Operation Of Lines Research Articles

Design and research of high-frequency electromagnetic wave detection antenna for discharge of transmission lines

Abstract Overhead lines have a wide range of applications in power systems. When early insulation faults occur in the long-term operation of overhead lines, corona discharge may occur. Timely detection of corona discharge can guide maintenance personnel to discover and eliminate faults in a timely manner. The height and distribution of overhead lines are relatively high, and close-range detection cannot quickly locate the discharge area. Using radio frequency antennas to measure the electromagnetic wave signals generated by corona discharge can achieve long-distance positioning detection of corona discharge. This article uses a log-periodic antenna as the detection antenna and simulates the antenna structure to prove that its directionality, gain, and measurement frequency band meet the design requirements. Finally, the discharge detection performance of the antenna is verified through experiments.

Lattice realizations of topological defects in the critical (1+1)-d three-state Potts model

Topological/perfectly-transmissive defects play a fundamental role in the analysis of the symmetries of two dimensional conformal field theories (CFTs). In the present work, spin chain regularizations for these defects are proposed and analyzed in the case of the three-state Potts CFT. In particular, lattice versions for all the primitive defects are presented, with the remaining defects obtained from the fusion of the primitive ones. The defects are obtained by introducing modified interactions around two given sites of an otherwise homogeneous spin chain with periodic boundary condition. The various primitive defects are topological on the lattice except for one, which is topological only in the scaling limit. The lattice models are analyzed using a combination of exact diagonalization and density matrix renormalization group techniques. Low-lying energy spectra for different defect Hamiltonians as well as entanglement entropy of blocks located symmetrically around the defects are computed. The latter provides a convenient way to compute the g-function which characterizes various defects. Finally, the eigenvalues of the line operators in the “crossed channel” and fusion of different defect lines are also analyzed. The results are all in agreement with expectations from conformal field theory.

The Management of Railway Operations during the Planned Interruption of Railway Infrastructure

A planned interruption of railway infrastructure is a situation where the operation of the track line or the operation of railway transport is limited. If there is also a restriction on the railway infrastructure, it means there will be complications not only for passengers but, above all, for railway undertakings operating freight transport. However, because of the planned railway infrastructure interruption, the quality of services provided not only to passengers but also to freight transport is decreasing. The aim of this paper is to propose effective planned maintenance works based on the analysis and evaluation of the processes performed during the planned railway infrastructure interruption or restriction. The research describes the process of affected railway infrastructure from technical, cost, and safety points of view. A methodological procedure is proposed under the condition of the Czech infrastructure manager. The main method is the calculation of the costs for the railway infrastructure manager and railway operator during the infrastructure interruption. The application part is undertaken using two interrupted lines according to the established alternative timetable in the area of České Budějovice.

Understanding the SM gauge group from SMEFT

We discuss heavy particles that can be used to pin down the faithful Standard Model (SM) gauge group and their patterns in the SM effective field theory (SMEFT). These heavy particles are not invariant under a specific ℤ6 subgroup of SU(3)c × SU(2)L × U(1)Y, which however acts trivially on all the SM particles, hence the faithful SM gauge group remains undetermined. Different realizations of the faithful SM gauge group correspond to different spectra of heavy particles, and they also correspond to distinct sets of line operators with one-form global symmetry acting on them. We show that the heavy particles not invariant under the ℤ6 group cannot appear in tree-level ultraviolet completions of SMEFT, this enforces us to consider one-loop UV completions of SMEFT to identify the ℤ6 non-invariant heavy particles. We demonstrate with examples that correlations between Wilson coefficients provide an efficient way to examine models with ℤ6 non-invariant heavy particles. Finally, we prove that all the scalars that can trigger electroweak symmetry breaking must be invariant under the ℤ6 group, hence they cannot be used to probe the faithful SM gauge group.

