Electrical Equipment Research Articles

The Role of Heater Size and Location in Modulating Natural Convection Behavior in Cu-Water Nanofluid-Loaded Square Enclosures

Enhancing the energy efficiency of thermal systems reduces their consumption, lowers costs, and reduces undesired environmental impact, thus making these systems more sustainable. The current work introduces a passive method for heat transfer enhancement that is carried out using natural convection by nanofluid. This work introduces a computational study of the process of natural convection within a square cavity containing Cu/H2O nanofluid. The cavity wall on the left side undergoes partial isothermal heating, while the opposing side is fully cooled isothermally, with all other boundaries maintained adiabatic. A mathematical model formulated based on a 2-D model was used to provide the solution for the system of governing equations of mass, momentum, and energy conservation, employing the finite element technique. A commercial CFD package is utilized to perform the computational simulation. The present investigation delves into the impact of the Rayleigh number, nanoparticle concentration, heater length, and heater location on the flow field and heat transfer characteristics. The model outcomes were displayed for a wide range of the pertinent parameters as 103 ≤ Ra ≤ 106, 0.25 ≤ lh ≤ 1.0, 0.125 ≤ hc ≤ 0.875, and 0.02 ≤ ϕ ≤ 0.10. Also, correlation equations relating the average Nusselt number to these crucial parameters are derived. These equations are simple and can be applied in practice easily in many fields, such as electric and electronic equipment cooling and thermal management of heat sources. Also, these equations gather all the parameters that affect the heat transfer process. They are shedding light on the intricate interplay between these parameters in the natural convection heat transfer process.

Analysis of Shielding Performance in Double-Layered Enclosures with Integrated Absorbers

Generally, various technologies, including waveguide below cutoff (WBC), gasket sealing, and bonding, are employed in metallic enclosures to achieve the high electromagnetic shielding performance required for EMP protection and EMC countermeasures in shielding structures or facilities. While the shielding structure or facility is properly constructed and maintained according to design specifications, its electromagnetic shielding performance can remain at the required level, effectively protecting internal electrical and electronic equipment from external electromagnetic interference. However, unintended apertures often occur during the construction or maintenance of shielding facilities, compromising their shielding performance. Therefore, it is crucial to develop technologies that prevent shielding effectiveness degradation caused by both intentional and unintentional apertures. This paper proposes a structure incorporating a composite absorber (made of dielectric and magnetic absorber) within a double metal panel of enclosure featuring an aperture, aimed at maintaining and improving the facility’s shielding performance. The effectiveness of the proposed structure was validated through numerical simulation.

Impact of electronic networks on E‐waste trade: Evidence from ASEAN+6 countries

AbstractThe development of electronic networks promotes economic growth and international trade flows. Because of the short life cycle of electrical and electronic equipment, considerable electronic waste (e‐waste) is generated. We identified the determinants of e‐waste trade, focusing on ASEAN+6 countries and their trading partners for the years 2000 to 2018. The empirical results regarding trade value and trade weight revealed that electronic networks significantly promoted the import of e‐waste into ASEAN+6 countries. The results were robust to using different measures of electronic networks and different estimation methods.

High-Frequency Test of Electric Locomotive Arrester and Its PHM Design

The zinc oxide (ZnO) arrester is positioned on the locomotive roof, preventing other onboard electrical equipment from being harmed by external and internal overvoltages. A number of locomotive arrester burnout and explosion accidents occur during traction supply network harmonic resonance, causing train stoppages and power outages in the network. This study first carries out high-frequency tests to investigate the wide frequency band electrical characteristics of the locomotive arrester and prove that the arrester is prone to fault during resonance. Consequently, a prognostics and health management (PHM) methodology for locomotive arresters is proposed, providing online monitoring, fault warning, and health assessment, which is beneficial to the operation and maintenance of arresters in the future.

