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This article presents an analysis and control approach for a non-smooth air-gap permanent magnet synchronous motor (NSAG-PMSM) in the absence of external disturbances. The analytical study of NSAG-PMSM shows the existence of equilibrium points. Based on the Routh–Hurwitz criterion, the stability of the equilibrium points reveals the existence of transcritical bifurcation. NSAG-PMSM exhibits various dynamical behaviours, such as bistable chaos, periodic spiking oscillations, chaotic spiking characteristics, coexistence between periodic and chaotic behaviours and periodic evolution towards monostable chaos as system parameters change. The research uses microcontroller implementation to validate the dynamical characteristics observed during the numerical simulations of the NSAG-PMSM. The study of NSAG-PMSM proposes a strategy to mitigate chaos and stabilise the system using two simple controllers, with a comparative study presented using peak overshoot and settling time diagrams. By combining these different aspects, this article significantly contributes to the understanding of the operation in NSAG-PMSM, highlighting specific aspects related to the application of microcontroller techniques in the field of electrical engineering and solutions to chaos control. [ABSTRACT FROM AUTHOR]

This study uses patent citations data to measure knowledge spillovers at three levels: inventor, firm, and technology. We have collected Indian patents data granted by the United States Patent and Trademark Office (USPTO) and their backward citations from 1990 to 2019. To conduct our empirical analysis, we apply the Negative Binomial (NB) and Generalised Negative Binomial Model (GNBM). To account for other factors influencing knowledge spillovers, we control for patent inventor size, applicant size, patent scope, and claims. Our findings show that US-origin inventors outperform Indian and European inventors in terms of knowledge spillover from patent citations. Similarly, US firms show better knowledge spillover performance than Indian firms, foreign firms, Indian research centres, and other ownership categories. Lastly, the knowledge spillovers in chemistry technology patents are high compared to mechanical engineering, electrical engineering, and other technology categories from existing patent stock. The emperical finding of our study suggests that the government can promote knowledge spillovers from patent citations and advance India towards a knowledge-based economy by attracting foreign inventors and firms in the country, investing in domestic R&D, and developing human capital. [ABSTRACT FROM AUTHOR]

Recently, the wavelet scattering transform (WST) was introduced as a powerful feature extraction tool for classification processes. It provides good performance in applications involving audio signals, images, medical data, and quadcopters for structural health diagnosis. It is also employed in several electrical engineering applications, such as the classification of induction motor bearing failures, electrical loads, and industrial robot faults. Despite its development, the performance of the wavelet scattering (WS) network constructed in the MATLAB environment to compute WST coefficients has not been highlighted in the literature so far. In this paper, the properties of the WST feature matrix are examined, and the parameters that have a significant impact on coefficient magnitudes and matrix dimensions are defined. With minimal configuration, a WS network could extract lowvariance features from real-valued time series for use in machine learning and deep learning applications. The feature matrix, which contains zero, first, and second-level WST coefficients derived from various power system signal configurations, is constructed to be trained using long short-term memory (LSTM) networks. The simulation results demonstrate the efficacy of the proposed classifier with an accuracy approach of 100%. The MATLAB toolbox has been used to create different signals for the WS and LSTM networks. WST has proven to be a powerful tool for power system disturbance classification. [ABSTRACT FROM AUTHOR]

The ongoing global shift towards sustainability in electrical engineering necessitates novel materials that offer both ecological and technical benefits. Biomass‐derived carbon materials (BCMs) are emerging as cornerstones in this transition due to their sustainability, cost‐effectiveness, and versatile properties. This review explores the expansive role of BCMs across various electrical engineering applications, emphasizing their transformative impact and potential in fostering a sustainable technological ecosystem. The fundamentals of BCMs are investigated, including their unique structures, diverse synthesis procedures, and significant electrical and electrochemical properties. A detailed examination of recent innovations in BCM applications for energy storage, such as batteries and supercapacitors, and their pivotal role in developing advanced electronic components like sensors, detectors, and electromagnetic interference shielding composites has been covered. BCMs offer superior electrical conductivities, tunable surface chemistries, and mechanical properties compared to traditional carbon sources. These can be further enhanced through innovative doping and functionalization techniques. Moreover, this review identifies challenges related to scalability and uniformity in properties and proposes future research directions to overcome these hurdles. By integrating insights from recent studies with a forward‐looking perspective, this paper sets the stage for the next generation of electrical engineering solutions powered by biomass‐derived materials, aligning technological advancement with environmental stewardship. [ABSTRACT FROM AUTHOR]