Understanding land subsidence in the Pearl River Delta region of China based on InSAR observations

Land subsidence occurs in river delta areas. A comprehensive understanding of the factors contributing to land subsidence is crucial for effective geohazard mitigation and land planning. This study investigates the distribution characteristics and spatiotemporal evolution pattern of the land subsidence in the Pearl River Delta based on the InSAR-derived ground deformation data spanning 2016 to 2021; and, the effects of the influencing factors, including the Quaternary sediment thickness, land use classification, land reclamation, and subway construction and operation, on the subsidence are analyzed. The results unveil the presence of four major subsidence zones and several minor coastal subsidence bowls in the research area, with a total area of about 1100 km, all of which exhibit prolonged and intense subsidence. In the remaining regions, land subsidence predominantly exhibits weak and short-term occurrences. The subsidence in the study area follows a distinct linear evolutionary pattern with a slight rebound, potentially linked to the rise of groundwater levels. Regions with Quaternary sediment thickness surpassing 20 m, engaging in aquaculture or agricultural activities, and featuring reclaimed land are predisposed to prolonged and intense ground subsidence. In addition, the construction and operation of subway lines may induce ground subsidence, especially in geologically vulnerable areas. This study provides valuable insights into land subsidence research in delta regions.

Research on Anti-breakage Target Detection Method of Transmission Line based on Improved YOLOv5

Abstract Illegal construction operations and sudden outbursts in transmission corridor protection areas pose a threat to the safe and stable operation of transmission lines. It is very important to identify the transmission line breakage risk to guarantee power grid security. The existing target detection model has many parameters and a large capacity, which makes it difficult to implement the deployment of edge terminals and cannot detect the risk of transmission line outbreaks in real time. This paper proposed a transmission line anti-breach detection method based on improved YOLOv5. Firstly, the CSPdarknet53 module is replaced with a lightweight MobilenetV3 module to reduce the model parameter number. The SPP module is replaced by SimSPPF module to realize the fast conversion of multi-scale image convolution features. Finally, the ECA attention mechanism is introduced to improve the ability of the model to focus on key features, thereby improving the overall performance of the model. Experiment results show that the proposed method has a smaller parameter number and a mAP value of 97.77%, which is the best overall performance, and provides support for the realization of outbreak risk warning of transmission lines based on edge intelligence.

Deep Learning-based Detection of Bird’s Nests on Power Transmission Towers

Abstract The problem of birds building nests on high-altitude towers has posed a significant hidden danger to the safe operation of long-distance transmission lines. The current manual inspection method is inefficient and costly, while automatic inspection technology still faces challenges in accuracy and efficiency. This article mainly aims to propose a deep learning target detection algorithm YOLOV3 based on convolutional neural networks to monitor and retrieve bird damage faults on power towers. By constructing a large dataset of bird nests on power towers, deep learning training models are used to extract features of the detection targets, and the YOLOV3 algorithm is used to intelligentially identify images with bird damage faults. Experiments show that YOLOV3 can effectively detect bird nests on power towers.

Quantum machine learning with Qiskit: Evaluating regression accuracy and noise impact

AbstractQuantum machine learning (QML) can be employed in solving complicated machine learning tasks although the performance in examining the regression processes is only barely understood. Knowledge gaps are intended to be closed by studying modelling performance of QML in regression tasks, with emphasis being dedicated to scaling up and ability to resist noise. The regression part offers the following functions that include straight line and complex operations. Furthermore, the authors employ quantum neural networks generated using Qiskit to perform experiments. The results demonstrate that QML has a remarkable level of accuracy in basic regressions, reaching a maximum of 97%. Nevertheless, there are difficulties in representing intricate functions, such as 5 × cos(x), which results in a noticeable decline in performance. The work deals with the influence of noise and IERs from imperfect hardware on the efficiency of QML algorithms providing insight into the core obstacles. The result of a detailed examination of the results that have tested the powers and limits of QML in the development of regression applications is represented. The future direction of research and development will be defined by the results obtained in it.

Risk and Prevention Control Analysis of Live Line Operation of Distribution Network Bypass

Risk and Prevention Control Analysis of Live Line Operation of Distribution Network Bypass

تطبيق دروس السلامة المستفادة من قطاع الطيران المدني التجاري على صناعة النفط والغاز

