Operation Of Lines Research Articles

IG‐YOLOv8: Insulator Guardian Based on YOLO for Insulator Fault Detection

AbstractInsulators have an extremely important role in transmission lines, and they are important components for ensuring the safe operation of transmission lines. In order to solve the difficult problem of insulator fault detection under complex background, IG‐YOLOv8 insulator fault detection algorithm is proposed in this paper. First, the Wise‐IoU (WIoU) loss function is introduced to mitigate the adverse impact of low‐quality images by employing a dynamic non‐monotonic focusing mechanism, thereby enhancing the detection performance of the entire model. Second, a novel C2f network is constructed by integrating the receptive field coordination attention (RFCA) convolutional module to address the parameter‐sharing issue associated with large convolutional kernels. Additionally, the data set has been reorganized using k‐fold cross‐validation to ensure that each subset undergoes training and testing, consequently reducing generalization errors. Finally, a deformable attention (DA) mechanism is employed to augment the feature extraction capability pertaining to insulator fault region information. In order to evaluate the detection performance of the improved IG‐YOLOv8 algorithm, this study constructed an insulator target detection data set containing four fault types: Normal, Defect, Dirty, and Aging. The experimental results show that the average accuracy of the improved model is increased from 89.7% to 96.9%, and the Recall value of the Aging type insulator is increased from 71.8% to 89.1%. The occurrence of missed detection is greatly reduced, and the accuracy of detection is improved. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Accelerating pharmaceutical tablet development by transfer of powder compaction equipment across types and scales

Roller compaction is a key unit operation in a dry granulation line for pharmaceutical tablet manufacturing. During product development, one would like to find the roller compactor (RC) settings that are required to achieve a desired ribbon solid fraction. These settings can be determined from the compression profile of the powder mixture being compacted and a mathematical model that interprets it. However, establishing compression profiles in an RC requires relatively large amounts of powder, which are expensive and may not be available during drug development. As a cost-effective alternative to an RC, a compactor simulator (CS) can be used, which is a small-scale equipment that uses minimal amounts of powder to build the compression profile. However, since the working principles of a CS and an RC are different, the compression profiles obtained from the two devices for a given powder are also different. In this study, we propose a transfer learning approach that allows the RC compression profile of a given powder to be easily predicted from the compression profile obtained in a CS for the same powder. Based on the well-known Johanson model and on the mass correction factor theory, we examine the compaction behavior of six formulations, two of which including active ingredients, and we find that the mass correction factor does not depend significantly on the powder being compacted. We develop a simple, generalized correlation (transfer model) that allows the mass correction factor to be predicted solely as a function of the pressure at which the compaction is carried out. By using the proposed transfer model, the prediction of the RC compression profiles for the validation powders is significantly improved over the case where a constant value of the mass correction factor is used.

Refined transmission line ice-cover prediction model and optimization study

Abstract This study aims to develop a fine-grained transmission line ice-cover prediction model and conduct an optimization study. Ice cover is one of the important factors leading to transmission line accidents, so accurately predicting the ice cover of transmission lines is crucial to ensure the safe operation of power grids. In this paper, an efficient ice-cover prediction model is constructed based on advanced machine learning algorithms and optimization methods and is validated and optimized with a large amount of experimental data. The results show that the proposed model has high accuracy and prediction capability in ice cover prediction, which provides important support for the safe operation of transmission lines.

AdS4 holography and the Hilbert scheme

We elucidate a holographic relationship between the enumerative geometry of the Hilbert scheme of N points in the plane ℂ2, with N large, and the entropy of certain magnetically charged black holes with AdS4 asymptotics. Specifically, we demonstrate how the entropy functional arises from the asymptotics of ’t Hooft and Wilson line operators in a 3d N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{N} $$\\end{document} = 4 gauge theory. The gauge-Bethe correspondence allows us to interpret this calculation in terms of the enumerative geometry of the Hilbert scheme and thereby conjecture that the entropy is saturated by expectation values of certain natural operators in the quantum K-theory ring acting on the localised K-theory of the Hilbert scheme. We give numerical evidence that the large N limit is saturated by contributions from a certain vacuum/fixed point on the Hilbert scheme, associated to a particular triangular-shaped Young diagram, by evolving solutions to the Bethe equations numerically at finite (but large) N towards the classical limit. We thus conjecture a concrete geometric holographic dual of the so-called gravitational/Cardy block.

