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Microstrip Lines I: Quasi-Static Analyses, Dispersion Models, and Measurements Microstrip Lines II: Fullwave Analyses, Design Considerations, and Compensation, Microstrip Discontinuities: Analysis, Characterization, and Coupled Microstrip Lines, Defected Ground Structure, Coplanar Lines: Coplanar Waveguide and Coplanar Strips, Metamaterials and Planar Transmission Lines, Substrate Integrated Waveguide, Numerical Simulation and Modeling

A two-dimensional displacement and alignment sensor is proposed based on two open-ended transmission lines, each loaded with a split ring resonator (SRR). In this arrangement, the depth of resonance-induced notches in the reflection coefficients can be used to sense a displacement of the loading SRRs in two orthogonal directions. Since the operation principle of the sensor is based on the symmetry properties of SRR-loaded transmission lines, the proposed sensor benefits from immunity to variations in ambient conditions. More importantly, it is shown that in contrast to previously published metamaterial-inspired two-dimensional displacement and alignment sensors, the proposed sensor can be operated at a single fixed frequency. The concept and simulation results are validated through measurement.

With the development of the new generation of information technology, artificial intelligence, cloud computing and big data are gradually becoming powerful engines of the smart grid. In recent years, people have been exploring how to reduce the dependence on human experience in the field of transmission line inspection. Therefore, transmission line inspection has attracted wide attention because of its high intelligence, flexibility and reliability. In this paper, we would like to present a survey on the intelligent transmission line inspection based on unmanned aerial vehicle (UAV). Firstly, the origin and development of intelligent electric power inspection are reviewed, and then the process of intelligent transmission line inspection and three key issues, i.e., path planning of UAV, trajectory tracking, and fault detection and diagnosis are presented in details. Finally, the challenges and future solutions are pointed out for power inspection.

This book presents and discusses alternatives to ordinary transmission lines for the design and implementation of advanced RF/microwave components in planar technology. This book is devoted to the analysis, study and applications of artificial transmission lines mostly implemented by means of a host line conveniently modified (e.g., with modulation of transverse dimensions, with etched patterns in the metallic layers, etc.) or with reactive loading, in order to achieve novel device functionalities, superior performance, and/or reduced size. The author begins with an introductory chapter dedicated to the fundamentals of planar transmission lines. Chapter 2 is focused on artificial transmission lines based on periodic structures (including non-uniform transmission lines and reactively-loaded lines), and provides a comprehensive analysis of the coupled mode theory. Chapters 3 and 4 are dedicated to artificial transmission lines inspired by metamaterials, or based on metamaterial concepts. These chapters include the main practical implementations of such lines and their circuit models, and a wide overview of their RF/microwave applications (including passive and active circuits and antennas). Chapter 5 focuses on reconfigurable devices based on tunable artificial lines, and on non-linear transmission lines. The chapter also introduces several materials and components to achieve tuning, including diode varactors, RF-MEMS, ferroelectrics, and liquid crystals. Finally, Chapter 6 covers other advanced transmission lines and wave guiding structures, such as electroinductive-/magnetoinductive-wave lines, common-mode suppressed balanced lines, lattice-network artificial lines, and substrate integrated waveguides. Artificial Transmission Lines for RF and Microwave Applications provides an in-depth analysis and discussion of artificial transmission lines, including design guidelines that can be useful to researchers, engineers and students.

Collision detection for ensuring safe navigation around power transmission lines is a critical task for maintaining operational safety. With advancements in sensor technology, cameras and LiDAR have been increasingly utilized for monitoring potential safety hazards associated with power lines. However, a single, fixed-position sensor may be insufficient to capture adequate information for accurately estimating the spatial relationship between transmission lines and mechanical equipment. To address this limitation, this paper proposes a method for measuring the spatial distance between transmission lines and mechanical equipment based on the simultaneous estimation of the relative position and orientation (extrinsic calibration) of the camera-LiDAR system. First, an enhanced extrinsic calibration technique for the camera-LiDAR system is introduced to effectively mitigate the impact of data noise on calibration results. Then, based on the calibration results, a spatial distance measurement method is developed to achieve more accurate distance calculations. Experimental evaluations using real-world data across various scenarios demonstrate that the proposed method exhibits strong robustness and accuracy, making it highly valuable for power transmission line safety monitoring and risk assessment.

