Electrical Research Articles

Recovery of complete left bundle branch block in dilated cardiomyopathy after optimal heart failure treatment: a case report

Complete left bundle branch block (CLBBB) is a significant cardiac conduction abnormality often associated with dilated cardiomyopathy (DCM). This case report highlights the improvement in CLBBB and symptom relief through reverse cardiac remodeling in a patient diagnosed with DCM following an optimized heart failure treatment regimen consisting of an angiotensin-converting enzyme inhibitor, beta-blocker, and mineralocorticoid receptor antagonist. This case highlights the potential of electrical remodeling and conduction system improvement in patients with DCM receiving optimized medical therapy.

Разработка комплексного показателя оценки состояния городского электротранспорта для органов исполнительной власти Российской Федерации

This article analyzes 115 cities on the territory of the Russian Federation in which one or more urban electric transport systems operate. It is determined that in the modern industry there is no single comprehensive indicator of the state of urban electric transport, which would allow an assessment of the state of systems not only in relation to their own indicators of previous years, but also in relation to other, similar systems. A comprehensive indicator is proposed that will allow a comprehensive assessment of the state of urban electric transport and interpret it in numerical terms for its application by state and regional authorities, allowing to quickly draw conclusions and make a decision on further adjustment of the functioning of the electric transport system, characterized by a relatively simple formula for calculation and does not require specific competencies for those conducting the analysis. The key indicators of urban electric transport are systematized, including those related to the operated energy infrastructure, linear infrastructure, park facilities, including technical buildings and structures for system maintenance, indicators related to rolling stock and its key technical characteristics, as well as the availability of urban electric transport for residents of the considered locality and passenger flows within this framework systems. The boundaries of the values of the final indicator have been determined, within which the state of urban electric transport will be assessed by state and regional authorities as satisfactory or unsatisfactory and requiring decisions on revising approaches to the development of the system within the framework of strategic transport planning documents.

Comparison Between Maintenance of Distribution Substations Using the Mobile Substation Unit (UGB) Method and Maintenance of Blackout Distribution Substations regarding Energy Not Served (ENS) which is not distributed to PLN UP3 Serpong

Electricity has become an important part of human life along with technological developments in this modern era. Economic progress, quality of life and the business world are very dependent on the electricity supply supplied by PT. PLN (Persero) UP3 Serpong. Electric power distribution is the final stage in the electric power delivery process. This is the process of distributing electric power from the electricity transmission system to electricity consumers. One of the main components in the electric power distribution system is the distribution substation. A distribution substation is an electrical installation device which contains disconnecting, connecting, safety and distribution transformer equipment to distribute electrical power according to consumer needs. Maintenance at distribution substations is carried out to support services to electricity consumers so that the substation equipment used is in reliable condition. and guaranteed continuity. Regular maintenance on distribution substations is important so that distribution substations do not experience damage and disruption while operating.

Differential Authentication Scheme for Electric Charging System through Light Gradient Boosting Machine

Differential Authentication Scheme for Electric Charging System through Light Gradient Boosting Machine

EVALUASI WEBSITE MONITORING SKUTER LISTRIK SEWA BERBASIS IOT DENGAN PROTOKOL MQTT

This research aims to evaluate the implementation and testing of an Internet of Things (IoT)-based electric scooter rental monitoring system using the MQTT protocol. The monitoring system is designed to track the location and condition of electric scooters through a website integrated with IoT devices. Data such as speed, GPS location, and the status of the help button are transmitted from the scooter to the website via the MQTT protocol. Testing was conducted using the blackbox method to evaluate the system's functionality, and a User Experience (UX) analysis was performed to assess user satisfaction with the website interface. The blackbox testing results showed that the system successfully processed and displayed data as expected, with reliable MQTT communication that effectively transmitted data without loss or delay. Additionally, the UX evaluation involving 30 respondents yielded an average score of 4.57 out of 5. The respondents' assessment indicated that the website interface is easy to use and features a user-friendly visual design.

