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Switchgear is a vital component of modern power systems, responsible for regulating the flow of electrical power. Contact issues, irregular loads, and other similar problems can cause switchgear to overheat, leading to unexpected disturbances and potential damage to the power equipment. Thermal imaging shows significant potential and is increasingly employed to detect faults in power equipment. However, the unique characteristics of thermal images often pose challenges to accurate fault detection. This research aims to study the effectiveness of transfer learning architectures for switchgear fault detection. This paper applies eleven transfer learning architectures, including SqueezeNet, GoogLeNet, InceptionV3, DenseNet201, ResNet50, Xception, InceptionResNetV2, ShuffleNet, EfficientNetB0, AlexNet, and VGG19. The results of the testing phase demonstrated that the application of transfer learning by fine-tuning pre-trained convolutional neural network architectures was highly effective in the classification of thermal images captured from switchgear units. The models achieved accuracy rates between 83.87% and 98.38%, and values of F 1 -Scores between 83.11% and 98.34% in the pre-trained architectures. [Display omitted] • Transfer learning is applied to address the unique characteristics of thermal images for switchgear fault detection. • A thermal image dataset is setup and augmented to improve the classification performance. • Establish standard comparisons among eleven transfer learning architectures. • Achieve rapid and precise classification with the proposed transfer learning architectures. [ABSTRACT FROM AUTHOR]

The electric power industry is a high-risk industry with frequent accidents. To ensure the life safety of employees and reduce the probability of accidents, it is necessary to utilize certain technical means to identify the main causative factors in electric fatal accident reports. This research proposes an accident causation identification method that combines text categorization and catastrophe association analysis. First, an improved text feature extraction method (TF-IDF-GloVe-LDA) is proposed by fusing term frequency-inverse document frequency (TF-IDF), global vectors for word representation (GloVe), and latent dirichlet allocation (LDA) algorithms. Then, an SVM classifier was used to categorize the 473 accident report texts into five categories, ACCIDENT, PROCESS, CAUSE, PROBLEM, and RESPONSE, based on the extracted text feature vectors. Second, for the 461 CAUSEs categorized from the accident report texts, the TF-IDF algorithm is utilized to mine them to obtain 62 accident causative factors. Using the word cloud map and semantic network, a visualization analysis is performed to reveal the intrinsic connection between causative factors. The human factor analysis and classification system (HFACS) is combined to construct a framework and a Boolean dataset for electric power fatal accident causative factors. Finally, the catastrophe progression method is utilized to improve the FP-growth algorithm and propose a catastrophe association analysis method. It is utilized to mine the Boolean dataset to obtain 28 association rules and construct the accident causal network. The main causative factors were ultimately identified by analyzing the critical causal chains within the network and examining the frequency and sensitivity of each node. The findings of this study indicate that a lack of effective management, supervision, and training serves as the fundamental cause of fatal accidents in the electric power industry when compared to equipment and environmental factors. This paper offered a fresh perspective on identifying primary accident causative factors in non-standardized text and exploring their intricate mechanisms of interaction, which holds significant implications for the pre-control management of accidents in the power industry. [ABSTRACT FROM AUTHOR]

This research paper aims to investigate the application of a unified power flow controller (UPFC) for relieving power congestion on a 500/230 kV grid, Power congestion occurs when the demand for electrical power exceeds the transmission capacity of the grid resulting in potential stability issues and voltage fluctuations. The UPFC is a flexible and advanced power electronic device that can regulate power flow and voltage levels in real-time hence offering a potential solution to alleviate congestion. This study explores the principles of UPFC operation its control strategies and the impact of its installation on power system stability and reliability. The research methodology involves computer simulations and analysis of the results obtained. [ABSTRACT FROM AUTHOR]

A micro-grid (MG) is regarded as a localized small-scale power supply system designed for providing electrical power for a remote community, which may be connected to the grid. The paper considers six participants' sites in a remote community. The peak energy consumption patterns for these participants must be different to ensure coalition for mutual benefits and this would facilitate efficient utilization of energy resources. This community concerned may include rural communities having participants such as schools, housing estates, industrial sites, etc. The propose approach would ensure that many participants and their needs are captured and optimized profit of the participants would ensure mutual benefits. In islanded MG, most of the cost/profit distributions in energy trading problems are done based on the self-interest of the participants. Some consider a fair (acceptable or reasonable) cost/profit distribution using the cooperative game theory (CGT) based on the Nash bargaining solution (NBS). However, this method leads to a high degree of dissatisfaction. To improve the fairness and mutual benefits for all MG participants, a new flexible approach is developed. Specifically, CGT which uses a generalized NBS (GNBS) has been proposed, which relaxes certain axiom of the NBS to ensure that fairness of game theory is achieved. The approach allows the profit of the MG participants to be shared using a mechanism based on the negotiation power (i.e. weighted fairness) so that the MG would attain an improved economic outcome. Empirical evaluation of the approach indicates that overall profits using CGT is higher when compared with an independent approach. Moreover, different in negotiation power is achieved in which higher profit is allocated to any participant based on participant's mutual agreement in achieving fair profit distribution. A robust optimization algorithm called Teaching-Learning-Based-Optimization (TLBO) is developed to obtain optimal results. The heuristics algorithms are used in testing the effectiveness of TLBO, which confirm that the TLBO is indeed powerful and robust enough to give quality solutions in solving energy-trading problems. [ABSTRACT FROM AUTHOR]