Plumbene, the new 2D structure of lead, single-layered with a hexagonal arrangement of atoms draws the attention of researchers around the globe. We have designed a plumbene sheet using multi-scale modelling i.e. nonequilibrium molecular dynamics (NEMD) simulation methods to determine the electrical properties of plumbene. The effects of varying sample sizes and temperatures on the electrical properties of plumbene using NEMD are studied here. It is observed from 298 K to 318 K electrical conductivity of plumbene ranges from 2.7792 × 10−14 S/m to 1.2012 × 10−13 S/m, with a dwindling nature. Almost static electrical resistivity from 298 K to 378 K is noticed, afterwards a sudden sharp increase till 398 K. The Lorenz number of plumbene from 298 K to 318 K shows reverse results with its electrical conductivity under similar experimental conditions. The electrical conductivity of plumbene sheets at 298 K with 600Å to 1050Å ranges from 0.17794 × 10−14 S/m to 4.7696 × 10−14 S/m. Only at 750Å sample size is a sharp jump is noticed. A reverse trajectory is observed for both electrical resistivity and Lorenz number of plumbene sheet samples. In these two cases, the sharp jump is observed at 1000Å. Our results will be helpful for the effective utilisation of plumbene in targeted applications of electrical engineering fields. [ABSTRACT FROM AUTHOR]

This paper explores the competing sources of productivity gains observed in China's urban agglomerations focusing on three pillar industries: agro-food processing, textiles and electronics. The main results reveal that the higher aggregate productivity observed in denser Chinese cities is driven by both agglomeration and selection forces. Subsequent analysis shows that industrial support policies help to mitigate the selection penalty, but come at the expense of reducing agglomeration benefits. Complementing the city-level analysis, we estimate dynamic and distributive firm-level panel data models to explore the policy rationale of propping up inefficient firms, despite creating a less competitive environment. The micro-based results reveal that inefficient firms targeted by industrial support policies benefit from productivity spillovers in denser cities, mainly in the electronics industry, providing some policy rationale for keeping them in the market. [ABSTRACT FROM AUTHOR]

This paper presents the development of a teaching approach for Reinforcement Learning (RL) for students at the Faculty of Electrical Engineering, University of Ljubljana. The approach is designed to introduce students to the basic concepts, approaches, and algorithms of RL through examples and experiments in both simulation environments and on a real robot. The approach includes practical programs written in Python and presents various RL algorithms. The Q‐learning algorithm is introduced and a deep Q network is implemented to introduce the use of neural networks in deep RL. The software is user‐friendly and allows easy modification of learning parameters, reward functions, and algorithms. The approach was tested successfully on a Franka Emika Panda robot, where the robot manipulator learned to move to a randomly generated target position, shoot a real ball into the goal, and push various objects into target position. The goal of the presented teaching approach is to serve as a study aid for future generations of students of robotics to help them better understand the basic concepts of RL and apply them to a wide variety of problems. [ABSTRACT FROM AUTHOR]