Desing of an observer with real time monitoring speed and load torque for submersible induction motors

Relevance. When operating submersible equipment for oil production in aggressive environments and transferring wells to the cyclic operation mode, a decrease in the service life of the submersible installation for oil production is observed. In the first case, this is due to the formation of salt deposits and clogging of the working parts of the electric pump with mechanical impurities. In the second case – an increase in the number of starts of the submersible electric motor. To solve the existing problems, it is possible to implement closed control systems for submersible electric motors based on state variable observers, which determines the relevance of the study. Aim. To develop an observer with operational monitoring of the angular velocity of the rotor and the moment of resistance on the shaft of a submersible asynchronous motor at inconsistency of the initial conditions in various operating modes and its testing using modeling tools. Methods. The observer is built on the basis of known engine models in a fixed coordinate system α, β, the theory of IIR-filters to obtain a forecast of estimates of the angular velocity of the rotor and the torque on the shaft and their correction in real time. Results. The authors have proposed the original structure of an observer with operational monitoring of the angular velocity of the rotor and the moment of resistance on the shaft of a submersible asynchronous motor. Conclusions. The paper demonstrates the observer performance with inconsistency of initial conditions and electric motor model data in various operating modes. In all modes, stable estimates of the speed and torque of resistance on the electric motor shaft are obtained. At the same time, the error in estimating the angular velocity under the condition of changing the load on the shaft and starting in the loaded state is no more than 1.2%, which is acceptable in submersible electric motor control systems. It is revealed that the developed observer, under the condition of changing the active resistance of the stator and rotor in the ranges from –25 to +25% of the nominal value, obtains estimates of the angular velocity with an integral error of no more than 5%, except for starting the motor with a decrease in the active resistance of the rotor by 25% of the nominal value –5.53%, which is acceptable in engineering practice.

Systematic Survey Analysis of the Application of Artificial Intelligence Base Network on Grid Computing Techniques

A smart grid is a contemporary electrical system that supports two-way communication and utilizes the concept of demand response. In order to increase the smart grid's dependability and enhance the consistency, efficiency, and efficiency of the electrical supply, stability prediction is required. The true test for smart grid system designers and specialists will therefore be the increase of renewable energy. With the goal of integrating the electric utility infrastructure into the advanced communication era of today, both in terms of function and architecture, this program has achieved great strides toward modernizing and expanding it. In this study, researchers used the Systematic literature review method which identifies, evaluates and interprets all relevant research results related to certain research questions, certain topics, or phenomena of concern. The study review on how a smart grid applied different deep learning techniques and how renewable energy can be integrated into a system where grid control is essential for energy management. The article discusses the idea of a smart grid and how reliable it is when renewable energy sources are present. Globally, a change in electric energy is needed to reduce greenhouse gas emissions, prevent global warming, reduce pollution, and boost energy security.

The Design and Construction of Overcurrent Soft Starting on ESP32 Microcontroller Based Water Machine

The advancement of household water pump technology utilizing induction motors provides numerous benefits, such as efficiency and low cost, but also faces challenges like high inrush current. This study demonstrates the necessity of voltage reduction methods to address high inrush currents that can impact home electrical systems. Testing reveals that the device's error percentage is 0.31%, indicating good performance. The DHT22 and PZEM sensors operate optimally in detecting temperature and current, and controlling relays and water pumps according to the specified thresholds, achieving a 100% system success rate. These results confirm that the system operates as expected according to the specifications.

Optimal Battery Energy Storage System Placement Strategy in Central Java Electrical System for Voltage and Losses Improvement

Optimal Battery Energy Storage System Placement Strategy in Central Java Electrical System for Voltage and Losses Improvement

Fault Diagnosis of Pumped Storage Units—A Novel Data-Model Hybrid-Driven Strategy

