The relevance of this study is due to the fact that the presented object of scientific work, namely 6–10 kV overhead lines, plays an important role in the process of providing electrical energy to consumers of the oil industry. The aim of the work is a detailed analysis of the reliability of overhead lines which are exploited in the difficult natural and climatic conditions of the Caspian region and Mangyshlak and the introduction of effective modeling tools for overhead lines. The methods used include the analytical method, theoretical method, logical analysis method, functional method, statistical method, synthesis method and others. In the course of the study, the natural and climatic conditions of the Atyrau region and their differences were noted and the reliability of the power supply systems was also analyzed. The most damaged elements of industrial power supply systems and their part of failures were identified in comparison with other elements of the power supply system. It was determined that the electrical power sector plays a crucial role in the oil and gas sector by determining the solution of the production tasks of all departments which have a significant impact on the formation of economic indicators. The practical value of the revealed results is that they will help to highlight the problems of operational reliability of the 6–10 kV overhead lines, considering the various natural and climatic factors, which in turn will help to change the power supply scheme and increase the resistance to external influences.

Against the background of increasing installed capacity of wind power in the power generation system, high-precision ultra-short-term wind power prediction is significant for safe and reliable operation of the power generation system. We present a method for ultra-short-term wind power prediction based on a copula function, bivariate empirical mode decomposition (BEMD) algorithm and gated recurrent unit (GRU) neural network. First we use the copula function to analyze the nonlinear correlation between wind power and external factors to extract the key factors influencing wind power generation. Then the joint data composed of the key factors and wind power are decomposed into a series of stationary subsequence data by a BEMD algorithm which can decompose the bivariate data jointly. Finally, the prediction model based on a GRU network uses the decomposed data as the input to predict the power output in the next four hours. The experimental results show that the proposed method can effectively improve the accuracy of ultra-short-term wind power prediction.