Transmission System Research Articles

Symmetry Breaking as a Basis for Characterization of Dielectric Materials

This paper introduces a novel method for measuring the dielectric permittivity of materials within the microwave and millimeter wave frequency ranges. The proposed approach, classified as a guided wave transmission system, employs a periodic transmission line structure characterized by mirror/glide symmetry. The dielectric permittivity is deduced by measuring the transmission properties of such structure when presence of the dielectric material breaks the inherent symmetry of the structure and consequently introduce a stopband in propagation characteristic. To explore the influence of symmetry breaking on propagation properties, an analytical dispersion equation, for both symmetries, is formulated using the Rigorous Coupled Wave Analysis (RCWA) combined with the matrix transverse resonance condition. Based on the analytical equation, an optimization procedure and linearized model for a sensing structure is obtained, specifically for X-band characterization of FR4 substrates. The theoretical results of the model are validated with full wave simulations and experimentally.

Torsional vibration characteristics and stability analysis of industrial robot joint transmission system under the electromechanical coupling parameters excitation

Torsional vibration characteristics and stability analysis of industrial robot joint transmission system under the electromechanical coupling parameters excitation

Effect of the transmission system on adhesion control and characteristics of a locomotive under different straight rail surface conditions

As the speed of heavy-haul trains is increased and transportation efficiency is improved, wheel-rail adhesion control has become a critical factor for train safety. Under the scenario of straight track, a dynamic model of the heavy-haul train considering the locomotive transmission system is established. Then, a wheel-rail adhesion control model is developed to research wheel-rail adhesion control and characteristics under complex service conditions, such as friction coefficients and train speeds. The results indicate that the wheelset angular acceleration and the wheelset longitudinal creepage considering the transmission system are larger than without considering the transmission system. Train speeds and friction coefficients are key factors affecting the adhesion control effect and characteristics. Effective control of the wheelset slipping is achieved. In the single torque control process, the torque adjustment time considering the transmission system is longer, the control efficiency is smaller and the control effect is worse. The transmission system will reduce the effect of adhesion control. Thus, it is necessary to consider the effect of the transmission system in adhesion control research.

Synthesis and Characterization of PVC/(Co3O4/CNT)@Au Semiconductor Nanocomposite for Enhanced Medium-Voltage Cable Insulation

Abstract This study presents the synthesis, characterization, and application of a novel PVC/(Co3O4/CNT)@Au nanocomposite for enhanced medium-voltage cable insulation. The nanocomposite was developed by incorporating Co3O4 octahedron nanoparticles, carbon nanotubes (CNTs), and gold nanoparticles (Au) into a polyvinyl chloride matrix. Compared to standard PVC insulation, the nanocomposite exhibited a 3% improvement in relative permittivity (increased from 2.34 to 2.41) and significantly enhanced field uniformity, as evidenced by simulation studies. Fourier-transform infrared spectroscopy, X-ray diffraction, and electron microscopy confirmed the successful integration of nanofillers and highlighted their contributions to the composite’s properties. Optical characterization revealed a direct bandgap of 4.60 eV and an Urbach energy of 0.3674 eV, indicating a wide-bandgap semiconductor with moderate structural disorder. AC conductivity measurements demonstrated frequency-dependent behavior, while dielectric constant and loss analyses suggested the material’s potential for energy storage and insulation applications. The choice of Co3O4 and CNTs was guided by their synergistic impact on charge trapping, field grading, and thermal management, while Au nanoparticles enhanced charge transfer and local electric field distribution. These findings demonstrate the nanocomposite’s promise in addressing the limitations of traditional PVC insulation, offering improved dielectric performance, reliability, and durability for power transmission and distribution systems.