Multi-source partial discharge pattern recognition in GIS based on Grabcut-MCNN

Partial discharge (PD) surveillance constitutes a pivotal methodology for diagnosing insulation failures in electrical equipment. Enhancing comprehensively the precision of identifying PD anomalies in Gas Insulated Switchgear (GIS) is of paramount significance for ensuring the steady functioning of power grids. This study introduces a novel framework that integrates Phase-Resolved PD Graph Segmentation (PRPD-Grabcut) with a tailored MobileNets-based Convolutional Neural Network (MCNN) to classify GIS-related PD issues. Leveraging image segmentation via PRPD-Grabcut, crucial features are extracted from PRPD diagrams, which then facilitate the construction of the MCNN model. This model employs depth-wise separable convolutions alongside inverted residual architectures to tackle the vanishing gradient dilemma inherent in Deep Convolutional Neural Networks (DCNNs) during GIS PD pattern discernment. Upon the model's subsequent training and validation, empirical evidence illustrates that the PRPD-Grabcut-MCNN hybrid significantly alleviates the computational load and storage requisites of the model, concurrently enhancing the recognition precision and expediting the training process of the neural network. Relative to diverse established lightweight neural network architectures, MCNN manifests superior performance in terms of recognition accuracy, reduced cross-entropy loss, and expedited training duration.

Digital Governance and Electronic Waste Management at Federal University of Mato Grosso do Sul – Brazil

The Federal University of Mato Grosso do Sul (UFMS) has the Information and Communication Technology Master Plan (PDTIC) to guide and monitor the performance of the Information Technology (IT) area within the institution. The IT area at UFMS has the Digital Governance Committee (CGD), responsible for drafting the Information Security Policy (PSI); the Information and Communication Technology Procurement Plan (PCTIC); the Open Data Plan (PDA); and the University's PDTIC. In addition to the Information and Communication Technology Agency (AGETIC), which is responsible for coordinating, guiding, supervising, executing, and controlling the organization's IT activities. This work aims to demonstrate that sustainability is part of both Digital Governance and the curriculum of UFMS students, focusing on the management of Waste from Electrical and Electronic Equipment (WEEE) within the University. The UFMS Solid Waste and Health Services Management Plan (PGRS/PGRSS) outlines guidelines for the management of these materials, and annually, since 2017, the Faculty of Computing (FACOM) at UFMS organizes the Electronic Waste Collection Week, a project aimed at raising awareness among the academic community about disposal, reuse, and recycling, helping to conserve the environment. This action assists in the education of these students by making them protagonists in this initiative. In each edition hundreds of electronic devices have been reused by students and community, serving as an example of socio-environmental impact action. The methodology employed by UFMS to organize and advance electronic waste management efforts involved the establishment of digital governance regulations, along with sustainability management and governance practices, in order to structure and promote electronic waste management actions at UFMS.

Thermochromic insulation composites for prewarning of thermal faults in electrical equipment

Thermochromic insulation composites for prewarning of thermal faults in electrical equipment

Assessment of Insulation Coordination and Overvoltage for Utility Girds Integrated with Solar Farms

Due to the economic and environmental concerns associated with fossil fuels, many government and private organizations are progressively shifting towards the integration of solar farms with Utility Grids. However, these systems are facing insulation failure issues due to internal and external transient overvoltage’s, in which their shape, magnitude, and duration are unpredictable, and consequently, the insulation stress also becomes unpredictable. To ensure the safety and integrity of the system against any transient overvoltage event, it is important to carry out an insulation coordination analysis. The primary goal of this research work is to achieve this optimization in an economically viable manner, ensuring both operational stability and cost-effectiveness in the design of electrical equipment like surge Arresters. The research work presented in the literature does not fully evaluate all International Electrotechnical Commission (IEC) overvoltage classes as specified in the insulation coordination standards for Utility Grids integrated with solar farms. Therefore, this research paper investigates the impact of various transient and switching overvoltage conditions, as defined in the IEC 60071.4 Insulation Coordination Standard at the Solar and Utility Grid Electrical power system using PSCAD 4.6/EMTP Software. Five distinct simulation scenarios were developed to assess the systems’ resilience against insulation stress events. The proposed system was also examined with and without the application of a lightning surge arrester.

Pulse current injection platform with consecutive early-time and intermediate-time high-altitude electromagnetic pulse conducted currents for immunity test of electrical equipment.