Symmetry fractionalization and duality defects in Maxwell theory

We consider Maxwell theory on a non-spin manifold. Depending on the choice of statistics for line operators, there are three non-anomalous theories and one anomalous theory with different symmetry fractionalizations. We establish the gauging maps that connect the non-anomalous theories by coupling them to a discrete gauge theory. We also construct topological interfaces associated with SL(2, ℤ) duality and gauging of electric and magnetic one-form symmetries. Finally, by stacking the topological interfaces, we compose various kinds of duality defects, which lead to non-invertible symmetries of non-spin Maxwell theories.

I3oT (Industrializable Industrial Internet of Things) Tool for Continuous Improvement in Production Line Efficiency by Means of Sub-Bottleneck Detection Method

The present paper shows how to develop an I3oT (Industrializable Industrial Internet of Things) tool for continuous improvement in production line efficiency by means of the sub-bottleneck detection method. There is a large amount of scientific literature related to the detection of bottlenecks in production lines. However, there is no scientific literature that develops tools to improve production lines based on the bottlenecks that go beyond rebalancing tasks. This article explores the concept of a sub-bottleneck. In order to detect sub-bottlenecks in a massive way, the use of one of the I3oT (Industrializable Industrial Internet of Things) tools developed in our previous work, the mini-terms, is proposed. These mini-terms use the existing sensors for the normal operation of the production lines to measure the sub-cycle times and use them to predict the deterioration of the machine components found in the production lines. The sub-bottleneck algorithms proposed are used in two real twin lines at the Ford manufacturing plant in Almussafes (Valencia), the (3LH) and (3RH), to show how the lines can be continuously improved by means of sub-bottleneck detection.

A fault location method for DC transmission line based on the distributed parameter model considering line parameter uncertainty

Addressing the impact of whether line parameters can be accurately obtained and the errors due to environmental influences on the accuracy of fault location for DC transmission line, this paper proposes a fault location method based on the distributed parameter model that accounts for uncertainty in line parameter. To address the challenge of accurately obtaining DC transmission line parameters, a high-order derivative-based parameter estimation method is introduced to determine unknown equivalent parameters of the DC transmission line. Additionally, an optimization algorithm is incorporated to dynamically correct parameter errors, thereby enhancing parameter stability and constructing an accurate line model. Based on this, theoretical derivations confirm the applicability of the Tellegen's quasi-power theorem in the normal operation and internal fault equivalent models of DC transmission line. A fault location function is established based on the relationship between the line's Tellegen quasi-power characteristic and fault distance. The proposed method's effectiveness and accuracy are validated using a DC system model built on the PSCAD/EMTDC simulation platform. Simulation results indicate that the proposed method can locate faults under different fault conditions.

Development characteristics and hazard analysis of debris flow along the Emei to Mianning section of the Chengdu–Kunming Railway

Geological disasters in the Emei to Mianning section of the Chengdu–Kunming Railway located in southwestern China occur frequently, and many debris flow gullies are distributed along the line. Debris flows often cause damage to the railway and highway that threatens the safe operation of the original line and the double-track line. Along the railway, the debris flow outbreak risks are analyzed for 53 debris flow gullies using field survey, remote sensing interpretation, and numerical simulation. Their developmental characteristics were judged according to the code. The results are used for numerical simulation of debris flow to further determine the movement characteristics and the accumulation range of debris flow. The results are combined with high-definition satellite images to analyze the form of the railway passing from the mouth of the ditch and the simulation results to determine the impact on the safe operation of the Chengdu–Kunming Railway when the debris flow erupts. Finally, disaster prevention and mitigation projects are proposed, and the prevention and control effects of the proposed works are validated.

Deep Learning Methods to Mitigate Human-Factor-Related Accidents in Maritime Transport

Artificial intelligence aims to be the solution to multiple engineering problems by trying to emulate the human learning process. In this sense, maritime transport standards have clearly evolved, which are based on two principal pillars: the International Convention for the Safety of Life at Sea Convention (SOLAS) and the International Convention for the Prevention of Pollution from Ships (MARPOL). Based on a formal safety assessment research process, these pillars try to solve most of the maritime transport accidents, which, in their final steps, are associated with human factors. In this research, an original methodology employing a deep learning process for image recognition during mooring line operation, a dangerous process on ships, is developed. The main results indicate that the proposed method is an excellent tool for advising ship officers on watch and, consequently, provides a new way to prevent human factors onboard from causing accidents, which in the future must be considered in international standards.