This paper presents a systematic literature survey on diagnostic methods used for railway materials and systems. The authors analyze various railway accident reports, focusing on the types of failures described and their causes. Previous review papers have addressed various aspects of railway systems diagnostics; however, most of the existing research focuses on specific parts of the rail vehicle or subsystems. In contrast, this survey focuses on railway diagnostic systems rather than general diagnostic methods used in mechanical and electrical engineering. The authors classify the types of failures and diagnostic methods that are used in rail transport into two categories: infrastructure and rolling stock. The purpose of this paper is to systematize the types of failure that occur in railway transport systems; identify the state-of-the-art means and methods of diagnostics in railway materials and systems, with particular focus on new research findings; and identify trends and possible research gaps in need of further development. [ABSTRACT FROM AUTHOR]

This paper focuses on the module "Fundamentals of Electrical Engineering," in which students from different engineering disciplines often face difficulties. Despite efforts to enhance the course through digital media, the formation of study groups, and adjusted lecture and exercise materials, there is still a high failure rate in the subject, leading to potential student dropouts. The primary goal is to analyze students' challenges in solving electrical engineering problems to evaluate their grasp of the fundamentals of electrical engineering and identify varying levels of competence. The findings are based on a cross-sectional study conducted at the conclusion of a university course in Germany, involving 196 students. Through item response theory (IRT) analyses, it was determined that the assessment items demonstrated satisfactory fit values. The proficiency model delineates four levels, with only 8.2% of students achieving the highest level. At this stage, students can tackle more intricate problems using circuit analysis techniques. However, a notable portion of students (40.3%) lack a basic understanding of electrical circuits, placing them at the lowest level. [ABSTRACT FROM AUTHOR]

False data injection (FDI) attacks have significantly threatened the security of the cyberphysical system (CPS). To ensure the stability of the CPS under FDI attacks, resilient model predictive control (MPC) has been extensively researched. However, most of the existing resilient MPC methods fail to consider the network resource limitation of the CPS, which may not be applicable in some real systems. Therefore, in this article, a resilient self-triggered (ST) MPC strategy is developed for a discrete-time nonlinear CPS under FDI attacks, which can not only ensure the system’s stability but also reduce resource consumption via decreasing the update frequency of MPC and economizing the utilization of the protection resource. First, an input signal reconstruction mechanism is designed based on key control data selection, which could reconstruct a feasible control sequence for the CPS even if the original ST control sequence is tampered with by FDI attacks. Then, a resilient ST-MPC algorithm based on the input signal reconstruction mechanism is proposed to weaken the adverse effects of FDI attacks and reduce resource consumption simultaneously. Moreover, the recursive feasibility of the resilient ST-MPC mechanism and input-to-state stability of the controlled system are respectively proved. Finally, the performance of the resilient ST-MPC mechanism is shown through a cart–damper–string system and an intelligent vehicle system. [ABSTRACT FROM AUTHOR]

Appreciation/understanding of the factors that affect proximal coupling in magnetic circuits and the manifestations of the effect of coupling due to magnetic proximity are of paramount importance in electrical engineering. In this paper and in another companion paper we endeavor dealing with each of the aspects critically and putting them in one place. Mainly three aspects—factors that affect the coefficient of coupling, expressing the coefficient of coupling in terms of the design parameters—thus showing the symmetry of the inductance matrix, and the factors that contribute to the deviation of the voltage/current ratios from turns‐ratio is considered, with the introduction of a new factor—a measure of (a)symmetry between the magnetic paths seen by the associated coils. It is also attempted to counter the wide‐spread myth that the proximal coupling imperfection is because of the air‐medium and to highlight the fact that it can also arise because of and be controlled by providing controlled magnetic shunting between the associated coils—a very useful insight. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The zero forcing number of a graph G, denoted Z(G), is a graph parameter which is based on a color change rule that describes how to color the vertices. Zero forcing is useful in several branches of science such as electrical engineering, computational complexity and quantum control. In this paper, we investigate the zero forcing number for Cartesian products of some graphs. The main contribution of this paper is to introduce a new presentation of the Cartesian product of two complete bipartite graphs and to obtain the zero forcing number of these graphs. We also introduce a purely graph theoretical method to prove Z(Kn ▢ Km) = mn − m − n + 2. [ABSTRACT FROM AUTHOR]