In recent times, various research studies have proven that the marine currents turbine (MCT) resource has enormous potential for electric power. For the proper exploitation of this resource, it is necessary to understand the hydrodynamics of the marine current turbine. In this paper a modeling was done in order to design a horizontal axis MCT, in an optimal way. In the first step, we evaluated the energy potential of the northern region of Morocco Ksar Srir. In the second step, we described the different building blocks of the conversion chain of a marine current turbine system. Finally, we present the simulation results obtained on Matlab/Simulink. The multi-physical and the hydrodynamic modeling were tested on the P66 marine current turbine of Guinard energy of 3,5 kW. [ABSTRACT FROM AUTHOR]

The two related articles (GeoGebra Tool: Development of Applications for Electrical Machines and Drives Teaching Support and The Efficacy of GeoGebra Tool in Enhancing Electrical Machines and Drives Instruction) are parts of the unique and comprehensive research approach to improving university instruction of electrical machines and drives. The second part of this educational investigation presents the evaluative research on the applicability and quality of the described set of applications. The students of electrical power engineering assessed the quality of the developed EMD GeoGebra applications as very important and useful tools for learning and mastering related courses. Finally, the usefulness and effectiveness of these applications are summarised in the conclusion of the paper. [ABSTRACT FROM AUTHOR]

Electricity consumption has been intensifying due to population growth, climate change, urbanization, and the growing use of electronic devices, which are increasingly non-linear loads that cause poor power quality conditions. The trend of the Internet of Things has led to the creation of devices that encourage the efficient and effective utilization of electrical power. This in turn facilitates the development of modern power distribution structures such as smart grids. Consequently, this paper presents in detail the design, construction, and validation of a three-phase IoT smart meter intended to form part of the end-user demand side of a smart grid. The compact embedded system, with a manufacturing cost below $80 USD, features a unique electronic design that enables its installation in any load center and employs a straightforward IoT structure that includes WiFi technology for Internet communication. Also, a deployed web application was developed specifically to display the smart meter measurements. Unlike other smart meters, the proposed meter not only provides the amount of active energy consumption, but total and fundamental RMS current and voltage, active, reactive, and apparent power, reactive energy, power factor, and some power quality parameters such as, line frequency, amplitude of 64 current harmonics, and total harmonic distortion. Additionally, this study shows that the prototype achieves an absolute error of less than 1% in all its measurements. Finally, real-life applications of the developed device are demonstrated in residential environments. • A smart meter embedded in a light IoT platform that sends data directly to the cloud. • Original electronic design for multiple 3-phase parameters without invasive tools. • Smart meter validated using different loads and tested in real-life environments. [ABSTRACT FROM AUTHOR]

This paper investigates simultaneous lightwave and information transfer (SLIPT) based non-orthogonal multiple access (NOMA) in underwater optical wireless communication (UWOC) systems. At the user signal separation (SS) based SLIPT scheme is employed. Imperfect successive interference cancellation (SIC) at the near user is considered. Firstly, we derive the maximum allowable electrical power for message symbols at the transmitter, ensuring input to the laser diode lies within the linear region and. A novel linearity-constrained transmitter design is proposed for the uniformly distributed message, followed by calculations of instantaneous data rate and instantaneous harvested energy for both users. Then, closed-form expressions for average data rates (AR) and average energy harvested (AE) under weak turbulence-induced fading channels using Holtzman's approximation are derived. The tightness of the approximation is confirmed by Monte-Carlo simulations. AE–AR boundaries for both users show an initial rise and subsequent decline in data rates with increased harvested energy, owing to higher bias values restricting transmitter power. The study also examines trade-offs between AE and AR across various system parameters including beam divergence angles, imperfection of SIC, power allocation coefficients, and water types, is conducted. We observe shifting and shrinking/expansion of the AE–AR region depending upon the variation of the various system parameters. We also show that the discussed SS scheme is general and encompasses time-switching (TS), and power-splitting (PS) schemes as special cases. We also briefly discuss as to how the developed framework could be extended to more than two users. In summary, the overall effective energy consumption deduces owing to the adoption of SLIPT at the users, making the considered setup more energy-efficient. [ABSTRACT FROM AUTHOR]

Estimating the parameters of a Photovoltaic (PV) cell is crucial, given the significant integration of the PV systems into electrical power systems. One of the primary challenges in the estimation of PV cell parameters is identifying a generalized method applicable to any PV system, irrespective of environmental variations and power ratings. This paper introduces a novel application of an optimized deep neural network designed to estimate the parameters of the PV systems across a range of temperatures, irradiance values, and PV module ratings. The network undergoes a training process by utilizing data obtained from the PV module block located within the Simulink library. In order to evaluate the effectiveness of the proposed methodology, the network is subjected to a series of assessments. These assessments encompass the utilization of PV cell data from the Simulink library, comparisons with recently developed methods, and practical evaluations using experimental PV cell data to estimate the PV cell parameters. The findings underscore the simplicity and precision of the proposed method across diverse PV cells. • PV parameter estimation. • Optimization. • Deep learning. [ABSTRACT FROM AUTHOR]