The always changing data problem of using AI in manufacturing : Using synthetic data from the digital twin to feed AI models

Production is becoming increasingly flexible, which also requires the flexibility of the support system for the production. And the key here is the speed of decisions, in which the support of modern artificial intelligence systems can be crucial. Flexible production is based on a well-planned control of production, which increasingly uses some artificial intelligence component. Artificial intelligence can already be useful in the early stages of planning the production line, and of course it can also control the daily operation of the production line after the installation of the production place or line. The biggest problem is supplying the neural network that controls the artificial intelligence with training data. The production lines typically change every 2-4 months, new products appear, the layout changes, and the main process data also changes due to the development of the processes. This results in the training data becoming outdated or obsolete very quickly and thus cannot be used to train models anymore. High-quality learning data can be produced by digital twin models of production lines. Such synthetic data has several advantages over data collected from production. In this article, we investigate how useful this synthetic data is during the life cycle of the production line.

METHOD OF RECOGNITION OF FPV-UAV RADIO SIGNALS FORMED ACCORDING TO CROSSFIRE AND EXPRESSLRS STANDARDS

Unmanned aerial vehicles (UAVs) with the First Person View (FPV) system are the most common type of attack UAVs used at the tactical level by the armed forces of the russian federation. Timely detection of facts of their using by the enemy and countering them are important tasks solved by units of the Ukrainian Defense Forces. An effective countermeasure way for FPV-UAVs is to create interference in their radio control channels frequency bands. To increase the radio suppression range, it is necessary to use interference matched in frequency and structure, the necessary condition for the formation of which is the presence of a priori information about the structure of the radio signal. Analysis of the available information on the construction of enemy FPV-UAVs shows that their radio control and telemetry channels are usually combined, have time division multiple access, the signals are formed according to CrossFire and ExpressLRS standards and have fixed sets of parameter values that can be used as features in recognition. The proposed FPV-UAV radio signal recognition technique is based on decision tree and minimum metric methods and uses a priori information on frequency, time and modulation parameters of radio signals. The combination of the two decision-making methods made it possible to reduce the computational complexity by eliminating operations for determining the parameters of radio signals that do not correspond to the decisions taken at the previous stages, and provides the possibility of adding new time parameters without changing the developed recognition technique. The method consists of a sequence of operations for estimation radio signal parameters, comparing them with known values, making decisions about the type of standard and command- telemetry radio line operation mode. Only one fragment with a duration of one frequency element is sufficient to recognize the FPV-UAV radio signal, which reduces the technical requirements for detection means in which the method can be used.

Methodology for selecting optimal wire grade and cross-section based on an integrated technical and economic criterion

The aim was to develop a methodology for selecting an optimal wire grade and cross-section for overhead power lines with a voltage of above 1 kV under intensive development of power grid systems. This aim was solved using the authors’ previously developed methods: selection of an optimal wire grade based on hierarchy analysis and selection of an optimal wire cross-section by optimizing the specific discounted costs during the entire period of construction and operation of overhead power lines. In the present study, these methods are integrated into a single methodology. It is proposed to implement wire inspections prior to the stage of technical and economic calculations, since the necessary data has already been taken into account during the selection of a wire grade and its cross-section. The proposed methodology was tested on a typical example of reconstruction of the Zapadnaya– Davydovka 110 kV overhead line, which creates limitations in the power supply of Primorsky Krai due to insufficient wire capacity and its operation beyond the normative period. SENILEK AT3/C 150/24 wire was selected as a solution for the example under consideration. The application of this wire grade allows not only the overhead line capacity to be increased by 151% without replacing the existing supports, but also active and reactive power losses in the electric network to be reduced by 18.9 and 2.5%, respectively. The proposed methodology enables selection of an optimal grade and cross-section of any wire design, taking the dynamically changing operational conditions of power grid systems into account. The solutions found by the proposed methodology may contribute to a more efficient operation of power lines due to increasing their transmission capacity, reducing the number of used supports, replacing the line wire without replacing supports, decreasing ice formation on wires, and reducing power losses.