Estimation of spectral spacing and linear ICI effects using IQ constellation images and CNN in gridless WDM systems

The estimation of spectral spacing (guard band) among optical channels in gridless WDM systems would be decisive for making swift decisions in reconfigurable optical add-drop multiplexers (ROADMs) to avoid linear interchannel interference (ICI) effects during the channel aggregation process in transit nodes. In this work, we propose a method based on the construction of heat scatter images from constellation diagrams along with convolutional neuronal networks (CNN) to identify when optical channels are spectrally overlapped as well as the value in GHz of the channel separation in a specific optical channel without adjacent channels information. We validate our method in a gridless 16-QAM Nyquist-WDM system with different channel spacing and optical signal-to-noise ratio (OSNR). Experimental results demonstrated that overlap detection can be achieved with 3×16GBd accuracy. Additionally, the estimation of spectral spacing achieved an error (RMSE) of less than 0.6 GHz. The penalty in accuracy and RMSE is only ∼98% and , respectively, when there is no knowledge of the OSNR value. Thus, this method has the potential to be integrated into monitoring tools designed for future dynamic gridless optical transmission systems.

Optimization of digital back-propagation for coherent optical fiber communication systems using fourth-order Runge-Kutta in the interaction picture method

The digital back-propagation (DBP) is an algorithm that can equalize the chromatic dispersion and nonlinearity in the coherent optical fiber communication system. However, the nonlinear equalization effect of traditional split-step Fourier method (SSFM)-based DBP is limited. This paper replaces the SSFM in DBP algorithm with the fourth-order Runge-Kutta in the interaction picture (RK4IP) method, and employs the Bayesian optimization algorithm (BOA) to optimize the coefficients in RK4IP-based DBP algorithm, then compares it with SSFM-based DBP algorithm, which is also optimized using BOA. The experimental results of 9×100 km standard single-mode fiber (SSMF) 20 Gbaud 16QAM transmission system demonstrate that the coefficient-optimized RK4IP (CO-RK4IP)-based DBP algorithm can achieve the maximum improvement of 0.89 dB in the Q-factor compared to the coefficient-optimized SSFM (CO-SSFM)-based DBP algorithm at equivalent complexity, proving that CO-RK4IP is an effective recursive method for DBP algorithm.

Computationally efficient physics-informed difference-symmetric nonlinear equalizer for C-band DML-DD links

Volterra nonlinear equalizer (VNE) is widely used in intensity modulation and direct detection (IM/DD) systems because it employs multi-order operations to effectively capture the nonlinear characteristics of signals as a generic tool. In the specific directly-modulated laser with direct detection (DML-DD) link, the interaction between the chirp of DML and chromatic dispersion (CD) can be modeled as composite second-order (CSO) distortion. By incorporating the CSO model into the nonlinear equalizer, it is possible to better extract the feature of the end-to-end channel, achieving superior performance with lower complexity. In this work, we propose a computationally efficient physics-informed difference-symmetric nonlinear equalizer (DSNE) based on the analytical formulation of the CSO. Additionally, we provide a thorough comparison of the computational complexity and bit-error-rate (BER) performance of various equalizers. Compared to the conventional VNE, the DSNE provides a 1-dB improvement in receiver sensitivity while reducing computational complexity by 51%. It is shown that the model-assisted DSNE structure enhances the matching to channel nonlinearity by omitting the less cost-effective taps in the conventional VNE and applying difference operations to the symmetric taps. The DSNE incorporates difference-symmetric terms, in contrast to the quadratic nonlinear equalizer (QNE), which uses only diagonal terms. This addition leads to a 56% reduction in BER while incurring only a 12% increase in computational complexity. The proposed DSNE technique demonstrates significant potential for low-cost, high-performance DML-DD optical transmission systems.

Enhanced Fault Prediction for Synchronous Condensers Using LLM-Optimized Wavelet Packet Transformation

This paper presents an enhanced fault prediction framework for synchronous condensers in UHVDC transmission systems, integrating Large Language Models (LLMs) with optimized Wavelet Packet Transform (WPT) for improved diagnostic accuracy. The framework innovatively employs LLMs to automatically optimize WPT parameters, addressing the limitations of traditional manual parameter selection methods. By incorporating a Multi-Head Attention Gated Recurrent Unit (MHA-GRU) network, the system achieves superior temporal feature learning and fault pattern recognition. Through intelligent parameter optimization and advanced feature extraction, the LLM component intelligently selects optimal wavelet decomposition levels and frequency bands, while the MHA-GRU network processes the extracted features for accurate fault classification. Experimental results on a high-capacity synchronous condenser demonstrate the framework’s effectiveness in detecting rotor, air-gap, and stator faults across diverse operational conditions. The system maintains efficient real-time processing capabilities while significantly reducing false alarm rates compared to conventional methods. This comprehensive approach to fault prediction and diagnosis represents a significant advancement in synchronous condenser fault prediction, offering improved accuracy, reduced processing time, and enhanced reliability for UHVDC transmission system maintenance.