Pumped storage units serve as a crucial support for power systems to adapt to large-scale and high-proportion renewable energy sources by providing a stable and flexible energy supply. However, due to the coupling effects of electric power load demands and the complex multi-source factors within the water–mechanical–electrical system, the interrelationship between unit parameters becomes more intricate, posing significant threats to the operational reliability and health status of the units. The complexity of fault diagnosis is further aggravated by the intricate and varied nature of fault characteristics, as well as the challenges in signal extraction under conditions of strong electromagnetic interference and high noise levels. To address these issues, this paper proposes a novel data-model hybrid-driven strategy that analyzes vibration signals to achieve rapid and accurate fault diagnosis of the units. Firstly, the spectral kurtosis theory is employed to enhance the traditional empirical mode decomposition, achieving optimal decomposition and noise reduction effects for vibration signals. Secondly, the intrinsic mode functions (IMFs) obtained from the decomposition are reconstructed, and the entropy values of effective IMFs are calculated as fault feature vectors. Subsequently, the CNN-LSTM model is utilized for fault diagnosis. The effectiveness and feasibility of the proposed method are verified through actual operational data from pumped storage units in a specific region. Through analysis, the fault diagnosis accuracy of the method proposed in this paper can be maintained above 95%, demonstrating robustness in complex engineering environments and effectively ensuring the safe and stable operation of pumped storage units.

A Systematic Survey on the Application of Artificial Intelligence (ai) Baseline Networks on Grid Computing Techniques - Challenges, Novelty and Prospects

A smart grid is a contemporary electrical system that supports two-way communication and utilizes the concept of demand response. To increase the smart grid's dependability and enhance the consistency, efficiency, and efficiency of the electrical supply, stability prediction is required. The true test for smart grid system designers and specialists will therefore be the increase of renewable energy. To integrate the electric utility infrastructure into the advanced communication era of today, both in terms of function and architecture, this program has made great strides toward modernizing and expanding it. The study reviews how a smart grid applied different deep learning techniques and how renewable energy can be integrated into a system where grid control is essential for energy management. The article discusses the idea of a smart grid and how reliable it is when renewable energy sources are present. Globally, a change in electric energy is needed to reduce greenhouse gas emissions, prevent global warming, reduce pollution, and boost energy security.

Influence of the quality of electrical energy on the operation of the main elements of electric arc plasma systems for thermochemical fuel preparation

The work is devoted to the study of the influence of the quality of electrical energy on the service life of electrodes of plasma thermochemical fuel treatment systems used in thermal power plants running on coal fuel. The issues of moving the near-electrode plasma section using magnetic wave scanning are discussed in detail. The ANSYS Maxwell software was used as the main research tool. The computer simulation was performed with the following main experimental parameters: the coefficient of asymmetry in the zero sequence (K0U) is 2% and 4%; the coefficient of asymmetry in the negative sequence (K2U) is 2% and 4%; the voltage deviation dU(+) is 5% and 10%; the materials used for manufacturing electrodes: copper (Cu), a pseudo-alloy of tungsten, nickel and copper – (W + Ni + Cu), a pseudo-alloy of molybdenum, tungsten and copper – (Mo + W+ Cu). Based on the obtained simulation results, graphical representations of changes in the trajectories of the near-electrode plasma section under various distorting factors, dependences of changes in the values of specific erosion of various electrode materials, graphical dependences of changes in the service life of electrodes of plasma systems are constructed. The studies performed using computer modeling based on the ANSYS Maxwell software product made it possible to quantify the effect of voltage distortions on the trajectory of the near-electrode plasma section and, consequently, the service life of the plasmatron electrodes. In particular, in the course of the study, a detailed assessment and analysis of the degree of influence of the following indicators of electrical energy quality was performed: the coefficients of asymmetry in the negative and zero sequence, voltage deviations on the operation and technical condition of the electrodes of plasma systems. The results of the study were discussed, and recommendations were formulated for the use of plasma thermochemical fuel preparation systems used at thermal power plants to ignite the fuel of pulverized coal boilers.