Design and stability performance optimization of a novel hybrid inspection robot walking device for smart grid applications

Inspecting in high-altitude windy environments requires maintaining a stable attitude to avoid significant oscillations, which is a crucial factor in advancing the use of hybrid power transmission line inspection robots (PTLIRs) in real-world scenarios for the electrical power and energy industry. We developed and optimized a novel walking device to improve the safety of hybrid PTLIRs operating in strong wind conditions. First, we investigate the operational conditions of transmission line inspection and design a walking device capable of transporting a flight device to be suspended under the line for inspection purposes. Second, a force equilibrium analysis is performed to explore the relationship between the key structural parameters of the walking device and the wind-induced deflection of the robot. A multiobjective optimization model is formulated to minimize the maximum deflection angle of the robot along three axes under wind loads. Finally, an optimization algorithm, using an improved non-dominated sorting particle swarm optimization (NSPSO) approach, is proposed to solve the model with good global search ability and fast convergence speed. A test platform is constructed to collect the wind deflection angle of a self-developed hybrid PTLIR under the transmission line. The test results demonstrate that the walking device can carry 30 kg to move along the transmission line. The average deflection angle of the robot around the Z axis is the highest under the influence of wind force level 7 (wind speed of 15.5 m/s) perpendicular to the windward surface of the robot. After optimization, the maximum deflection angle is limited to 10.38°, aligning within safety thresholds. The optimization algorithm effectively reduces the maximum deflection angles around the X, Z, and Y axes by 33.19 %, 39.21 %, and 13.23 %, with minimal average errors of 2.40 %, 3.87 %, and 2.13 %, respectively. The research in this paper can solve the bottleneck problem of robot practicality, which has important theoretical significance and practical application value for the safety, stability, and intelligent operation and maintenance management of power transmission lines.

Three-dimensional modeling of 500 kV transmission lines by airborne LiDAR

This study aims to enhance the accuracy of three-dimensional (3D) modeling in the planning, design, operation management, and risk assessment of 500 kV transmission lines. Addressing the limitations of traditional two-dimensional (2D) or low-precision 3D models, a new method based on airborne Light Detection and Ranging (LiDAR) technology is proposed. Initially, high-density, high-precision 3D point cloud data are collected using airborne LiDAR technology. This data is then processed using specialized software for noise removal, terrain classification extraction, and Geographic Information System (GIS) data integration. Subsequently, a Convolutional Neural Network (CNN) is employed to classify the 3D point cloud data to enhance the accuracy of complex structure recognition. Lastly, the point cloud data are converted into linear and planar entities using Computer-Aided Design (CAD) technology, achieving high-precision 3D reconstruction of the 500 kV transmission lines and their complex terrain environment. Experimental results show that the spatial resolution of the constructed model reaches 0.1 m per pixel, with overall geometric accuracy within ±5 cm. Compared to traditional aerial imagery and ground survey methods, the model based on airborne LiDAR technology achieves improvements of 30 % in detail richness and 35 % in terrain adaptability, with a 45 % increase in decision-making efficiency. These improvements significantly enhance the accuracy of line operations, safety assessments, and environmental impact analyses, providing solid data support for the modernization and intelligent development of the power industry.

Generalized symmetry in dynamical gravity

We explore generalized symmetry in the context of nonlinear dynamical gravity. Our basic strategy is to transcribe known results from Yang-Mills theory directly to gravity via the tetrad formalism, which recasts general relativity as a gauge theory of the local Lorentz group. By analogy, we deduce that gravity exhibits a one-form symmetry implemented by an operator Uα labeled by a center element α of the Lorentz group and associated with a certain area measured in Planck units. The corresponding charged line operator Wρ is the holonomy in a spin representation ρ, which is the gravitational analog of a Wilson loop. The topological linking of Uα and Wρ has an elegant physical interpretation from classical gravitation: the former materializes an exotic chiral cosmic string defect whose quantized conical deficit angle is measured by the latter. We verify this claim explicitly in an AdS-Schwarzschild black hole background. Notably, our conclusions imply that the standard model exhibits a new symmetry of nature at scales below the lightest neutrino mass. More generally, the absence of global symmetries in quantum gravity suggests that the gravitational one-form symmetry is either gauged or explicitly broken. The latter mandates the existence of fermions. Finally, we comment on generalizations to magnetic higher-form or higher-group gravitational symmetries.

