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One of the main tasks of the last two decades is to find ways to optimize energy consumptions for aircrafts. The commercial aviation business is increasingly using environmental monitoring systems and electrical control by using AC and DC tires. One of the trends in the development of aircraft control systems is the replacement of hydraulic and pneumatic systems with electrical ones. The aerospace industry and airlines are interested in performing steering operations without major engines. This operation method allows to save fuel, reduce brake wear, eliminates towing and achieve decreasing of environmental pollution. In the future it is necessary to implement electric steering using a traction drive (TD) based on a synchronous motor with permanent magnets (PMSM). This system is powered by an available auxiliary power unit or other sources such as fuel cells or batteries. This study presents a highly efficient electric steering system as a modern solution for improving the ground operations of modern aircraft powered by main engines. The system was investigated using steering profiles for takeoff and landing. The study determined the effectiveness of its use for steering. The influence of external factors and the change of parameters of the electromechanical system of wheel with an elastic tire were investigated. The results of modeling the dynamic processes of an electromechanical system containing elastic links in the conditions of parametric perturbations confirmed the robust stabilization of dynamic control quality indicators based on the laws of fuzzy logic.

Since in Ukraine there are fines for imbalances in solar power generation in the “day-ahead” energy market, the forecasting of electricity generation is an important component of the solar power plant operation. To forecast the active power generation of photovoltaic panels, a mathematical model should be developed, which considers the main factors affecting the volume of energy generation. In this article, the main factors affecting the performance of solar panels were analysed using correlation analysis. The data sets for the construction of the forecasting model were obtained from the solar power plant in the Kyiv region. Two types of data sets were used for the analysis of factors and model building: 10-minute time interval data and daily data. For each data set, the input parameters were selected using correlation analysis. Considering the determining factors, the models of finding the function of reflecting meteorological factors in the volume of electricity generation are built. It is established that through models with a lower discreteness of climatic parameters forecast it is possible to determine the potential volume of electricity production by the solar power plant for the day-ahead with a lower mean absolute error. The best accuracy of the model for predicting electric power generation over the 10-minute interval is obtained in the ensemble random of a forest model. It is determined that models without solar radiation intensity parameters on the input have an unsatisfactory coefficient of determination. Therefore, further research will focus on combining a model of forecasting the day-ahead solar radiation with 10-minutes discreteness with a model for determining the amount of electricity generation. The determined predicted values of solar radiation will be the input parameter of the forecasting model described in the article

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The new model of the wholesale electricity market in Ukraine causes appearance the market for the day ahead. In this market, the generating company undertakes to supply a certain amount of electricity. So, it is necessary to carry on the most accurate forecast of possible electricity generation by solar power plant (SPP). Generation value depends on certain factors. A brief summary of different influence of parameters on the PV cell performance has been provided. The article analyzes and identifies the factors that should be included in the forecast mathematical model of electricity generation by a solar power plant for a certain short-term period. According to analyzed data from SPP located in the Kyiv region, such parameters are the intensity of solar radiation, temperature and humidity, wind speed, and atmospheric pressure. The degree of influence of these factors on the initial function of electric energy generation were estimated by analyzing the scatter plot diagrams of relationship between parameters and correlation coefficients. Thus, the analysis of the influence of factors on the magnitude of electricity generation allowed to determine the priority of including each of the parameters in the mathematical model of the SPP power forecast. It was established that the influence of certain climate parameters for target function is different in each season. Therefore, in the mathematical model for forecasting electric power generation, it is necessary to take into account seasonality. In addition, the dynamic value change of factors also affects the current magnitude of electricity generation. Moreover, at different times of the year and with different combination of the corresponding values of climatic parameters, this effect may have different magnitudes. Therefore, the data obtained from the last periods before the forecasting should have a greater impact on obtaining the predicted value than the data from previous periods.