Artificial Intelligence-Based Electric Vehicle Smart Charging System in Malaysia

The worldwide transition to electric vehicles (EVs) is gaining momentum, propelled by the imperative to reduce carbon emissions and foster sustainable transportation. In Malaysia, the government is facilitating this transformation through targeted initiatives aimed at promoting the use of electric vehicles (EVs) and developing the required infrastructure. This paper investigates the crucial role of artificial intelligence (AI) in developing intelligent electric vehicle (EV) charging infrastructure, specifically focusing on the context of Malaysia. The paper examines the current electric vehicle (EV) charging infrastructure in Malaysia, highlights advancements led by artificial intelligence (AI), and references both local and international case studies. Fluctuations in the Total Industry Volume (TIV) and Total Industry Production (TIP) reflect changes in market demand and production capabilities, with notable peaks in March 2023 and March 2024. The research reveals that AI technologies, such as machine learning and predictive analytics, can enhance charging efficiency, improve user experience, and support grid stability. A mathematical model for an AI-based smart charging system was developed, and the implemented system achieved 30% energy savings and a 20.38% reduction in costs compared to traditional methods. These findings underscore the system’s energy and cost efficiency. In addition, we outline the potential advantages and challenges associated with incorporating artificial intelligence (AI) into Malaysia’s electric vehicle (EV) charging infrastructure. Furthermore, we offer recommendations for researchers, industry stakeholders, and regulators. Malaysia can enhance the uptake of electric vehicles and make a positive impact on the environment by leveraging artificial intelligence (AI) to enhance its electric vehicle charging system (EVCS).

Research on Maintenance Capability Standards for Drive Motor Systems of New Energy Vehicles

Developing industry recognized industry standards within the qualification framework is the benchmark for learning outcomes transformation. Based on the study of general competency standards, this article takes the maintenance competency requirements of new energy vehicle drive motor systems as a case study, and conducts a detailed analysis of the requirements for this module in vocational education undergraduate education for secondary and higher vocational schools. Finally, the vocational skill level standard for "electric vehicle high-voltage system evaluation and maintenance" is revised and preliminarily verified.

Characterization method of conductive film state of micro-cathode arc thrusters

Micro-Cathode Arc Thruster (μCAT) is an advanced electric propulsion system developed in the past decades to meet the maneuvering requirements of space microsatellites. However, the constraint on the its application is the short lifespan of the thruster. This is attributed to the conductive film, a key component of thruster discharge. In this study, we propose a measurement method for μCAT conductive film, and enables the evaluation of the evolution process of film thickness. The method is based on the four-probe resistivity testing theory and size effect fitting model, and establishes a method for characterizing the thickness and resistivity of μCAT conductive films. A comparison was made between the corrected resistivity test results of 1, 2, 5, and 10 μm film and the fitting results of the titanium film, revealing an error ofapproximately 10%. Additionally, a total of 2500 ignition experiments were conducted to verify the film thickness and resistance measurement of this method. This research serves as a valuable basis for the lifetime investigation of μCAT. Consequently, the thickness of the film progressively decreases with each discharge cycle, and the resistivity exhibits a corresponding increase due to the size effect. This method could be used as an effective method for exploring the μCAT lifespan and performance.

Applied Metal Hydride System Optimization to Improve Hydrogen Fuel Cell System Performance in Mobile Heavy-Duty Applications

Hydrogen fuel cell vehicles face challenges in competing against battery electric systems as their efficiency is 25 % lower. Especially in the heavy-duty sector the efficiency of fuel cell electric vehicles needs to be increased accordingly, as this market will play a major role in future. One promising approach could be to decrease the energy demand of refrigerated trucks by the pressure energy between the hydrogen tank and fuel cell in metal hydrides to provide cooling power. However, the cooling power output is not constant and further efficiency improvements can be done. Here we show a simulative state space controller optimization which can provide a constant power output and can increase the efficiency by 8 % points compared to an uncontrolled system. A future combination with state-of-the-art efficiency improvement measures can potentially increase the system efficiency even further.