High-altitude electromagnetic pulse (HEMP) has attracted considerable attention for its influence on electrical equipment. HEMP is divided into three parts: early time HEMP (E1), intermediate-time HEMP (E2), and late-time HEMP (E3). Most studies focused on E1 alone, while few investigated others. However, the electrical equipment may be disturbed by more than one part since these three parts co-exist in a HEMP environment. The pulse current injection (PCI) test is an effective immunity test method for evaluating the sensitivity and susceptibility of electrical equipment. To investigate the responses of electrical equipment excited by the consecutive E1 and E2 and compare with those under the individual E1 or E2, this paper builds a PCI platform that can carry out PCI tests with the consecutive E1 and E2 conducted current. To output the E1 current and the E2 current consecutively, which exhibit significant differences in the rise time (20ns and 1.5 μs), peak current (2500 and 250A), and full width at half maximum (FWHM, 500-550ns and 3-5ms), an E1&E2 coupler with a fast-closing high-current trigger switch is designed in this paper. Finally, some 10-kV epoxy insulators are tested in the developed platform, which shows the capacity of the proposed platform to test the immunity of electrical equipment with the consecutive E1 and E2 conducted current.

Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN

Accurate identification of partial discharge (PD) and its types is essential for assessing the operating conditions of electrical equipment. To enhance PD pattern recognition under imbalanced and limited sample conditions, a method based on a Deep Autoencoder-embedded Generative Adversarial Network (DAE-GAN) is proposed. First, the Deep Autoencoder (DAE) is embedded within the Generative Adversarial Network (GAN) to improve the realism of generated samples. Then, complementary PD data samples are introduced during GAN training to address the issue of limited sample size. Lastly, the model’s discriminator is fine-tuned with augmented and balanced training data to enable PD pattern recognition. The DAE-GAN method is used to augment data and recognize patterns in experimental PD signals. The results demonstrate that, under imbalanced and small sample conditions, DAE-GAN generates more authentic PD samples with improved probability distribution fitting compared to other algorithms, leading to varying levels of enhancement in pattern recognition accuracy.

Infrared thermal measurement in power system considering complex circumstances

Abstract Infrared thermal measurement is a vital tool in power systems for monitoring the temperature of electrical equipment without direct contact. This non-invasive technique helps detect hot spots, identify potential issues, and evaluate the performance of power system components. However, the accuracy of the thermal measurement for power system equipment suffers from complex situations and stochastic circumstances. This paper uses a machine-learning-based method to calculate the accurate surface temperature of equipment, which is mainly based on the feedforward network and backpropagation algorithm. At the same time, multiple regression algorithms are implemented and compared. The simulation results and the experiments show that the infrared thermal measurement based on the proposed method reduces the measurement error to 0.1%.

A frequency control strategy with DC microgrid systems for EV stations

ABSTRACT Fluctuations in power supply and demand lead to frequency deviations, which have detrimental effects on the operation of electrical devices and equipment. Therefore, the implementation of effective frequency control mechanisms is essential for enhancing the strength and dependability of microgrid systems. Research aims to develop a frequency control mechanism for an electrical grid system that integrates both wind and photovoltaic (PV) energy sources. The proposed system utilises a Doubly Fed Induction Generator (DFIG) system with frequency controller for regulating frequency and balancing source and demand of power in the grid. To optimise the solar power output, a Luo converter is implemented, which offers high efficiency with improved conversion of power. Additionally, an adaptive neuro fuzzy inference system (ANFIS) maximum power point tracking (MPPT) controller is engaged to ensure optimal regulation of photovoltaic system. DC link obtained from the PV system is then utilised for powering DC loads and charging electric vehicles (EVs) effectively. In order to accomplish this, a bidirectional converter is used, which permits power flow in both ways and facilitates effective EV charging from the microgrid system. The validation of research is conducted on MATLAB software, and the results proves greater converter efficiency of 94.93%, with reduced total harmonic distortion (THD) of 1.24%.

