere has obviously become the dominating factor. It has been found that the time series relating to the failure rate parameter of the lines under consideration has become more chaotic in nature: the largest Lyapunov exponent’s positive value shows a growth from 0.2 to 0.285. The latter circumstance entails an objective need to reduce the acceptable planning horizons from five to four years, since they, as is known from the theory of deterministic (dynamic) chaos, are inversely proportional to the value of this indicator. Modified approaches applied in using neural networks have been substantiated and serve as a basis to give forecast of the 500 kV overhead lines failure rate for the specified subsequent prospect.
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