Condition-based monitoring (CBM) has emerged as a critical instrument for lowering the cost of unplanned operations while also improving the efficacy, execution, and dependability of tools. Thermal abnormalities can be thoroughly examined using thermography for condition monitoring. Thanks to the advent of high-resolution infrared cameras, researchers are paying more attention to thermography as a non-contact approach for monitoring the temperature rise of objects and as a technique in great experiments to analyze processes thermally. It also allows for the early identification of weaknesses and failures in equipment while it is in use, decreasing system downtime, catastrophic failure, and maintenance expenses. In many applications, the usage of IRT as a condition monitoring approach has steadily increased during the previous three decades. Infrared cameras are steadily finding use in research and development, in addition to their routine use in condition monitoring and preventative maintenance. This study focuses on infrared crucial thermographic theoretical stages, experimental methodologies, relative and absolute temperature requirements, and infrared essential thermographic theoretical processes for electrical and electronics energy applications. Furthermore, this article addresses the major concerns and obstacles and makes some specific recommendations for future development. With developments in artificial intelligence, particularly computer fiction, depending on the present deep learning algorithm, IRT can boost CBM analysis.

High-voltage direct current (HVDC) has received considerable attention due to several advantageous features such as minimum transmission losses, enhanced stability, and control operation. An appropriate model of HVDC is necessary to assess the operating conditions as well as to analyze the transient and steady-state stabilities integrated with the AC networks. Nevertheless, the construction of an HVDC model is challenging due to the high computational cost, which needs huge ranges of modeling experience. Therefore, advanced dynamic modeling of HVDC is necessary to improve stability with minimum power loss. This paper presents a comprehensive review of the various dynamic modeling of the HVDC transmission system. In line with this matter, an in-depth investigation of various HVDC mathematical models is carried out including average-value modeling (AVM), voltage source converter (VSC), and line-commutated converter (LCC). Moreover, numerous stability assessment models of HVDC are outlined with regard to stability improvement models, current-source system stability, HVDC link stability, and steady-state rotor angle stability. In addition, the various control schemes of LCC-HVDC systems and modular multilevel converter- multi-terminal direct current (MMC-MTDC) are highlighted. This paper also identifies the key issues, the problems of the existing HVDC models as well as providing some selective suggestions for future improvement. All the highlighted insights in this review will hopefully lead to increased efforts toward the enhancement of the modeling for the HVDC system.

Clean, inexpensive, and renewable energy sources with zero adverse environmental impact are essential for long-term sustainability. Implementing waste-to-energy (WtE) technologies has been suggested to improve solid waste management and promote the development of clean and sustainable urban environments. This involves the retrieval of waste materials and their conversion to electricity. By 2050, the global rate of Municipal Solid Waste (MSW) production is anticipated to rise to 2.01 billion tonnes annually. This study evaluated various WtE technologies that have been developed to date. These technologies can be categorized into three groups: thermochemical methods (incineration, pyrolysis, and gasification), biochemical methods (anaerobic digestion and landfilling), and hybrid waste-to-energy systems. Additionally, the discussion touched upon various environmental aspects, highlighting the advantages of reducing CO _X , NO _X , SO _X , furans, and dioxin emissions. Furthermore, this study thoroughly describes the economic impact of various steps on a WtE plant. It also discusses policy and regulatory frameworks, namely availability, affordability, rights, social aspects, and environmental issues, that aim to incorporate principles of ethics, justice, planning, and decision-making when evaluating different aspects of energy systems. Graphical

Advanced metering infrastructure (AMI) is an integrated system of smart meters, communications networks, and data management systems that enable the secure, effective, and dependable distribution of power while also delivering enhanced capabilities to energy consumers. The system also can measure power usage, connect, and disconnect service, detect tampering, identify and isolate outages, and monitor voltage automatically and remotely, which were previously unavailable or required user intervention. This article focuses on AMI and effectively integrating renewable energy sources (RES). However, the study also recommends smart metering for renewables such as solar photovoltaic (PV), hydropower, anaerobic digestion (ad) metering, and renewable energy storage, in which AIM thoroughly supervises the energy utilized by users' appliances. With the prediction of new ancillary services connected with contestability, related regulation, the sufficiency of consumer protection, and safety issues, the magnitude of renewable energy sources in the AMI is an almost unprecedented problem for consumers. The present energy management problems include reducing the power supply-demand gap and boosting power supply dependability. Implementing AMI with distributed renewable energy resources might be a viable strategy for lowering power consumption, improving power supply management, and maximizing management resource use.

Hybrid power systems have evolved into a vital component of contemporary power networks, finding application in various domains ranging from automotive to small-scale off-grid setups. Their purpose is to optimize the utilization of diverse energy sources. This study delves into the efficacy of integrating ultra-capacitors and batteries synergistically. Employing a multi-input converter to drive a variable DC load, the aim is to minimize losses and expenses. In the proposed configuration, a single inductor is utilized, facilitating the integration of a variable array of distributed energy sources. Notably, this converter expedites ultra-capacitor (UC) charging by offering a low inductance pathway, distinguishing it from conventional multi-input DC-DC converters. This proposed topology is bidirectional and adaptable to accommodate varying numbers of energy sources. The obtained numerical results reveal the converter’s effectiveness in stabilizing output voltage and current, making it suitable for multiple applications like electric vehicles, fuel cell systems, and renewable energy integration. Additionally, in the proposed topology, the results showed that it could charge a 2000F UC from a 300V source from 40% to 100% in just 400s and from a 150V battery from 20% to over 90% in just 200s due to the single inductor present in the charging path. Moreover, the load voltages are below 2% in all operational modes when either one or two sources are driving the load. Future research may focus on refining control algorithms to further enhance system efficiency and expand its applicability across different sectors.