Comparison and performance optimization of two absolute circuit designs based on Multisim

Digital circuits are crucial in today's technology, underpinning infrastructure such as computing, communicating, and intelligent devices. Its high efficiency, accuracy and programmable capability drive the development of information processing, data transmission and automation systems. The application of digital circuits has promoted innovation in artificial intelligence, the Internet of Things and other fields, providing strong support for the intelligence and interconnection of modern society. The absolute value circuit is discussed in this paper. The absolute value circuit is implemented for converting the negative value of the input signal to a positive value, ensuring that the output signal is always non-negative. Its significance is to handle application scenarios that require non-negative values, such as signal processing, audio amplification, and sensor output. Through the absolute value circuit, negative value can avoid the negative effect on the subsequent circuit, and improve the stability and accuracy of the system. This paper first introduces all the gates used in the circuit, points out the functions of each gate circuit, and then puts forward two absolute value circuit designs, explaining their working principles, differences and advantages and disadvantages. Then the minimum delay is analyzed by the critical path logic and the sizes of each logic gate is given. Then the supply voltage is optimized after selecting the design with the smallest delay to reduce energy consumption.

DAE-BiLSTM Model for Accurate Diagnosis of Bearing Faults in Escalator Principal Drive Systems

The extensive deployment of escalators has greatly improved travel convenience; however, significant concerns have been raised due to the increasing frequency of safety incidents in recent years. Ensuring the safe operation of escalators and detecting faults in a timely manner have become critical concerns for both manufacturers and maintenance personnel. Traditional periodic inspections are resource-intensive and increasingly deemed inadequate due to the growing diversity and number of escalators. In this article, a data acquisition and transmission system for the main drive shaft bearing of the escalator, based on the Internet of Things (IoT), is designed using the main drive shaft bearing as an example. Additionally, a fault classification model combining a deep autoencoder (DAE) and Bidirectional Long Short-Term Memory Network (BiLSTM) is proposed. The experimental results of this study demonstrate that the DAE-BiLSTM-based fault diagnosis model provides accurate fault detection and early warnings, achieving an accuracy rate exceeding 99%, while significantly reducing the computational costs and training time.

Gear modification optimization design and simulation research of hybrid powertrain transmission system

Gear modification optimization design and simulation research of hybrid powertrain transmission system

Overview of Wide-Frequency Measurement Technology for Electrical Parameters in Distribution Substations

The rapid development of modern transmission and distribution systems requires transformers with wide bandwidth and high accuracy to meet the measurement needs of new characteristics in distribution substations, including wide dynamic ranges in the time-frequency domain and multi-parameter measurements. This paper outlines the measurement principles and structural design of new wide-frequency voltage and current measurement technologies. Based on the current status and measurement demands of distribution substations, an analysis of the economic, safety, and reliability aspects of these technologies is conducted. The results of the analysis indicate that capacitive voltage transformers and current transformers based on Rogowski coils are the current suitable solutions for wide-frequency measurement in distribution substations. Finally, the paper provides a forward-looking perspective on the development prospects of wide-frequency electrical parameter sensing technologies in distribution substations

Nonpolar P-type Conjugated Small Molecules Enable High-Performance Organic Photodetectors for Potential Application in Optical Wireless Communication.