Optimal Configuration of Electricity-Heat Integrated Energy Storage Supplier and Multi-Microgrid System Scheduling Strategy Considering Demand Response

Shared energy storage system provides an attractive solution to the high configuration cost and low utilization rate of multi-microgrid energy storage system. In this paper, an electricity-heat integrated energy storage supplier (EHIESS) containing electricity and heat storage devices is proposed to provide shared energy storage services for multi-microgrid system in order to realize mutual profits for different subjects. To this end, electric boiler (EB) is introduced into EHIESS to realize the electricity-heat coupling of EHIESS and improve the energy utilization rate of electricity and heat storage equipment. Secondly, due to the problem of the uncertainty in user-side operation of multi-microgrid system, a price-based demand response (DR) mechanism is proposed to further optimize the resource allocation of shared electricity and heat energy storage devices. On this basis, a bi-level optimization model considering the capacity configuration of EHIESS and the optimal scheduling of multi-microgrid system is proposed, with the objectives of maximizing the profits of energy storage suppliers in upper-level and minimizing the operation costs of the multi-microgrid system in lower-level, and solved based on the Karush-Kuhn-Tucker (KKT) condition and Big-M method. The simulation results show that in case of demand response, the total operation cost of multi-microgrid system and the total operation profit of EHIESS are 51,687.73 and 11,983.88 CNY, respectively; and the corresponding electricity storage unit capacity is 9730.80 kWh. The proposed model realizes the mutual profits of EHIESS and multi-microgrid system.

Enhancing Predictive Maintenance of Power Transformers Through Machine Learning Approaches

This paper focuses on the use of machine learning algorithms to assist in predictive maintenance, aiming to reduce downtime and associated costs for electrical equipment. It introduces two machine learning predictive indicators: the Chromatographic Assay Indicator (CAI) and the Electrical Failure Risk Indicator (EFRI), which leverage chromatographic and sensor data, respectively. The CAI indicator was trained on external data, and its generalization capability was assessed using company-specific chromatographic data with expert assistance. The evaluation involved two heuristics, both focusing on keyword identification in free-text diagnoses. Results showed over 90% accuracy in predicting failures in reactors and power transformers, and an AUC of nearly 85% for reactors. Additionally, CAI outperformed classical Dissolved Gas Analysis (DGA) methods. On the other hand, the EFRI indicator was trained using company’s internal data from a monitoring system of operational sensors and maintaining power transformer data. The classification model achieved over 95% accuracy and an AUC of almost 90% on the test set. These results indicate that both indicators can be integrated into a solution to support maintenance specialists in their decision-making processes.

Development of mathematical models of power consumption at coal plants

The object of research is coal enterprises where powerful electrical equipment is used (underground installations up to 3000 kW, tunneling complexes of 500–1500 kW, technological complexes of 6–10/0.4 kV, etc.). The deficit of generated capacities, caused by growing energy consumption, can be reduced by regulating power consumption modes. The relevance of this issue is determined by the need to conduct a research of the electricity consumption system, to determine the qualitative and quantitative characteristics of electricity consumption using mathematical models. The lack of mathematical models makes it difficult to analyze energy intensity and consumption modes of each technological operation in the overall balance of electricity consumption of coal enterprises. The article considers the structure and classification of the main technological groups of energy consumers, the development of mathematical models for each type of load modes, as well as a generalized model of electricity consumption of coal enterprises, with the use of mathematical apparatus of probability theory and mathematical statistics. As a result of the work, mathematical models of the electricity consumption process for the main technological groups and for models of daily electricity consumption of coal enterprises as a whole were developed, and it was also established that technological objects of electricity consumption are divided into three different values, in terms of power consumption modes: constant, uniform and pulse. For each class of consumers their statistical characteristics were obtained. The work results can be applied for managing power consumption modes of coal enterprises

Under Voltage Relay System for Battery and DC Load Safety Using Arduino Nano

The quality of the electric power supply in an electrical system is very necessary. One of the important things that need to be considered is the quality of the voltage supply. The disturbance in the electrical system is under voltage. Undervoltage is an electrical system disturbance that will affect the performance of connected electrical equipment and reduce the life of the equipment, including battery components in DC voltage source electrical systems such as solar power plants. To anticipate this occurrence, Under Voltage Relay equipment is needed which can be used to protect batteries and DC equipment from under voltage conditions by using a potentiometer as a voltage divider and IC 7809 to reduce the voltage from the battery input, Relays to disconnect the load, LCD for monitoring and using Arduino Nano microcontroller control. The method used is a development method or Research and Development. This method is used as a basis for designing and manufacturing Low Voltage Relay devices to protect batteries and DC equipment. From the results of the tests carried out, it can disconnect the DC load when detecting low voltage conditions with an accuracy of ± 99.795%. for the normalized execution test for under voltage relay at voltages of 12 V and 24 V, there is a difference value of ± 0.096V and an error of ± 0.554%.