The transformer is an important component of electrical engineering for many applications including those for integrated circuits. For integrated circuit applications, the use of a synthetic transformer has been recognised to be a viable option for a number of applications. This paper presents two new designs of electronically-controllable CMOS active transformers. The first preposition is based upon a capacitive T-network embedded between two active CMOS gyrators each of which has been obtained by a parallel back-to-back connection of one non-inverting and other inverting electronically-controllable CMOS transconductor. This circuit requires thirty-two MOSFETs, all operating in saturation region. The second design simulates an inductive T-network using two CMOS electronically-controllable floating inductors and one electronically-controllable CMOS grounded inductor thereby employing eighty MOSFETs. The first design offers the advantage of realising a step up as well as step down transformer by appropriate selections of the transconductances of two transconductors. The second design although uses a larger number of MOSFETs than the former but has the advantage of using three grounded capacitors which is a desirable from the view point of IC implementation. It may also be mentioned that using more MOSFETs does not increase layout area significantly since the capacitors require more layout area than all the MOSFETs put together. The workability of both the designs, along with their various performance characteristics and non-ideal analysis, has been well corroborated by computer simulations using SPICE software using 0.18 µm CMOS technology parameters. The application of the proposed circuits in the design of double tunned band pass filter has also been shown and its workability too has been validated by computer simulations using SPICE software. [ABSTRACT FROM AUTHOR]

The general evolution of fieldbus systems has been variously affected by both computer electrical engineering and science. First, the main contribution undoubtedly originated from network IT systems, when the Open Systems Interconnection model was presented. This reference model with seven layers was and remains the foundation for the development of numerous advanced communication protocols. In this paper, the conducted research resulted in a major contribution; specifically, it describes the mathematical model for the Modbus protocol and defines the acquisition cycle model that corresponds to incompletely defined protocols in order to provide a timestamp and achieve temporal consistency for proposed Modbus Extension. The derived technical contribution of the authors is to exemplify the functionality of a typical industrial protocol that can be decomposed to improve the performance of data acquisition systems. Research results in this area have significant implications for innovations in industrial automation networking because of increasing distributed installations and Industrial Internet of Things (IIoT) applications. [ABSTRACT FROM AUTHOR]

This survey introduces 101 new publications on applications of Clifford's geometric algebras (GAs) newly published during 2022 (until mid‐January 2023). The selection of papers is based on a comprehensive search with Dimensions.ai, followed by detailed screening and clustering. Readers will learn about the use of GA for mathematics, computation, surface representations, geometry, image, and signal processing, computing and software, quantum computing, data processing, neural networks, medical science, physics, electric engineering, control and robotics. [ABSTRACT FROM AUTHOR]

Polymer dielectrics have gained significant usage in electrical engineering due to their unique merits, such as good flexibility, easy processing, and low cost. However, it is a huge challenge that polymer dielectrics maintain high breakdown strength meanwhile high permittivity for enhanced energy density. Here, sandwiched polymer films are constructed for enhanced energy density. Increased permittivity has been achieved via blending BaTiO3@polyfluorosiloxane with a polymer as the top and bottom layer. Furthermore, the neat polymer as the middle layer is without critically sacrificing breakdown strength. The energy storage density increases from 7.8 J/cm3 of the neat polymer to 10.41 J/cm3 of the sandwich structure at 460 MV/m. This research offers a feasible route for fabricating capacitive films with enhanced energy density. [ABSTRACT FROM AUTHOR]

