Rail is the most efficient and low carbon means of transport, but efforts are still being made to improve the energy efficiency of this sector. In Poland, one of the elements of the implementation of the energy transformation in rail is the “Green Railway” program, which assumes an increase in the share of renewable energy sources (RES) in the power supply structure of the sector to 50% in 2025 and 85% in 2030, and ultimately, to 100%. An increase in energy efficiency leads to a reduction in financial costs, and also contributes to improving the environment and, consequently, to enhancing the economic and social benefit through a cost–benefit analysis. Energy consumption in railway operations is characterized (unlike in construction) by being repetitive. This energy consumption is produced in four areas: in the movement of trains; in auxiliary systems in the trains; in auxiliary systems in the infrastructure (lighting consumption of tunnels or sections of track, point heating systems, the signaling and communication systems, etc.); and in stations, workshops and by other consumers. The aim of this article was to review modern technologies using renewable energy sources in rail transport for traction and non-traction customers.

The objective of this work was to study the possibility of upgrading the control system of the drum shear mechanism by using neural network PI controllers to improve the efficiency of the sheet-metal cutting process. The developed detailed model of the mechanism, including a dual DC electric drive with three subordinate control loops for the voltage of the thyristor converter, current and speed of the motors, a 6-mass kinematic system with viscoelastic connections as well as a model of the metal cutting process, made it possible to uncover that the interaction of electric drives with the mechanical part leads to significant speed fluctuations during the cutting process, which worsens the quality of the sheet-metal edge. A modified system of current and speed controllers with built-in three-layer fitting neural networks as nonlinear components of proportional-integral channels is proposed. An algorithm for the fast learning of neural controllers using the gradient descent method in each cycle of calculating the controller signal is also proposed. The developed neuro-regulators make it possible to reduce the amplitude of speed fluctuations during the cutting process by four times, ensuring the effective damping of oscillations and reducing the duration of transient processes to 0.1 s.

This study presents the development process of a digital twin for a complex dynamic object using Artificial Neural Networks. A separately excited DC motor is considered as an example, which, despite its well-known electromechanical properties, remains a non-trivial object for neural network modeling. It is shown that describing the motor using a generalized neural network with various configurations does not yield satisfactory results. The optimal solution was based on a separation into two distinct nonlinear autoregressive with exogenous inputs (NARX) artificial neural networks with cross-connections for the two main machine variables: one for modeling the armature current with exogenous inputs of voltage and armature speed, and another for modeling the angular speed with inputs of voltage and armature current. Both neural networks are characterized by a relatively small number of neurons in the hidden layer and a time delay of no more than 3 time steps. This solution, consistent with the physical understanding of the motor as an object where electromagnetic energy is converted into thermal and mechanical energy (and vice versa), allows the model to be calibrated for the ideal no-load mode and subsequently account for the influence of torque loads of various natures and changes in the control object parameters over a wide range. The study demonstrates that even for modeling an object such as a DC electric drive with cascaded control, reducing errors at the boundaries of the known operating range requires generating test signals covering approximately 120% of the nominal speed range and 250–400% of the nominal current. Analysis of various test signals revealed that training with a sequence of step changes and linear variations across the entire operating range of armature current and speed provides higher accuracy compared to training with random or uniform signals. Furthermore, to ensure the neural network model’s functionality under varying load torque, a mechanical load observer was developed, and a model architecture incorporating an additional input for disturbance was proposed. The SEDCM_NARX_LOAD neural network model demonstrates a theoretically justified response to load application, although dynamic and static errors arise. In the experiment, the current error was 7.4%, and the speed error was 0.5%. The practical significance of the research lies in the potential use of the proposed model for simulating dynamic and static operational modes of electromechanical systems, tuning controllers, and testing control strategies without employing a physical motor.

This paper presents a novel approach to determining the ampacity of DC and AC railway overhead contact lines, considering both operational and design factors. The ampacity of contact lines is influenced by several design parameters, including materials, cross section and the number of catenary and contact wires, etc. Additionally, the permissible long-term and short-term current values, which correspond to the permissible temperatures defined by EN 50119:2020, are affected by operational factors such as ambient temperature, solar radiation, heat exchange conditions, wind speed, wear of contact wires. Existing methods for determining the ampacity of overhead contact lines often fail to account adequately for these critical factors and lack the necessary procedures to calculate the 30 min ampacity required by EN 50119:2020. In response, the method proposed in this study adapts the procedures of IEEE 738-2012 to the specific needs of railway contact lines, taking into account the effect of contact wire wear on current distribution and overall ampacity. The proposed method offers a more realistic and practical approach to determining the ampacity of contact lines, thereby enhancing the reliability and efficiency of the traction power supply infrastructure. This method could be useful for railway infrastructure managers to ensure the safe and efficient operation under varying environmental and operational conditions.

The integration of solar generation into national energy balances is associated with a wide range of technical, economic, and organizational challenges, the solution of which requires the adoption of innovative strategies for energy system management. The inherent variability of electricity production, driven by fluctuating climatic conditions, complicates system balancing processes and necessitates the reservation of capacities from conventional energy sources to ensure reliability. Under modern market conditions, the pricing of generated electricity is commonly based on day-ahead forecasts of day energy yield, which significantly affects the economic performance of solar power plants. Consequently, achieving high accuracy in day-ahead electricity production forecasting is a critical and highly relevant task. To address this challenge, a physico-statistical model has been developed, in which the analytical approximation of daily electricity generation is represented as a function of a random variable—cloud cover—modeled by a β-distribution. Analytical expressions were derived for calculating the mathematical expectation and variance of daily electricity generation as functions of the β-distribution parameters of cloudiness. The analytical approximation of daily generation deviates from the exact value, obtained through hourly integration, by an average of 3.9%. The relative forecasting error of electricity production, when using the mathematical expectation of cloudiness compared to the analytical approximation of daily generation, reaches 15.2%. The proposed forecasting method, based on a β-parametric cloudiness model, enhances the accuracy of day-ahead production forecasts, improves the economic efficiency of solar power plants, and contributes to strengthening the stability and reliability of power systems with a substantial share of solar generation.

This paper investigates, for the first time, the synthesis of a controller that incorporates a fractional-order integral component to achieve a closed-loop astaticism order greater than one. To enhance both static and dynamic accuracy, the controller integrates direct-signal-propagation neural networks within each control channel. The controlled plant is the BLDCM speed loop, which is modeled using a fractional-order differential equation. The study compares the performance of four controller types: a classical PID regulator tuned close to the optimal modulus criterion (IntPID); a fractional PI–PI^μD controller (FrPID) that achieves an astaticism order of at least 1.8; and two hybrid neuro-controllers, NN–IntPID and NN–FrPID. While the FrPID controller reduces the root-mean-square error by nearly a factor of five compared with IntPID, the best results are delivered by NN–FrPID. Specifically, it decreases overshoot eight-fold during a reference step (from 2.98% to 0.35%), lowers the root-mean-square error during linear reference tracking by a factor of eleven, and reduces the relative speed error by more than thirty-five times. When combined with a fast learning algorithm executed at each control-cycle iteration, the controller enables the closed loop to adapt not only to variations in gain coefficients, but also to changes in the fractional-aperiodic order of the plant. These results demonstrate that neural fractional-integral controllers offer strong potential for improving accuracy and robustness in BLDC motor drives and are applicable to a wide range of electromechanical systems.