Commercial aviation safety-related protocols and initiatives have received considerable interest from several industries due to the widely recognition of their effectiveness as an ultra-safe industry. And yet, it’s not clear how to adopt these protocols in oil and gas sector. Thus, this article provides a typology of these protocols to apply to the oil and gas sector. This paper examines seven examples of error counter measures used in commercial aviation safety. Examples are Crew Resource Management (CRM) and Line Operations Safety Audit (LOSA) methodology. This would help in developing a conceptual typology that might potentially apply to the oil and gas industry. The initiatives examined in this article fall into twofold themes: concepts that seek to increase collective awareness of safety values; and concepts that promote safety by design. The results of this research revealed that all task checklists, joint safety briefings, and the confidential safety reporting system are protocols that help users routinely enhance workplace safety. However, initiatives such as the sterile cockpit system and LOSA approach in frontline operations fall under the responsibility of middle and upper management to ensure safety performance. Examining and discussing the proposed typology with the sector’s safety experts and front-line professionals before the implementation process would highlight any unanticipated challenges, and therefore proactively reduce them to promote an organizational safety culture in the oil and gas sector which enables operational cost reduction and mitigates loss-time injury. Finally, this study outlines that it is possible to benefit from aviation safety standards in the oil and gas sectors and achieve higher safety performance. Keywords: Oil & Gas Safety; Occupational Safety; Safety Management; Safety Culture; Aviation Safety; Threat and Error Management.

Planning of multi-production line maintenance

Efficiency of production lines is significantly impacted by maintenance activities, which are often overshadowed by the focus on production planning. The symbiotic relationship between maintenance and production activities is a critical yet overlooked factor. We examine the importance of maintenance planning, specifically within the dynamic context of production line operations. In this maintenance planning setting, scarce resources, particularly technicians, are commonly disregarded. Our research addresses the challenge of real-time maintenance planning across multiple production lines by developing a state-of-the-art heuristic, which takes into account machine production states and resource availability and is augmented by re-enactment procedures. We validate the heuristic by employing Markov Decision Processes and digital twin Discrete Event Simulations, which leverage real-world production data. This modeling framework is new for this specific problem. In our experiments, we compare the heuristic against optimal policies and practitioner-based policies. Our heuristic matches the performance of optimal policies in the Markov Decision Processes and significantly outperforms FCFS and practitioner-based policies in the Discrete Event Simulations. Notably, our heuristic is not only effective against standard policies, realizing improvement of 1% to 3%, but it is also the first to be generally applicable to production lines involving multiple machines and resources in real-time.

Line operators, vortex statistics, and Higgs versus confinement dynamics

We study a 2+1D lattice gauge theory with fundamental representation scalar fields which has both Higgs and confining regimes with a spontaneously-broken U(1) 0-form symmetry. We show that the Higgs and confining regimes may be distinguished by a natural gauge invariant observable: the phase Ω of a correlation function of a vortex line operator linking with an electric Wilson line. We employ dualities and strong coupling expansions to analytically explore parameter regimes which were inaccessible in previous continuum calculations, and discuss possible implications for the phase diagram.

The transmission line foreign body detection algorithm based on weighted spatial attention.

The secure operation of electric power transmission lines is essential for the economy and society. However, external factors such as plastic film and kites can cause damage to the lines, potentially leading to power outages. Traditional detection methods are inefficient, and the accuracy of automated systems is limited in complex background environments. This paper introduces a Weighted Spatial Attention (WSA) network model to address the low accuracy in identifying extraneous materials within electrical transmission infrastructure due to background texture occlusion. Initially, in the model preprocessing stage, color space conversion, image enhancement, and improved Large Selective Kernel Network (LSKNet) technology are utilized to enhance the model's proficiency in detecting foreign objects in intricate surroundings. Subsequently, in the feature extraction stage, the model adopts the dynamic sparse BiLevel Spatial Attention Module (BSAM) structure proposed in this paper to accurately capture and identify the characteristic information of foreign objects in power lines. In the feature pyramid stage, by replacing the feature pyramid network structure and allocating reasonable weights to the Bidirectional Feature Pyramid Network (BiFPN), the feature fusion results are optimized, ensuring that the semantic information of foreign objects in the power line output by the network is effectively identified and processed. The experimental outcomes reveal that the test recognition accuracy of the proposed WSA model on the PL (power line) dataset has improved by three percentage points compared to that of the YOLOv8 model, reaching 97.6%. This enhancement demonstrates the WSA model's superior capability in detecting foreign objects on power lines, even in complex environmental backgrounds. The integration of advanced image preprocessing techniques, the dynamic sparse BSAM structure, and the BiFPN has proven effective in improving detection accuracy and has the potential to transform the approach to monitoring and maintaining power transmission infrastructure.