Introduction: Accurate prediction of line losses in distribution networks is crucial for optimizing power system planning and network restructuring, as these losses significantly impact grid operation quality. This paper proposes a novel approach that combines advanced feature selection techniques with Stacking ensemble learning to enhance the effectiveness of distribution network loss analysis and assessment. Methods: Utilizing data from 44 substations over an 18-month period, we integrated a Stacking ensemble learning model with multiple feature selection methods, including correlation coefficient, maximum information coefficient, and tree-based techniques. These methods were employed to identify the key predictors of power loss in the distribution network. Results: The proposed model achieved a Mean Absolute Percentage Error (MAPE) of 3.78% and a Root Mean Square Error (RMSE) of 1.53, demonstrating a substantial improvement over traditional linear regression-based prediction methods. The analysis revealed that historical line loss and line active power were the most influential predictive variables, while the inclusion of time-related features further refined the model's performance. Discussion: This study highlights the efficacy of combining multiple feature selection methods with Stacking ensemble learning for predicting power loss in 10 kV distribution networks. The enhanced accuracy and reliability of the proposed model offer valuable insights for electrical engineering applications, potentially contributing to more efficient and sustainable energy distribution systems. Future research could explore the applicability of this approach to other distribution network voltage levels and investigate the incorporation of additional environmental and network-specific factors to further improve power loss prediction. [ABSTRACT FROM AUTHOR]

Undergraduate courses in electrical machines often include an introduction to the air gap magnetic field as a basic element in the energy conversion process. The students must learn the main properties of the field produced by basic winding configurations and how they relate to the winding current and frequency. This paper describes a new test equipment design aimed at helping students achieve these learning goals. The test equipment is designed based on four main elements: a modified slip ring induction machine, a winding current driver board, the DAQ boards, and a PC-based virtual instrument. The virtual instrument provides the winding current drivers with suitable current references depending on the user selected machine operational status (single- or three-phase/winding with DC or AC current) and measures and displays the air gap magnetic field for that operational status. Students' laboratory work is organized into a series of experiments that guide their achievement of these air gap field-related abilities. Student learning, assessed based on pre- and post-lab exams and end-of-semester exams, has increased significantly. The students' opinions of the relevance, usefulness, and motivational effects of the laboratory were also positive. [ABSTRACT FROM AUTHOR]

Economic dispatch (ED) problem is a popular optimization problem in electrical engineering. Many studies that handled ED problem employed metaheuristics as the optimization technique. Unfortunately, these metaheuristics are old or the existing ones. Meanwhile, ED problem is not the favourite constrained case in studies that introduced a novel metaheuristic. This research introduces a novel metaheuristic called quadratic time optimization (QTO). QTO comprises three sequential searches where both diversification and intensification are blended in every search. But the portion of both orientations changes by following the quadratic time. The appraisal of QTO is performed by employing it to solve both unconstrained and constrained problems. The 23 traditional functions represent the unconstrained problem while economic load dispatch (ELD) problem with valve point effect and spinning reserve problem represents the constrained problem. In both appraisals, there are five novel metaheuristics represent the benchmarks, including: walrus optimization algorithm (WaOA), coati optimization algorithm (COA), total interaction algorithm (TIA), addax optimization algorithm (AOA), and language education optimization (LEO). The result shows the competitiveness of the proposed QTO in handling both unconstrained and constrained problems. QTO becomes the first best in 8 functions out of 23 functions including five high dimension unimodal functions, two high dimension multimodal functions, and one fixed dimension multimodal functions. The result also shows that QTO is competitive in three use cases in ELD problems as the ratio between the range and average total cost among metaheuristics for the first, second, and third cases is 0.72, 0.04, and 0.02 percent consecutively. [ABSTRACT FROM AUTHOR]

Sustainable development has become crucial in various fields, including electrical engineering education. This study proposes a learning model incorporating sustainable development principles to better prepare prospective electrical engineering teachers in Indonesia. The model emphasizes interdisciplinary learning outcomes, cognitive mapping of sustainability concepts, and the integration of environmental ethics into the curriculum. The development of the model uses the ADDIE model and involves participants consisting of several potential users and experts. The investigation results show that the developed model is quite effective based on the criteria according to the N-Gain coefficient obtained. [ABSTRACT FROM AUTHOR]