Inspection and maintenance of power transmission lines are crucial in terms of providing uninterrupted power for the consumers. Inspection and maintenance of transmission lines passing through areas that are difficult to reach and pass through are usually carried out by workers or with the help of helicopter. These methods are not effective in terms of time, energy, safety, economy, and efficiency. Transmission line inspection robots can perform this task safely, efficiently, economically, quickly with the least risk. In this study, theoretical and experimental studies on line suspended power transmission line inspection robots in the literature are examined. Details of robots with different mechanisms are presented. Robots, which perform the line inspection process with the help of cameras and sensors, can also overcome various obstacles on the line. Produced robot prototypes have been tested in the field and laboratory environment. In this paper, these robots are grouped as those that move on live lines and ground wires. In addition, the advantages and disadvantages of robots have been determined depending on the line they move. Looking at the studies in the literature, it can be said that the inspection of power transmission lines with the help of robots is the most appropriate method compared to traditional methods.

The rising development of power systems and smart grids calls for advanced fault diagnosis techniques to prevent undesired interruptions and expenses. One of the most important part of such systems is transmission lines. This paper presents a survey on recent machine learning-based techniques for fault detection, classification, and location estimation in transmission lines. In order to provide reliable and resilient electrical power energy, faster and more accurate fault identification tools are required. Costly consequences of probable faults motivate the need for immediate actions to detect them using intelligent methods, especially emerging machine learning approaches that are powerful in solving diagnosis problems. This paper presents a comprehensive review of various machine learning methodologies including naive Bayesian classifier, decision tree, random forest, k-nearest neighbor, and support vector machine as well as artificial neural networks such as feedforward neural network, convolutional neural network, and adaptive neuro-fuzzy inference system that have been used to detect, classify, and locate faults in transmission lines.

As the scale of the power grid continues to expand, the human-based inspection method struggles to meet the needs of efficient grid operation and maintenance. Currently, the existing UAV inspection system in the market generally has short endurance power time, high flight operation requirements, low degree of autonomous flight, low accuracy of intelligent identification, slow generation of inspection reports, and other problems. In view of these shortcomings, this paper designs an intelligent inspection system based on self-developed UAVs, including autonomous planning of inspection paths, sliding film control algorithms, mobile inspection schemes and intelligent fault diagnosis. In the first stage, basic data such as latitude, longitude, altitude, and the length of the cross-arms are obtained from the cloud database of the power grid, while the lateral displacement and vertical displacement during the inspection drone operation are calculated, and the inspection flight path is generated independently according to the inspection type. In the second stage, in order to make the UAV’s flight more stable, the reference-model-based sliding mode control algorithm is introduced to improve the control performance. Meanwhile, during flight, the intelligent UAV uploads the captured photos to the cloud in real time. In the third stage, a mobile inspection program is designed in order to improve the inspection efficiency. The transfer of equipment is realized in the process of UAV inspection. Finally, to improve the detection accuracy, a high-precision object detector is designed based on the YOLOX network model, and the improved model increased the mAP0.5:0.95 metric by 2.22 percentage points compared to the original YOLOX_m for bird’s nest detection. After a large number of flight verifications, the inspection system designed in this paper greatly improves the efficiency of power inspection, shortens the inspection cycle, reduces the investment cost of inspection manpower and material resources, and successfully fuses the object detection algorithm in the field of high-voltage power transmission lines inspection.

We examine the localized mode and the transmission of plane waves across a capacitive impurity of strength Δ, in a 1D bi-inductive electrical transmission line where the usual discrete Laplacian is replaced by a fractional one characterized by a fractional exponent s . In the absence of the impurity, the plane wave dispersion is computed in closed form in terms of hypergeometric functions. It is observed that the bandwidth decreases steadily, as s decreases towards zero, reaching a minimum width at s  = 0. The localized mode energy and spatial profiles are computed in closed form vía lattice Green functions. The profiles show a remnant of the staggered-unstaggered symmetry that is common in non-fractional chains. The width of the localized mode decreases with decreasing s , becoming completely localized at the impurity site at s  = 0. The transmission coefficient of plane waves across the impurity is qualitatively similar to its non-fractional counterpart ( s  = 1), except at low s values ( s ≪ 1 ). For a fixed exponent s , the transmission decreases with increasing Δ.

The access of new energy power source makes the traditional transmission line structure become complex, and due to the influence of power electronic device control strategy, the fault characteristics have been fundamentally changed, resulting in the traditional transmission line fault localization method can not be applied to the new energy sending line. To address the above problems, this paper analyzes the transient current characteristics of different power supply faults, and learns that there are obvious differences in the transient currents on both sides of the fault point inside and outside the transmission line area, and then proposes a new energy transmission line fault localization method based on the Pearson correlation coefficient, which determines the fault location by calculating the correlation coefficients of the fault waveforms of the neighboring monitoring points. Finally, comparative experiments are carried out under different fault types and fault locations to further verify the applicability of the proposed method.