The algorithm for artificial neural networks is presented for the optimal distribution of tasks in electric networks in an automatic mode without operator participation. The article presents the artificial neural networks algorithm based on Levenberg-Marquardt approach that implements the specified task, as well as substantiation of its characteristics. It is proposed to use the technology of artificial neural networks (ANN), which on the basis of the developed multi-criteria evaluation electricity system of ARES allows ranking. The ANN architecture with Levenberg-Marquardt algorithm of weights optimization and their efficiency is estimated. As indicators of efficiency, the F-measure and the percentage of correctly made decisions (accuracy) were chosen for optimal network parameters. The obtained ANN was successfully tested.
The paper considers the wavelet decomposition for machine learning model to solve the problem of analyzing the shape of vibration signals in wind energy turbines. The questions of training and optimization of the parameters of the classmate method are highlighted. The principle of constructing a training sample is described. The algorithm for the instance recognition of circuit elements by vibration signals removed from wind turbines. Thus, the method of recognition of equipment elements based on sparse wavelet decomposition of vibration signals can be used in practical renewable energy. The research results will stimulate of the development of generating facilities based on wind energy with an installed capacity of up to 15 kW will contribute to the stimulation of the development of wind power with a vertical axis in the urban environment.
The paper proposes the radical transformation of the global energy market is influenced by a combination of geopolitical, macroeconomic, technological realities, the combination of which leads to fundamental changes in the world order in the development of the gas segment. The paper uses the method of energy balance. It proved ensuring growth in gas production and transportation, production and sale of high value-added gas products in the domestic, European and Asian markets. The onset of the Golden Age of Gas, according to the forecast of the International Energy Agency (IEA), is expected by 2035, when global gas consumption will increase by one and a half times. The study result is that the expansion of the range of gas resources and the modernization of the structure of the gas industry, the formation of the latest Eurasian energy architecture in the face of increasing competition in international markets necessitate the strengthening of Russia's leading positions in the global gas market.
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