This research and development project aimed to create learning materials in the form of an Internet of Things (IoT) trainer kit specifically designed for the Digital Circuit course. The goal was to enhance student motivation and learning outcomes by facilitating a comprehensive understanding of the subject matter. The research methodology employed was research and development (R&D) utilizing the Instructional Development Institute (IDI) development model, which consists of define, develop, and evaluate stages. Primary data were gathered from experts in instrumentation, media, and materials, as well as feedback from lecturers and students. The findings revealed a high level of validity for the IoT trainer kit learning media. Media experts assigned a validity value of 0.93, while material experts provided a validity value of 0.86. The practicality of the media was confirmed by favorable responses from lecturers, indicating a practicality score of 87.31%. Additionally, students' responses after the trial reflected a high practicality rating of 83.25%. Regarding effectiveness, the IoT trainer kit learning media demonstrated success in improving student learning outcomes, with a gain score of 77.25%, affirming its effectiveness. In conclusion, the IoT trainer kit Learning Media is considered valid, practical, and effective for teaching digital circuits within the Electrical Engineering Study Program at Muhammadiyah University of West Sumatra. Its utilization is expected to boost student engagement, creativity, and learning outcomes, potentially improving the overall quality of education. [ABSTRACT FROM AUTHOR]

The virtual synchronous generator (VSG) has been widely used to improve the system inertia and damping in the renewable energy generation system. However, the in-depth understanding of VSG's stability under disturbances on different control parameters is lacked. In order to solve the problem, the small-signal model of single-VSG is established at first. The influences of key control parameters on the stability of system are analyzed by using the eigenvalue analysis method in detail. On this basis, a novel optimization strategy for control parameters is proposed based on the Particle Swarm Optimization (PSO) algorithm. The control parameters are optimized to realize excellent damping and stability of VSG system. Finally, the simulation and experimental results verify the effectiveness of stability analysis and parameter optimization strategy. [ABSTRACT FROM AUTHOR]

In this paper, the attitude of the students towards learning of fundamentals of electrical engineering and their professor is considered. For this purpose, a survey of first-year students studying this subject was conducted. The survey was conducted on two generations of students from the academic year 2020/2021 during the COVID-19 pandemic and 2022/2023 generation after the pandemic. Our goal is to see students’ opinion about the teaching process and the professor and determine if there is a difference in the students' opinion. It should be noted that during the COVID-19 the teaching took place online, and after the COVID-19 with physical presence. Students during the COVID-19 period gave lower ratings for their professors and found the colloquiums more difficult, expressing less satisfaction with the lectures and exercises. Their colloquium results were also worse. Additionally, final exam results showed that a higher percentage of students from academic years with physical presence passed the exams. [ABSTRACT FROM AUTHOR]

Control charts are customarily developed under the assumption of normal quality characteristics to be monitored. However, in many real dataset applications, the normality assumption is not easy to hold. The in-control (IC) robustness of the charting schemes has significant importance for the valid out-of-control (OOC) performance. This article intends to investigate the IC robustness of the unbiased-function-based adaptive cumulative sum (ACUSUM) chart and nonparametric ACUSUM (NPACUSUM) chart against the non-normal process distributions including symmetrical, skewed, and contaminated normal distributions. The OOC run length profiles of the ACUSUM and the proposed NPACUSUM charts for the individual measurements against the non-normal distributions are also a part of this study. The run length profiles of the proposed charting schemes have been computed using the Monte Carlo simulation method. The artificial datasets have been taken from some symmetric and skewed distributions to implement the proposals. An electrical engineering dataset has also been taken for the implementation of the proposal on a real dataset. [ABSTRACT FROM AUTHOR]