High-performance insulating materials with high breakdown strength and low permittivity for eco-friendly electrical equipment

High-performance insulating materials are essential for developing lightweight, compact, and green offshore wind power equipment. It has been shown that nanoporous structures can limit the development of electron avalanche, leading to a significant increase in the breakdown electric strength of dielectrics. Hence, we fabricated a polysiloxane nanoporous biopolymer insulating material (PNBIM) with the nanoporous structure that presents exceptionally high electrically insulating properties. Under a CO2 atmosphere, the breakdown electric strength of the PNBIM was remarkably improved, exhibiting 761.01 % enhancement relative to that of the pure gas gap. Moreover, the PNBIM demonstrates excellent dielectric performance, with a low dielectric constant (1.63) and dielectric loss (0.014), as well as high hydrophobicity and excellent thermal stability. The investigations revealed that the nanopores inside the material can effectively suppress gas discharge and improve the breakdown electric strength. This study widens our understanding of conventional insulating materials and provides a promising approach for exploring high-performance insulating materials provide valuable insights.

Seismic mitigation of porcelain cylindrical electrical equipment using synergistic concept with base isolation and tuned mass damper

Porcelain cylindrical electrical equipment (PCEEs), such as potential and current transformers, are critical components of electrical substations and essential to power supply networks. Nevertheless, previous strong earthquakes have rendered high vulnerability and poor performance of PCEE during seismic occurrences. To improve seismic performance of PCEE and alleviate adverse seismic impacts, an innovative synergistic mitigation method based on base isolation (BI) and tuned mass damper (TMD) was proposed and subsequently employed to PCEE. Combining BI with TMD allows for a greater utilization of each technology’s capabilities while also circumventing some of the limits of each. While TMD effectiveness is sensitive to input ground motion frequencies, BI could act as a filter to tune input frequency and help TMD fulfill better damping effectiveness. TMD could in return help BI achieve better stability under long-period dominant or pulse-like ground motions. The BI-TMD effectiveness and robustness were investigated using finite element modeling and nonlinear time history analysis. A comprehensive parametric study that considers variable PCEE frequencies, BI periods, TMD mass ratios, and TMD control frequencies was conducted. Results show that the proposed BI-TMD method can significantly enhance the effectiveness and robustness beyond BI single control in mitigating seismic responses of PCEE.

Methodology for assessing the technical condition of electrical networks based on determining the probability of their failure

RELEVANCE. The current technical condition of electrical networks at the moment is generally unsatisfactory with an average level of physical wear of about 60-70 %, therefore,the development of fundamentally new methods for assessing their technical condition is currently an urgent task. The methods of assessing the technical condition of electrical networks analyzed in the article are based to a greater extent on the subjective and insufficiently accurate method of expert assessments, which actually does not take into account the data of its technical diagnostics, as well as statistics of defects and failures. OBJECT. To develop a methodology for assessing the technical condition of electrical networks, which would be based on probabilistic models characterizing the physical processes occurring in electrical networks, as well as data from their technical diagnostics, statistics of defects and failures. METHODS. To assess the technical condition of electrical networks, a probabilistic assessment method was used. In this case, the methodology involves sequential determination of the probabilities of occurrence of selected groups of emergency modes, the probability of failure of a network element as a whole, as well as the probability of failure of an object and a group of objects of the power grid. RESULTS. Based on the research results, probabilistic models of failure of electrical networks were obtained, which, unlike the assessment methods discussed in the article, are based on data from technical diagnostics and inspections of electrical equipment. An example of the implementation of the developed methodology on real electrical network objects is given. CONCLUSION. The developed methodology allows for a more reasonable prioritization of technical impacts on electrical grid equipment, which will make it possible to replace or repair equipment that has actually exhausted its physical resource, which will reduce the frequency of emergency conditions and reduce damage from power supply interruptions.

Composition and Volume of Electrical and Electronic Equipment Waste in Togo: Case of District Autonome Du Grand Lome and the City of Kara

Composition and Volume of Electrical and Electronic Equipment Waste in Togo: Case of District Autonome Du Grand Lome and the City of Kara