This article is devoted to the relevant problem of increasing the efficiency of PV systems. The presented analysis discusses the available methods for improving the power generation of PV modules under partial shading. Mathematical models for power loss calculation were compiled based on the results of this analysis. The proposed approach minimizes the negative impact of partial shading on the energy production of PV modules. It is based on the equalization of voltages of parallel-connected arrays of modules by installing additional power elements in them. The proposed solution is promising for various areas; it allows for the minimization of the unfavorable influence of existing urban objects (buildings, trees, communications, etc.) on the energy efficiency of PV modules. The obtained results are useful for the sustainable development of the urban environment in the context of digital transformation. They are the basis for the promising methodology of the parametric optimization of power plants using renewable energy sources.

To minimize the existing risks associated with the energy production, distribution, and development of urban areas, the concept of sustainable development of smart manufacturing is being introduced everywhere. However, most of existing digital transformation technologies do not sufficiently take into account environmental, economic, and social components. It requires the development of specialized solutions that, on the one hand, contribute to the development of the urban environment, and on the other, are focused on the use of innovative technologies that have a positive effect on the environment. However, in order to provide electric power to objects of a smart urban environment, we propose using autonomous power plants with renewable energy sources (wind and sun) instead of classical central power supply. The article describes the benefits of using combined wind-solar power plants to provide electricity to smart urban environments. The article considers advantages and disadvantages of this solution. We describe the design of the combined wind-solar installation with a vertical Darier rotor and photoelectric converters located on a common supporting structure. This solution allows getting a positive synergistic effect from the use of several renewable energy sources. The presented main advantages of this solution include increasing the energy efficiency of photovoltaic converters due to the intensification of heat removal from the surface of solar cells by the wind flow from the Darier rotor. In addition, this article considers the efficient use of the area and stability of electricity output. Digital aerodynamic modeling performed the parametric optimization of the Darier rotor having three blades with NACA 0021profile. The simulation demonstrated that the blades installed at an angle of 3.8° provide the maximum value of the wind utilization coefficient. This article also considers parametric optimization of a photovoltaic installation. In particular, the optimal installation angle of photovoltaic modules for Sevastopol is 34°.

The need for application of a device for online remote monitoring of electrical parameters and the operability of photovoltaic converters of solar power plants is justified in the context of...

One of the most important conditions for the efficient operation of solar power plants with a large installed capacity is to ensure the systematic monitoring of the surface condition of the photovoltaic modules. This procedure is aimed at the timely detection of external damage to the modules, as well as their partial shading. The implementation of these measures solely through visual inspection by the maintenance personnel of the power plant requires significant labor intensity due to the large areas of the generation fields and the operating conditions. Authors propose an approach aimed at increasing the energy efficiency of high-power solar power plants by automating the inspection procedures of the surfaces of photovoltaic modules. The solution is based on the use of an unmanned aerial vehicle with a payload capable of video and geospatial data recording. To perform the procedures for detecting problem modules, it is proposed to use “object-detection” technology, which uses neural network classification methods characterized by high adaptability to various image parameters. The results of testing the technology showed that the use of a neural network based on the R-CNN architecture with the learning algorithm—Inception v2 (COCO)—allows detecting problematic photovoltaic modules with an accuracy of more than 95% on a clear day.

The increase in the frequency and intensity of hazardous hydrometeorological phenomena is one of the most dangerous consequences of climate instability. In this study, we summarize the data on severe weather phenomena using the data from 23 meteorological stations in Crimea from 1976 to 2020. Particular attention was paid to the precipitation phenomena descriptions. For the last 45 years, a significant positive trend of interannual variability of the annual occurrence of severe weather phenomena was estimated to be +2.7 cases per decade. The trend for severe precipitation phenomena was estimated to be +1.3 cases per decade. The probable maximum annual daily precipitation as a quantitative indicator of hazardous events was estimated for each station by using both the stationary and the non-stationary GEV models. For at least half of the meteorological stations, a non-stationary GEV model was more appropriate for the estimation of the precipitation extremes. An analysis of the main synoptic processes that drive severe weather phenomena of precipitation was carried out. The greatest contribution to the formation of severe precipitation was made by Mediterranean–Black Sea cyclones. At the same time, half of all of the cases of extreme precipitation were caused by cyclones generated over the Black Sea only, in all seasons apart from winter. In the mid-troposphere, four types of meridional circulation were identified depending on the location of troughs and ridges, with respect to the Black Sea region. More than 42% of severe precipitation phenomena were accompanied by an isolated high-altitude cyclone in the mid-troposphere over the Black Sea region. The main recommendation that can be drawn from this study is that long-term climatic non-stationarity should be taken into account whenever the risk assessment or hazard analysis is to be carried out. The results can also favor the designing of drainage and sewerage systems in urban areas. The findings of atmospheric patterns can be used for the improvement of extreme precipitation forecasts.

Relevance. Proactive management of the processes of effective realization of the varietal potential of grapes is associated with the need to introduce innovative digital technologies for automated monitoring of heterogeneous data sources characterizing agro-climatic conditions and degradation processes of the biological state of plants. Currently, there is a steady trend aimed at digitalization of the viticulture and winemaking industry. There is a whole complex of scientific, practical, technical, technological tasks associated with the introduction of digital technologies for collecting the necessary information, aggregating it and creating a pre-processing technique for implementing procedures for multifactorial data analysis with their further use in decision support systems. The solution of the above-described tasks of a systemic nature requires the creation of scientific and methodological foundations for the implementation of intelligent adaptive automated monitoring of various objects and processes of agricultural enterprises.Methods. The above technology is based on the complex use of methods of technical vision, neural network classification and detection of grape leaves, evaluation of the quality of training neural network algorithms, video recording methods when using unmanned aerial vehicles (UAVs).Results. The results of the development of information technology for automated neural network detection of signs of deterioration of grape plantations for proactive management of the processes of effective realization of the varietal potential of grapes are presented. The technology allows the vineyard service personnel to promptly receive information about signs of deterioration of the condition of grape plantations based on video recording data of grape plants obtained using UAVs in static and dynamic mode. The results of testing the accuracy of detecting affected leaves showed that the mAP value of the trained neural network is at least 91%, which is sufficient to identify problem areas.