Dynamic response analysis of high-speed train gearboxes excited by wheel out-of-round: experiment and simulation

Due to the special characteristics of rail vehicles, wheel–rail excitation has always had a huge impact on the stable operation of the vehicle system and the dynamic performance of each component structure. This paper takes high-speed train gearbox as the research object and establishes a rigid-flexible coupling dynamics model and analyses the dynamic response and fatigue damage of the gearbox under the excitation of wheel flat and wheel polygon. Combined with the analysis of test data from the gear transmission testrig and actual line operation of high-speed trains, it shows that high-speed train gearboxes are different from those applied in other fields, where the gear meshing excitation is no longer its main excitation source, but the wheel–rail excitation instead. The comparison analysis between the wheel flat and wheel polygon conditions indicates that the response of the gearbox to the wheel flat excitation is similar to the operational modal test and is capable of causing multi-order modal vibration of the gearbox. The effect of the wheel polygon on the gearbox is similar to that of single-frequency excitation, which lead to certain order modal resonance in the gearbox under frequency coupling situations. The impact of both conditions on gear box fatigue damage increases with the vehicle operating speed.

UAV‐aided distribution line inspection using double‐layer offloading mechanism

AbstractWith the continuous growth of electricity demand, the safe and stable operation of distribution lines is crucial for power transportation. Unmanned aerial vehicle (UAV) inspection has been widely used for the maintenance and repair of distribution lines. Due to the limitations of computational power and endurance, it is difficult for UAVs to independently complete data processing. Combined with mobile edge computing (MEC), this paper proposes a computing offloading strategy based on multi‐agent reinforcement learning and double‐layer offloading mechanism, which can further utilize the computing power of non‐task devices and edge servers. Firstly, three‐layer system architecture, named MEC‐U‐NTDC (MEC‐UAV‐Non‐task Device Cloud), is built. Secondly, double‐layer offloading mechanism is designed to comprehensively utilize the computing power of edge servers and neighbouring non‐task devices. Finally, a multi‐agent algorithm DLMQMIX is proposed to minimize the total cost for UAV inspection. Simulation experiments show that the proposed algorithm can effectively solve the task offloading problem of UAV‐aided distribution line inspection, and compared with algorithms such as PSO, GA, and QMIX, it performs better in terms of average delay, system cost, and load balancing, achieving a smaller total system cost.

PERHITUNGAN BEBAN KERJA OPERATOR LINI PERAKITAN KOMPRESOR TIPE SVC93E20PBE UNIT REFRIGERATOR R600A DI PT. PANASONIC MANUFACTURING INDONESIA

Panasonic Manufacturing Indonesia produces electronic equipment especially for the needs of ordinary consumers, business and industry such as refrigerators, ACs, water pumps, fans, washing machines and audio with problems with refrigerator and AC production units. The research was carried out on the SVC93E20PBE type compressor assembly line for the R600A refrigerator unit with the problem of operator workload being too high, above 100%. The research was conducted for 30 days on three compressor assembly line operators using the workload analysis method, with the aim of obtaining the productive percentage and workload level of operators on the SVC93E20PBE type compressor assembly line for the R600A refrigerator unit. Data was taken following a standard work sampling observation sheet, containing employee activities while working or unemployed with adjustments. The results of workload measurement using the workload analysis method showed that operator 1's workload was 133.63%, operator 2's workload was 133.62%, and operator 3's workload was 133.74%. Based on the description of the workload results for compressor assembly operators for the SVC93E20PBE type 600A refrigerator unit, the researcher proposes the addition of two operators to the compressor assembly line, a total of five operators with a workload for each operator of 80.198%.

Calculation study of ion flow field in ±800 kV transmission lines under the effects of air pressure, temperature, humidity and improvement measures