This document discusses the increasing demand for high-efficient isolated DC/DC converters in renewable energy applications. It presents seventeen papers that cover various topics such as reducing switch count and current balancing, addressing voltage ringing issues, solving current unbalance problems, and optimizing converter design. The papers provide experimental results and propose innovative methods to improve the efficiency and performance of DC/DC converters in renewable energy applications. The document also includes three research papers related to power converters, including one on a prototype for NanoGrid applications, one on a new DC-DC bipolar resonance converter, and one on enhancing the stability of a DCMG cluster. The text also includes acknowledgments and biographies of the guest editors. [Extracted from the article]

The degradation of electrical insulating materials has been a subject of interest for decades as they are commonly applied in many fields of electrical engineering. Suitably modeling such a process is important since the known and well-described degradation process reveals the effect of ambient conditions, and this allows us to possibly estimate a material's remaining useful life. However, not many studies are dealing with the effect of the hygrothermal degradation of impregnating mono-component epoxy resins in the context of electrical engineering. Therefore, this study deals with this issue and discusses both the dielectric response (based on the measurement of relative permittivity, dissipation factor, and dielectric strength) and the mechanical response (based on measurements of tensile strength and Shore D hardness) to a hygrothermal degradation experiment. In addition, the results of thermal analyses are presented for the evaluation of the pristine specimen manufacturing process and possible post-curing processes. Furthermore, this study presents several methodologies for modeling the degradation process, including a novel methodology in this area based on Bayesian experimental design. As an outcome, mechanical parameters are proven to be specific in terms of the actual condition of the material and the Bayesian enhanced degradation model seems to be superior to the conventional evaluation methods in this particular study. [ABSTRACT FROM AUTHOR]

The occurrence of partial shading in solar power systems presents a substantial challenge with widespread implications, sparking extensive research, notably in the field of maximum power point tracking (MPPT). This study emphasizes the critical process of accurately tracking the maximum power points with the characteristic curves of photovoltaic (PV) modules under real-time, diverse partial shading patterns. It explores the various stages of the tracking process and the methodologies employed for optimization. While conventional methods show effectiveness, they often fall short in swiftly and accurately tracking maximum power points with minimal errors. To address this limitation, this research introduces a novel machine learning approach known as adaptive reinforcement learning with neural network architecture (ARL-NNA) for MPPT. The results obtained from ARL-NNA are compared with existing algorithms using the same experimental data. Furthermore, the outcomes are validated through different factors and processing time measurements. The findings conclusively demonstrate the efficacy and superiority of the proposed algorithm in effectively tracking maximum power points in PV characteristic curves, providing a promising solution for optimizing solar energy generation in partial shading patterns. This study significantly impacts various realms of electrical engineering including power engineering, power electronics, industrial electronics, solid-state electronics, energy technology, and other related field of engineering and technology. [ABSTRACT FROM AUTHOR]

In the process of tension wire construction of transmission lines, the key state quantities such as tension, inclination and spatial position of the traction walking plate directly affect the construction safety. At present, the construction personnel use visual observation to obtain the state quantity of traction walking plate in the process of tension wire construction. This monitoring method is not only inefficient, and there are personal subjective factors of judgment errors. For this reason, this paper develops a kind of intelligent traction walking plate for tension wire construction, based on the principle of resistance strain type and gyroscope to obtain the tension and inclination of the traction walking plate, respectively, using two front cameras and two rear cameras to obtain the spatial position of the traction walking plate, and finally completing the real‐time transmission of the monitoring data through the Lora technology. The developed intelligent traction walking plate was applied in Shandong Heze Yongfeng 220 kV transmission line project on 11 October 2020, which improved the construction efficiency by 15.71% while ensuring the construction safety. [ABSTRACT FROM AUTHOR]

We developed three coupled van der Pol oscillators with different memristor coupling strengths. We investigate the synchronization state changing the coupling strength of one memristor. Compared with different resistor coupling strengths, amplitude death as the new synchronization state is obtained. In addition, we evaluated the synchronization dependence on the parameter of one memristor to analyze the relative phase difference, amplitude, and power consumption. [ABSTRACT FROM AUTHOR]