К совершенствованию технологии планирования транспортных процессов взаимодействия железнодорожных линий различных категорий

Purpose: increasing the efficiency of the transport network based on the developed balanced system of indicators for the interaction of railway lines of various categories. Methods: methods of control theory, system analysis, synthesis and mathematical modeling, theory of transport processes and systems, and transport planning methods are used. Results: a set of indicators that influence the interaction of railway lines of various categories is substantiated, based on a balanced system of indicators, which makes it possible to develop corrective measures to optimize the transportation process. Practical significance the implementation of the proposed solutions allows us to develop and specify a program to increase the efficiency of operation of low-intensity public railway lines and adjust scheduled transportation on these lines.

Lightning Location Method Using Orthogonal Coded Polarization Optical Time-Domain Reflectometer in Optical Fiber Transmission

Lightning strike is one of the main factors affecting power transmission lines and may lead to broken strands and damage of transmission lines, resulting in abnormal power supply. To improve the accuracy of lightning location, this paper presents a polarization optical time-domain reflectometer (POTDR) lightning location method based on orthogonal coding technology. The lightning strike point can be located by detecting the state of polarization (SOP) mutation of Rayleigh backscattering in the POTDR system. The orthogonal coding technology is used to improve the distance and accuracy of POTDR lightning location. Through calculation and simulation analysis, the location accuracy is significantly improved at the same sensing distance compared with the ordinary POTDR lightning location method. The lightning strike location error can be limited to 200 m in 20 km sensing distance which meets the actual engineering needs. The results of this paper are important to improve the operation and maintenance efficiency of transmission lines and reduce maintenance costs in the power industry.

Giant graviton expansion for general Wilson line operator indices

We propose a giant graviton expansion for Wilson line operator indices in general representations. The inserted line operators are specified by power sum symmetric polynomials pλ labeled by partitions λ. We interpret the partitions as the structure of fundamental string worldsheets wrapping around the temporal circle. The strings may or may not end on giant gravitons, and by summing the contributions from all brane configurations consistent with the specified partitions, we obtain the finite N line operator index. The proposed formula is consistent with known results and passes highly non-trivial numerical tests.

Study on the detection of power line operation anomalies in the Hexi Corridor based on multi-scale filtering of BeiDou satellite signals

In the artificial intelligence detection of power line anomalies, the identification effect is determined by the positioning process of two-dimensional map, which is also a difficult point in this field Therefore, this paper proposes a method to detect the abnormal operation of power lines in Hexi Corridor based on multi-scale filtering of Beidou satellite signals. Multi-scale processing is carried out by wavelet transform to obtain multiple wavelet basis functions of different scales. On this basis, a two-dimensional time–frequency diagram is drawn according to the results of continuous wavelet transform. The adaptive multi-time frequency entropy method is selected to divide the two-dimensional time–frequency map into multiple layers and scales, and the adaptive multi-scale time–frequency entropy of the time–frequency plane is calculated. After smoothing the obtained power line position signal by S-G filtering method, the power line position signal of Hexi Corridor is obtained by GPS/ Beidou dual-mode satellite positioning system, and the positioning signal is optimized by using the positioning mechanism of multiple receiving stations. Realize the abnormal operation detection of power lines in Hexi Corridor. The experimental results show that this method can obtain more accurate positioning signals, and the geometric accuracy factor GDOP fluctuates in the range of 0 ∼ 2. The unsmooth signal can be effectively removed; The maximum error result of abnormal position detection is 1.22kn；; At the same time, the abnormal displacement section of the line can be determined.

ADHM wilson line defect indices

The Coulomb and Higgs indices of the 3d N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{N} $$\\end{document} = 4 U(N) ADHM theories can be decorated by line defect operators as the line defect correlators. We obtain exact closed-form expressions and various non-trivial algebraic relations for the correlators of the Wilson lines in the fundamental and (anti)symmetric representations by means of the Hall-Littlewood expansion, the Fermi-gas method and the residue calculation. From the large N limit of the correlators we obtain the single particle gravity indices which are expected to encode the spectra of fluctuation modes on the gravity duals of line operators in M2-brane SCFTs.