In line operation, Ultra-High Voltage Direct Current (UHVDC) transmission lines must meet the corresponding electromagnetic environment requirements; the ion flow field is a crucial environmental index of ultra-high-voltage DC transmission lines. Under the combined effects of humidity, air pressure, and temperature, the ion flow field is easily influenced. A calculation method for the ion flow field of UHVDC transmission lines that considers the combined effects of humidity, air pressure, and temperature is proposed to investigate the impact on the ion flow field. Based on this, when the humidity exceeds 60%, the influence of the charging characteristics of airborne droplets on the ion flow field is considered to align the calculations more closely with actual conditions. An improved finite element method is used to calculate the ground synthetic electric field strength and ion flow density of the ±800 kV line under these combined effects, and its effectiveness is validated through empirical testing. The results show that the synthetic electric field strength under the positive line decreases with increasing humidity, peaks at 90% humidity under the negative line and reaches a minimum at 50% humidity for the negative line. When considering the charging characteristics of droplets with humidity above 60%, the maximum synthetic electric field strength under the positive line is increased by 5.46%, and under the negative line by 5.5%, while the ground ion flow density remains largely unchanged. Finally, in conjunction with the electromagnetic environmental parameter limits for UHVDC transmission lines promulgated by China, improvement measures are proposed for the ground ion flow field.

Action of the Axial U(1) Non-Invertible Symmetry on the ’t Hooft Line Operator: A Lattice Gauge Theory Study

Abstract We study how the symmetry operator of the axial $U(1)$ non-invertible symmetry acts on the ’t Hooft line operator in the $U(1)$ gauge theory by employing the modified Villain-type lattice formulation. We model the axial anomaly by a compact scalar boson, the “QED axion”. For the gauge invariance, the simple ’t Hooft line operator, which is defined by a line integral of the dual $U(1)$ gauge potential, must be “dressed” by the scalar and $U(1)$ gauge fields. A careful consideration on the basis of the anomalous Ward–Takahashi identity containing the ’t Hooft operator with the dressing factor and a precise definition of the symmetry operator on the lattice shows that the symmetry operator leaves no effect when it sweeps out a ’t Hooft loop operator. This result appears inequivalent with the phenomenon concluded in the continuum theory. In an appendix, we demonstrate that the half-space gauging of the magnetic $\mathbb {Z}_N$ 1-form symmetry, when formulated in an appropriate lattice framework, leads to the same conclusion as above. A similar result is obtained for the axion string operator.

Next-to-eikonal corrected double graviton dressing and gravitational wave observables at OG2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{O}\\left({G}^2\\right) $$\\end{document}

Following a recent proposal to describe inelastic eikonal scattering processes in terms of gravitationally dressed elastic eikonal amplitudes, we motivate a collinear double graviton dressing and investigate its properties. This is derived from a generalized Wilson line operator in the worldline formalism by integrating over fluctuations of the eikonal trajectories of external particles in gravitationally interacting theories. The dressing can be expressed as a product of exponential terms — a coherent piece with contributions to all odd orders in the gravitational coupling constant and a term quadratic in graviton modes, with the former providing classical gravitational wave observables. In particular, the coherent dressing involves Oκ3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{O}\\left({\\kappa}^3\\right) $$\\end{document} subleading double graviton corrections to the Weinberg soft factor. We use this dressing to derive expressions for the waveform, radiative momentum spectrum and angular momentum. In a limiting case of the waveform, we derive the nonlinear memory effect resulting from the emission of nearly soft gravitons from a scattering process.

Predicting the remaining useful life of rails based on improved deep spiking residual neural network

the remaining useful life (RUL) of rails is important to ensure safe and reliable operation of railway transportation lines. However, the severity of deterioration of rails from different types of damage varies. Multichannel vibrational data collected by sensors can be utilized to identify the relevant complex correlations and temporal relationships; however, existing methods struggle to predict rail damage accurately. Thus, this paper proposes a RUL prediction method for rails based on an improved deep spiking residual neural network. First, multichannel data collected by sensors are used directly as input to the predictive network without requiring a separate feature extraction step. Additionally, an encoding module is designed to adaptively convert the temporal sequences of data into spiking signals to reduce the information loss during the encoding process. Second, a spiking residual convolutional neural network (CNN) is incorporated into the proposed method to extract optimal features. The separable, spiking CNN can model the relationships among multichannel data accurately, and an attention mechanism is implemented to recalibrate the spiking feature maps such that the predictive network can distinguish between items of information effectively. Third, the spiking residual connections are altered to mitigate network degradation caused by the incompatibility between the conventional residual connections and the spiking neural network. Finally, the obtained spiking features are input to a fully connected layer to predict the rail RUL. Experimental results demonstrated that the proposed method can predict the rail RUL accurately, and validation results obtained on a rolling bearing dataset demonstrated the high generalizability of the proposed method.