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Wave Energy Converters (WECs) need to be deployed in large numbers in an array layout in order to have a significant power production. Each WEC has an impact on the incoming wave field, by diffracting, reflecting and radiating waves. Simulating the wave transformations within and around a WEC array is complex; it is difficult, or in some cases impossible, to simulate both these near-field and far-field wake effects using a single numerical model, in a time- and cost-efficient way in terms of computational time and effort. Within this research, a generic coupling methodology is developed to model both near-field and far-field wake effects caused by floating (e.g., WECs, platforms) or fixed offshore structures. The methodology is based on the coupling of a wave-structure interaction solver (Nemoh) and a wave propagation model. In this paper, this methodology is applied to two wave propagation models (OceanWave3D and MILDwave), which are compared to each other in a wide spectrum of tests. Additionally, the Nemoh-OceanWave3D model is validated by comparing it to experimental wave basin data. The methodology proves to be a reliable instrument to model wake effects of WEC arrays; results demonstrate a high degree of agreement between the numerical simulations with relative errors lower than 5 % and to a lesser extent for the experimental data, where errors range from 4 % to 17 % .

With the development of distributed energy resources, the low voltage distribution network (LVDN) is supposed to be the integrator of small distributed energy sources. This makes the users in LVDNs multifarious, which leads to more complex modeling. Additionally, data acquisition could be tricky due to rising privacy concerns. These impose severe demands on control schemes in LVDNs that the classical centralized control might not be able to fulfill. To tackle this, a model-free control approach with distributed decision-making architecture is proposed in this paper. Employing statistical methods and game theory, individual users in LVDNs achieve local optimum autonomously. Comparing to conventional approaches applied in LVDNs, the proposed approach is able to achieve active control with less communication burden and computational resources. The paper proves the convergence to the Nash Equilibrium (NE) and uses player compatible relations to form the specific equilibrium. A variant of the log-linear trial and error learning process is applied in a novel “suggest-convince” mechanism to implement the proposed approach. In the case study, a 103 nodes test network based on a real Belgian semiurban LVDN is illustrated. The proposed approach is validated and analyzed with practical load profiles on the 103 nodes network. In addition to that, centralized control is implemented as a benchmark to show the performance of the proposed approach by comparing it with the classical optimization result. The results demonstrate that the proposed approach is able to achieve player compatible equilibrium in an expected way, resulting in a good approximation to the local optimum.

The uneven load of the electric drive of sucker-rod pumping units causes an increase in the consumption of reactive power, which requires compensation. This article discusses the issue of calculating the processes in sucker-rod pumping units in the case of individual compensation of the reactive power. Mathematical models for calculating the process of changing the capacity of cosine capacitors for reactive power compensation in the starting and steady-state modes of the electric drive of the sucker-rod pumping unit, which are characterized by the periodically varying load and moment of inertia, have been developed. The calculation is based on a mathematical model of the induction motor that takes into account the saturation of the magnetic path and the skin effect in the bars of the deep-bar rotor. The created mathematical model can be used to regulate the capacitance of the capacitors connected to the induction motor in order to reduce the reactive power consumption.

The paper considers the transformation of human resource management processes in the healthcare settings of Ukraine in the context of war and the COVID-19 pandemic. It is noted that the unstable and hostile environment of a healthcare setting during times of crisis leads to the need to change the personnel selection and team formation model to increase the adaptability and resilience of human resources involved in the provision of medical care. The key features of the human resource management process in a turbulent environment are the high migration activity of personnel, which leads to the need to reallocate resources, the need to operate under severe financial constraints, and the need to consider personnel as a non-renewable resource when it is impossible to attract additional resources. To ensure the reliability of the functioning of a medical institution, the transformation of human resource management processes should be based on strategic agility and human resource management, organizational resilience as a resource-based capability, corporate sustainability, and transformation of enterprises’ resources, which can be achieved by applying methodological support for resource management in a multi-project environment. Considering a network of medical institutions as a multi-project environment will allow using the methodology of project-oriented resource management, forming adaptive teams in a multi-project environment, to ensure flexible redistribution of resources both within a single institution and within a network of institutions. It is proposed to use formal transformations to manage a medical institution’s human resources. Applying the proposed approach for managing the human resources of a medical institution is considered. The formation of a project team that satisfies the minimum requirements with the maximum value of the team’s qualification score is considered. It is shown that the use of this methodological support made it possible to choose the composition of the project team with a minimum number and a maximum value of the characteristic.

