This paper contains studies of daily energy production forecasting methods for photovoltaic solar panels (PV panel) by using mathematical methods and fuzzy logic models. Mathematical models are based on analytic equations that bind PV panel power with temperature and solar radiation. In models based on fuzzy logic, we use Adaptive-network-based Fuzzy Inference Systems (ANFIS) and the zero-order Takagi-Sugeno model (TS) with specially selected linear and non-linear membership functions. The use of mentioned membership functions causes that the TS system is equivalent to a polynomial and its properties can be compared to other analytical models of PV panels found in the literature. The developed models are based on data from a real system. The accuracy of developed prognostic models is compared, and a prototype software implementing the best-performing models is presented. The software is written for a generic programmable logic controller (PLC) compliant to the IEC 61131-3 standard.

The approximation of a fractional order PI^λD^μ-controller transfer function using a chain fraction theory is considered. Analytical expressions for the approximation of $s^{\pm \alpha }$ components of the transfer functions of PI^λD^μ-controllers were obtained through the application of the chain fraction theory. Graphs of transition functions and frequency characteristics of D^μ (α = μ = 0.5) and I^λ (α = λ = −0.5) parts for five different decomposition orders were obtained and analyzed. The results showed the possibility of applying the approximation of the PI^λD^μ-controller transfer function by the method of chain fractions with different valuesof λ and μ. For comparison, the transfer functions with the same order polynomials, obtained by the methods of Oustaloup transformation and chain fractions, were approximated for α = ±0.5. The analysis proved the advantages of using the chain fraction method to approximate the transfer function of the PI^λD^μ-controller. The performed approximation opens up the possibility of developing engineering methods for the technical implementation of PI^λD^μ-controllers. The accuracy of the same order transfer function approximation is higher when the method of chain fractions is used. It has been established that the adequacy of the frequency characteristics of the transfer functions obtained by the chain fraction method also depends on the approximation order.

Great significance is given to the use of energy from renewable sources, especially in industrial and municipal applications. The present article is devoted to the optimal control of a DFIG generator with the help of a rotor-side converter (RSC). Its aim is to ensure the delivery of the voltage of a three-phase network with appropriate parameters while operating in an islanded grid. Such a grid is usually characterized by an uneven loading of each phase. Additionally, the load of these phases changes randomly in time. In order to ensure the assumed parameters of line voltages, the optimal control is applied with a square cost function. This ensures the shape of voltage that is in accordance with the referential voltage. Moreover, higher harmonics with a given number are detected and reduced. The simulations that were executed confirm compliance with the conditions of the parameters of the output voltage in the islanded grid. Attention was paid to oscillations in the power flowing through the rotor-side converter (RSC). The methods to accelerate the suppression of these oscillations are presented.

The development of electric vehicles (EV) necessitates the search for new solutions for configuring powertrain systems to increase reliability and efficiency. The modularity of power supplies, converters, and electrical machines is one such solution. Among modular electric machines, dual three-phase (DTP) motors are the most common in high-power drives. To simplify low and medium power drives for EVs based on DTP PM motor, it is proposed to use a BLDC drive and machine of the simplest design – with concentrated windings and surface mounted PMs on the rotor. To study and create such drives, an improved mathematical model of DTP PM machine was developed in this work. It is based on the results of 2D FEM modeling of the magnetic field. According to the developed method, the dependences of the self and mutual inductances between all phase windings from the angle of rotor position and loads of different motor modulus were determined. Based on these inductances, the circuit computer model of DTP PM machine was created in the Matlab/Simulink. It has a high simulation speed and a high level of adequacy, which is confirmed by experimental studies with a mock-up sample of the electric drive system.

The continuous rise in electricity consumption and the integration of renewable energy sources into distribution grids are gradually posing challenges to conventional protection systems. This trend significantly impacts traditional centralized methods of electricity generation, shifting towards local production and consumption and moving closer to emerging microgrids. This aspect was specifically considered during the development of the current power system we utilize. To ensure the successful emergence of microgrids, it's imperative to begin addressing the issue of protecting these renewable energy sources, especially considering that our current devices lack communication or remote-control capabilities. The size of the topology and the number of devices managed at each point in a microgrid will play a crucial role. Therefore, addressing the overall management of individual devices and resources within the microgrid is essential.

Optimization of industrial processes such as manufacturing or processing of specific materials constitutes a point of interest for many researchers, and its application can lead not only to speeding up the processes in question, but also to reducing the energy cost incurred during them. This article presents a novel approach to optimizing the spindle motion of a computer numeric control (CNC) machine. The proposed solution is to use deep learning with reinforcement to map the performance of the reference points realization optimization (RPRO) algorithm used in the industry. A detailed study was conducted to see how well the proposed method performs the targeted task. In addition, the influence of a number of different factors and hyperparameters of the learning process on the performance of the trained agent was investigated. The proposed solution achieved very good results, not only satisfactorily replicating the performance of the benchmark algorithm, but also speeding up the machining process and providing significantly higher accuracy.