Online monitoring of Partial Discharges (PDs) in rotating electrical machines is an useful tool for machine prognosis, as it presents reduced costs compared to intrusive inspections and is associated with relevant problems. Although this monitoring method has been developed for almost 50 years, the recent advancements in processes automation and signal processing techniques allow improvements that are still being studied by academic and industrial researchers. To analyze the current context of PDs monitoring, this article presents a literature review based on concepts of PDs in rotating machines, data acquisition techniques, state-of-the art commercial equipment, and recent methodologies for detection and pattern recognition of PDs. The challenges identified in the literature that motivate the development of more reliable and robust PD monitoring systems are presented and discussed.

We derive exact closed-form expressions for Long Range (LoRa) bit error probability and diversity order for channels subject to Nakagami-m, Rayleigh and Rician fading. Analytical expressions are compared with numerical results, showing the accuracy of our proposed exact expressions. In the limiting case of the Nakagami and Rice parameters, our bit error probability expressions specialize into the non-fading case.

This paper proposes and implements a smart architecture for Home Energy Management Systems (HEMS) that enables interoperability among devices from different manufacturers. This is achieved through the use of standardized elements and the design of an innovative middleware. The system comprises a control unit that communicates with smart outlets using the Wireless Smart Ubiquitous Network (WI-SUN) Home Area Network (HAN) specification, while smart metering is achieved using the WI-SUN Field Area Network (FAN) specification. To manage important data, a web platform and mobile app were created. Additionally, machine learning techniques are utilized to identify energy consumption of individual appliances when only the aggregate energy consumption of the house is available. The architecture presented here supports real-time control of energy use and generation through HEMS, and new devices can be added transparently. Finally, a comparison of the proposed system with similar systems in literature highlights its many advantages in terms of functionality.

Instabilities in energy supply caused by equipment failures, particularly in power transformers, can significantly impact efficiency and lead to shutdowns, which can affect the population. To address this, researchers have developed fault diagnosis strategies for oil-immersed power transformers using dissolved gas analysis (DGA) to enhance reliability and environmental responsibility. However, the fault diagnosis of oil-immersed power transformers has not been exhaustively investigated. There are gaps related to real scenarios with imbalanced datasets, such as the reliability and robustness of fault diagnosis modules. Strategies with more robust models increase the overall performance of the entire system. To address this issue, we propose a novel approach based on Kolmogorov–Arnold Network (KAN) for the fault diagnosis of power transformers. Our work is the first to employ a dedicated KAN in an imbalanced data real-world scenario, named KAN_Diag, while also applying the synthetic minority based on probabilistic distribution (SyMProD) technique for balancing the data in the fault diagnosis. Our findings reveal that this pioneering employment of KAN_Diag achieved the minimal value of Hamming loss—0.0323—which minimized the classification error, guaranteeing enhanced reliability for the whole system. This ground-breaking implementation of KAN_Diag achieved the highest value of weighted average F₁-Score—96.8455%—ensuring the solidity of the approach in the real imbalanced data scenario. In addition, KAN_Diag gave the highest value for accuracy—96.7728%—demonstrating the robustness of the entire system. Some key outcomes revealed gains of 68.61 percentage points for KAN_Diag in the fault diagnosis. These advancements emphasize the efficiency and robustness of the proposed system.

The Reconfigurable Intelligent Surface (RIS) has recently gained traction as a promising, enabling technology for future sixth-generation (6G) mobile networks. Thereby, this paper presents a comprehensive statistical, secrecy, and performance analysis of RIS-assisted multiple-antenna wireless communication systems under Weibull fading. The system model encompasses an antenna array at the source transmitting to a typical single-antenna user through both a generally weaker direct link and an enhanced RIS-aided double-Weibull-fading linear cascaded communication channel. Moreover, the RIS is assumed to perform constructive interference towards the user through realistic non-ideal phase cancellation with residual phase errors that follow the von Mises distribution. Then, the statistical distribution of the resultant channel fading is accurately modeled by the analytically derived moment-generated Gamma distribution, in conformity with the central limit theorem (CLT). While considering the existence of a malicious eavesdropper directly linked with the source, relevant performance metrics, expressly the secrecy outage probability (SOP), the symbol error rate (SER), and the spectral efficiency of the system, have their expressions determined for the specific Weibull-fading scenario and verified by Monte Carlo simulations. The study highlights the significant performance improvement of the system arising from a larger number of antenna elements of the RIS, as well as through a more accurate phase cancellation response associated with a higher von Mises concentration parameter, κ. Furthermore, the effects of diverse fading scenarios and varying line-of-sight (LoS) propagation strength are evaluated through the Weibull shape parameter, k .

In this work we present experimental results on the evaluation of two commercial integrated circuits for IoT connectivity, using a systematic approach. One devoted to LoRa and the other to IEEE 802.15.4g, which is the physical layer adopted by the WI-SUN Alliance. The goal behind this evaluation is to present results to support those who will make use of LoRa or IEEE802.15.g/Wi-SUN or other type of connectivity to compare technologies in a fair fashion. The results show that there are differences between datasheet values and the measures collected during the experiments. There are a number of reasons for this divergence, such as the setup, transmitted packet length, equipment calibration and test specifications. This shows the importance of a systematical approach when comparing technologies.