Anti-islanding image detection and optimization of distributed power supply using neural network architecture search

Anti-islanding detection (AILD) for distributed power sources plays an important role on the stable operation of power grid systems and the safety of electrical systems. In order to improve the detection accuracy, we propose neural network architecture search (NAS) based approach for anti-islanding image detection and optimization of distributed power sources. It combines the fast region-based convolutional neural network (Faster-R-CNN) with the differentiable architecture search (Darts), utilizing electrical signal image data acquired from cameras or photovoltaic (PV) inverters, thereby enhancing the accuracy and efficiency of detection. We conduct a comparative analysis of two methods for obtaining distributed power supply (DPS) anti-islanding image data: camera-based acquisition and PV inverter-based acquisition. Our observation reveals that the image data acquired through cameras is more conducive for the learning process of DCNN. The proposed algorithm was compared with other convolutional neural network (CNN) models, validating its performance superiority. This provides a novel perspective and methodology for the advancement of AILD for distributed power sources, and enhances the security and stability of DPS.

Performance Analysis of Electric Vehicle Fast Charging System with Dual Active Bridge Converter using Triple Phase Shift Modulation

Performance Analysis of Electric Vehicle Fast Charging System with Dual Active Bridge Converter using Triple Phase Shift Modulation

Estimation of SOC for Li-ion battery-powered three-wheeled electric vehicle using machine learning methods

Abstract The Battery Management System (BMS) serves as the heart of the electric vehicle system, in which estimating the state of charge (SOC) is the crucial part of the BMS to ensure the durability, reliability, and sustainability of the battery pack. Due to its nonlinear characteristics, accurately estimating the SOC for a slow degradation of the charge is highly cumbersome. The literature provides a series of machine learning algorithms (MLA) to estimate and predict the SOC of lithium-ion (Li-ion) battery systems for electric vehicle (EV) applications. The literature has proposed various MLA, coulomb counting, and different Kalman filter methods to address this challenge and estimate the SOC of Li-ion battery systems for EV applications. This research looks at the differences and similarities between the coulomb counting method, the unscented Kalman filter method, and a number of machine learning algorithms. These include linear regression, polynomial linear regression, support vector regression, decision trees, random forests, artificial neural networks (ANN), and long short-term memory (LSTM). The goal is to assess the MLAs' accuracy in estimating battery SOC. Analyzing model errors optimizes the battery's performance parameter. We identify ANN and LSTM as the two most efficient methods for accurate SOC estimation in an EV-operated BMS system by evaluating the performance indices of the aforementioned machine learning methodologies. Once again, the LSTM model for SOC estimation has proven to be highly accurate in analyzing the discrepancy between the actual and predicted traveling ranges of the designed prototype. We design a MATLAB/SIMULINK EV powertrain by collecting realtime data from the Li-ion battery pack, analyzing the SOC variation data, and using the previously mentioned MLA in the Python platform to estimate the SOC and its accuracy. It highlights the effectiveness of advanced MLAs in improving SOC estimation, thereby enhancing the performance and reliability of EV battery systems.

Performance of Protection Devices Integrated into Lithium-Ion Cells during Overcharge Abuse Test

Lithium-ion batteries currently represent the most suitable technology for energy storage in various applications, such as hybrid and electric vehicles (HEVs and BEVs), portable electronics and energy storage systems. Their wide adoption in recent years is due to their characteristics of high energy density, high power density and long life cycle. On the other hand, they still face challenges from a safety point of view for the possible faults that could generate several problems, ranging from simple malfunctioning to a dangerous thermal runaway. Overcharge is one of the most critical types of faults, and, depending on the level of abuse, it may trigger a thermal runaway. To prevent high levels of overcharge abuse, some cells include integrated protection devices that cut off the circuit when a critical condition is met. In this paper, the performance of these protection devices is evaluated to assess their effectiveness. The cells were tested at different ambient temperatures and current levels. In the worst-case scenarios, the maximum cell temperature slightly exceeded 70 °C and the State of Charge (SOC) reached a peak of 127% when the Current Interruption Device (CID) was activated. These conditions were not critical, so serious events such as thermal runaway were not triggered. These outcomes confirm the effectiveness of the CID, which always intervenes in maintaining a safe state. However, since it never intervened in the overcharge abuse tests, a specific set up was also used to investigate the operation of the other protection device, the Positive Temperature Coefficient.