In the current paper, a novel technique is represented to control the liquid petrochemical and petroleum products passing through a transmitting pipe. A simulation setup, including an X-ray tube, a detector, and a pipe, was conducted by Monte Carlo N Particle-X version (MCNPX) code to examine a two-by-two mixture of four diverse petroleum products (ethylene glycol, crude oil, gasoline, and gasoil) in various volumetric ratios. As the feature extraction system, twelve time characteristics were extracted from the received signal, and the most effective ones were selected using correlation analysis to present reasonable inputs for neural network training. Three Multilayers perceptron (MLP) neural networks were applied to indicate the volume ratio of three kinds of petroleum products, and the volume ratio of the fourth product can be feasibly achieved through the results of the three aforementioned networks. In this study, increasing accuracy was placed on the agenda, and an RMSE < 1.21 indicates this high accuracy. Increasing the accuracy of predicting volume ratio, which is due to the use of appropriate characteristics as the neural network input, is the most important innovation in this study, which is why the proposed system can be used as an efficient method in the oil industry.

The ongoing amplification of climate change necessitates the exploration and implementation of effective strategies to mitigate ecological issues while simultaneously preserving economic and social well-being. Renewable power systems offer a way to reduce adverse anthropogenic effects without hindering economic growth. This study aims to conduct a comprehensive bibliometric analysis of renewable power systems to explore their historical context, identify influential studies, and uncover research gaps, hypothesizing that global contributions and policy support significantly influence the field’s dynamics. Following Preferred Reporting Items For Systematic Reviews And Meta-Analyses guidelines, this study utilized Scopus tools analysis and VOSviewer 1.6.20 software to examine the metadata sourced from scientific databases in Scopus. The outcomes of this investigation facilitate the identification of the most prolific countries and authors, as well as collaborative efforts that enrich the theoretical landscape of renewable power systems. The study also traces the evolution of research on renewable power systems. Furthermore, the results reveal key scientific clusters in the analysis: the first cluster concentrates on renewable energy and sustainable development, the second on the relationship between government policies and renewable power systems, and the third on the role of incentives that catalyse the advancement of renewable power systems. The findings of this meta-analysis not only contribute valuable insights to existing research but also enable the identification of emerging research areas related to renewable power system development.

This article considers the task of developing mathematical models and their computer implementation that would establish the dependence of pH (acidity of the environment) on the volume and structure of raw materials for daily loading, as well as on the operating parameters of temperature and humidity based on the interval analysis of experimental data obtained during BGP research of a given type. In the process of research, based on the developed interval models, it was established that this indicator depends on the volume and structure of raw materials, as well as on the temperature and humidity of the substrate in the bioreactor. To build this mathematical model, it is proposed to use the method of interval data analysis and the method of identification of model parameters based on multidimensional optimization. The results of experimental studies for a specific type of biogas plant are given, and interval models with guaranteed prognostic properties that characterize the pH of the environment depending on the specific type of bio-raw material of solid and liquid fractions, temperature, and humidity are obtained. Based on the use of different types of raw materials, the developed models, based on experimental data, describe different configurations of the structure and volumes of raw materials for daily loading. The obtained mathematical models are an algebraic nonlinear equation that can be applied to control the level of pH of the environment in the bioreactor by determining the optimal volumes of raw materials of each type during the loading period depending on the temperature and humidity of the substrate in the bioreactor.