The high responsivity and broad spectral sensitivity of organic photodetectors (OPDs) present a bright future of commercialization. However, the relatively high dark current density still limits its development. Herein, two novel nonpolar p-type conjugated small molecules, NSN and NSSN, are synthesized as interface layers to enhance the performance of the OPDs, which not only can tune energy alignments and increase the reverse charge injection barrier but also can reduce the interfacial trap density. Moreover, benefiting from the smoother surface morphology and enhanced conductivity, the NSN exhibited superior charge transport and collection properties. Consequently, the OPD with NSN achieved a dark current density of 0.37 nA cm-2 and a high specific detectivity of 2.77 × 1013 Jones at -2 V. More importantly, the optimized OPDs can be successfully integrated into optical communication systems, demonstrating precise digital signal communication without obvious distortion, showing promising application potential in the wireless transmission system.

Deep Reinforcemnet Learning for Robust Beamforming in Integrated Sensing, Communication and Power Transmission Systems

Deep Reinforcemnet Learning for Robust Beamforming in Integrated Sensing, Communication and Power Transmission Systems

Designing a Security Metric for EV-Based Load-Altering Attacks in Transmission Systems

Designing a Security Metric for EV-Based Load-Altering Attacks in Transmission Systems

Energy Management System for Distributed Energy Resources using Blockchain Technology

: Power generation in today’s world is of utmost importance, due to which blockchain is used for the categorization and formation of decentralized structures. This paper has proposed decentralized energy generation using a nester, i.e., energy sharing without third-party intervention. Decentralized blockchain technology is applied to ensure power sharing between buyer and seller, and also to achieve efficient power transmission between prosumer and consumer. Energy management is associated with controlling and reducing energy consumption. Blockchain technology plays a major role in distributed power generation, for example, power-sharing (solar and wind energy), price fixation, energy transaction monitoring, and peer-to-peer power-sharing. These are operations performed by blockchain in renewable power generation. Solar power generation using blockchain technology can obtain an impact resting upon the power generation system. Distributed ledger is the key area of blockchain technology for recording and tracking each transaction in the distribution system to improve the efficiency of the overall transmission system. A smart contract is another important tool in the blockchain technology, which is issued to confirm an assent between buyer and seller before starting any energy transaction without external intervention and also to avoid time delay. Maximum power point tracking is conducted in PV cells using blockchain technology. Blockchain influences energy management systems to improve the utilization of energy, optimize energy usage, and also to reduce the cost.

DC fault protection assessment of medium voltage DC (MVDC) systems for offshore wind energy transmission

DC fault protection assessment of medium voltage DC (MVDC) systems for offshore wind energy transmission

Innovative Lightweight Encryption Schemes Leveraging Chaotic Systems for Secure Data Transmission

Innovative Lightweight Encryption Schemes Leveraging Chaotic Systems for Secure Data Transmission

Enhancing Smart Healthcare Networks: Integrating Attribute-Based Encryption for Optimization and Anti-Corruption Mechanisms

This study investigates the feasibility and effectiveness of integrating Attribute-Based Encryption (ABE) into smart healthcare networks, with a particular focus on its role in enhancing anti-corruption mechanisms. The study provides a comprehensive analysis of current vulnerabilities in these networks, identifying potential data security risks. An anti-corruption mechanism is designed to ensure data integrity and reliability. The ABE approach is then empirically compared to other prominent encryption algorithms, such as Identity-Based Encryption, Data Encryption Standard, Advanced Encryption Standard, and Rivest-Shamir-Adleman algorithms. These methods are evaluated based on access latency, data transmission speed, system stability, and anti-corruption capabilities. Experimental results highlight the strengths of the ABE algorithm, demonstrating an average access latency of 31.6 milliseconds, a data transmission speed of 3.56 MB/s, and an average system stability of 98.74%. Furthermore, when integrated into anti-corruption mechanisms, ABE effectively protects against data tampering and misuse, ensuring secure data transmission. Compared to alternative algorithms, ABE offers a more efficient, secure, and stable solution for data management within smart healthcare networks, supported by its robust anti-corruption capabilities. This positions ABE as an optimal choice for safeguarding the integrity and security of healthcare data.

Coordinated Reactive Power Optimization for Transmission and Distribution System with Gross Prediction Errors: A Modified Belief Markov Decision Process-Based Reinforcement Learning Methodology

Coordinated Reactive Power Optimization for Transmission and Distribution System with Gross Prediction Errors: A Modified Belief Markov Decision Process-Based Reinforcement Learning Methodology