The occurrence of partial shading in solar power systems presents a substantial challenge with widespread implications, sparking extensive research, notably in the field of Maximum Power Point Tracking (MPPT).This study emphasizes the critical process of accurately tracking the maximum power points with the characteristic curves of Photovoltaic (PV) modules under real-time, diverse partial shading patterns. It explores the various stages of the tracking process and the methodologies employed for optimization. While conventional methods have shown effectiveness, they often fall short in swiftly and accurately tracking maximum power points with minimal errors. To address this limitation, this research introduces a novel machine learning approach known as Adaptive Reinforcement Learning with Neural Network Architecture (ARLNNA) for MPPT. The results obtained from ARL-NNA are compared with existing algorithms using the same experimental data. Furthermore, the outcomes are validated through different factors and processing time measurements. The findings conclusively demonstrate the efficacy and superiority of the proposed algorithm in effectively tracking maximum power points in PV characteristic curves, providing a promising solution for optimizing solar energy generation in partial shading patterns. This study significantly impacts various realms of electrical engineering including power engineering, power electronics, industrial electronics, solid state electronics, energy technology and other related field of Engineering and Technology.

The knowledge of impacts and the load-bearing capacity of unstrengthened/strengthened structures is a crucial source of information about the safety of masonry buildings near deep excavations, especially in dense urban areas. Incorrect calculations made for such designs can seriously affect not only an analyzed object, but also the adjacent buildings. The safety of masonry buildings can be determined by many factors that are closely related to the hazards presented during the performance of deep excavations. These factors are at first identified and then prioritized. The AHP process in the multi-criteria analysis was used to support the decision-making process related to the verification of factors affecting the safety assessment of masonry buildings in the area of deep excavations. The proper design of building structures, including the verification of the structure strengthening near deep excavations, was found to be the most significant factor determining the safety of such buildings. The methodology for proceeding with the verification of ultimate (ULS) and serviceability (SLS) limit states in accordance with the literature data, current regulations, such as Eurocode 6 and other design standards, and know-how of the authors, described in this paper was the next stage of the discussed analysis.

Efficiency, reliability, and durability play a key role in modern drive systems in line with the Industry 4.0 paradigm and the sustainability trend. To ensure this, highly efficient motors and appropriate systems must be deployed to monitor their condition and diagnose faults during the operation. For these reasons, in recent years, more and more research has been focused on developing new methods for fault diagnosis of permanent magnet synchronous motors (PMSMs). This paper proposes a novel hybrid method for the automatic detection and classification of PMSM stator winding faults based on combining the continuous wavelet transform (CWT) analysis of the negative sequence component of the stator phase currents with a convolutional neural network (CNN). CWT scalogram images are used as the inputs of the CNN-based interturn short circuits fault classifier model. Experimental tests were carried out to verify the effectiveness of the proposed approach under various motor operating conditions and at an incipient stage of fault propagation. In addition, the effects of the input image format, CNN structure, and training process parameters on model accuracy and classification effectiveness were investigated. The results of the experimental tests confirmed the high effectiveness of fault detection (99.4%) and classification (97.5%), as well as other important advantages of the developed method.

In the paper results of the operation and efficiency of a DC-DC resonant converter with a switched capacitor topology, equipped with GaN transistors and SiC diodes are presented. Investigated problems are related to the optimization of the DC- DC power electronic converter in order to achieve miniaturization, a simplified design and high efficiency. The proposed system operates at a high frequency with low switching losses. The proposed design helps to achieve uniform heating of the transistors and diodes, as demonstrated by the results of the thermal imaging measurements. The GaN transistors are integrated in one package with dedicated gate drivers and used to simplify the drivers circuitry and increase the power density factor of the proposed device. In the high-frequency design presented in the paper, the converter is implemented without electrolytic capacitors. The results included in the paper contain waveforms recorded in the power circuit at ZVS operation when switching on the transistors. It occurs when the system operates above the frequency of current oscillations in the resonant circuit of the switched capacitor. Efficiency characteristics and a voltage gain curve of the converter versus its output power are presented as well. Results of efficiency and quality of waveforms are important because they allow to characterize the tested system for the implementation using WBG devices. The use of integrated GaN modules to minimize elements in the physical system is also unique to this model and it allows for very short dead-time use, operation in ZVS mode at low reverse-conduction losses.

In this paper, a voltage control system for a PMSM motor based on the QZSDMC converter is proposed, which allows operation in both buck and boost modes as a possible method to make the drive resistant to power grid voltage sags. The authors presented a method for measuring and transforming the output voltage from QZS, enabling the use of a PI controller to control the voltage supplied to the DMC converter. The publication includes simulation and experimental studies comparing the operation of a PMSM motor powered by DMC and the proposed QZSDMC with voltage regulation. Simulation studies confirm the drive with QZSDMC resistance to voltage sags up to 80% of the rated value. Experimental studies demonstrate the correct operation of PMSM even with a power grid voltage amplitude equal to 40% of the rated value.