Solar energy production based on a photovoltaic system is closely related to solar irradiance. Therefore, the planning of production is based on the prediction of solar irradiance. The optimal use of different energy storage systems requires an accurate prediction of solar irradiation with at least an hourly time horizon. In this work, a solar irradiance prediction method is developed based on the prediction of solar shading by clouds. The method is based on determining the current cloud position and estimating the velocity from a sequence of multiple images taken with a 180-degree wide-angle camera with a resolution of 5 s. The cloud positions for the next hour interval are calculated from the estimated current cloud position and velocity. Based on the cloud position, the percentage of solar overshadowing by clouds is determined, i.e., the solar overshadowing curve for the next hour interval is calculated. The solar irradiance is determined by normalizing the percentage of the solar unshadowing curve to the mean value of the irradiance predicted by the hydrometeorological institute for that hourly interval. Image processing for cloud detection and localization is performed using a computer vision library and the Java programming language. The algorithm developed in this work leads to improved accuracy and resolution of irradiance prediction for the next hour interval. The predicted irradiance curve can be used as a predicted reference for solar energy production in energy storage system optimization.

Hand gesture recognition is a rapidly expanding field with diverse applications, and the use of skeleton-based methods is gaining popularity due to their potential for lightweight execution on embedded devices. However, ensuring robustness and accuracy in both gesture classification and temporal localization is critical for any gesture recognition system to be successful. In this paper, we propose a novel skeleton-based approach to online gesture recognition that draws inspiration from the YOLO object detection model. Specifically, we propose a transformer-based architecture for online gesture recognition that directly predicts both gesture classes and gesture boundaries from a sliding window input. The model is trained in an end-to-end manner using a proposed loss function that focuses on samples containing gesture centers and learns to predict boundaries in the temporal domain analogous to object centers and spatial bounding boxes in YOLO object detection. To evaluate the effectiveness of our method, we conduct experiments on two publicly available continuous hand gesture datasets: SHREC’22 and IPN Hand. Our results outperform the results of skeleton-based approaches from the SHREC’22 online gesture recognition contest. Moreover, our approach obtained competitive results compared to the approaches utilizing alternative input modalities on the IPN Hand dataset.

Regenerative braking systems are an efficient way to increase the energy efficiency of electric rail vehicles. During the development phase, testing of a regenerative braking system in an electric vehicle is costly and potentially dangerous. For this reason, Hardware-In-the-Loop (HIL) simulation is a useful technique to conduct the system’s testing in real time where the physical parts of the system are replaced by simulation models. This paper presents a HIL simulation of a tram regenerative braking system performed on a scaled model. First, offline simulations are performed using a measured speed profile in order to validate the tram, supercapacitor, and power grid model, as well as the energy control algorithm. The results are then verified in the real-time HIL simulation in which the tram and power grid are emulated using a three-phase converter and LiFePO₄ batteries. The energy flow control algorithm controls a three-phase converter which enables the control of energy flow within the regenerative braking system. The results validate the simulated regenerative braking system, making it applicable for implementation in a tram vehicle.

Electric trams are one of the standard forms of public transport. They are characterized by large amounts of electric current and electric current gradient from the power grid, especially during acceleration. For this reason, a regenerative braking system is considered with the aim of reducing electric current peaks and increasing energy efficiency by reducing the total energy consumption of the power grid. A supercapacitor module is used as a storage device for storing and utilizing the braking energy. The supercapacitor module and the power grid constitute a hybrid energy system, for which a control algorithm has been developed. The control algorithm takes into account the influence of the elevation profile and the slope of the vehicle route in storing and using the braking energy. The operation of the algorithm was simulated and analyzed using the MATLAB/Simulink software package for tram lines with different elevation profiles.

This paper deals with control of the fuel cell stack with DC/DC boost converter connected at its output. Feed-forward and feedback controllers of the fuel cell system are designed independently of the boost converter. The voltage control of the boost converter is realized through its input voltage, using fuel cell stack as an actuator, while the PWM duty cycle is left constant. The boost converter output voltage PI controller is designed as an inner control loop, and model reference adaptive controller with signal adaptation algorithm is designed as an outer control loop. The simulation results show that designed adaptive controller is robust to changes of the operating point, mostly determined by the stack current.

The motivation to write this paper is a need to contribute to research of methods of defence against intentional attacks on components of hybrid power systems (microgrids) based on renewable energy sources and storage systems. Spatially distributed electrical energy sources and energy storage devices are gradually being developed and installed, but the security protection of microgrids or individual components of a microgrid is not being improved accordingly. A good example is a fleet of EVs in an urban environment connected to a microgrid with low level of security measures in place (or sometimes none). The term security refers to protection against intentional attacks, not protection against accidents and malfunctions (safety). Lithium batteries, as potentially the most dangerous devices in a microgrid system, i.e. the most likely targets of intentional attacks, can be protected by adequate protection of their power converters and BMSs. This paper presents some of the possible intentional attacks on a battery energy storage system in a microgrid, as well as proposed improvements to the protection of communication channels, power converters’ process data logging, and physical protection of battery energy storage systems.