Grid Transmission Line Research Articles

A Lightweight and Efficient Multi-Type Defect Detection Method for Transmission Lines Based on DCP-YOLOv8.

Currently, the intelligent defect detection of massive grid transmission line inspection pictures using AI image recognition technology is an efficient and popular method. Usually, there are two technical routes for the construction of defect detection algorithm models: one is to use a lightweight network, which improves the efficiency, but it can generally only target a few types of defects and may reduce the detection accuracy; the other is to use a complex network model, which improves the accuracy, and can identify multiple types of defects at the same time, but it has a large computational volume and low efficiency. To maintain the model's high detection accuracy as well as its lightweight structure, this paper proposes a lightweight and efficient multi type defect detection method for transmission lines based on DCP-YOLOv8. The method employs deformable convolution (C2f_DCNv3) to enhance the defect feature extraction capability, and designs a re-parameterized cross phase feature fusion structure (RCSP) to optimize and fuse high-level semantic features with low level spatial features, thus improving the capability of the model to recognize defects at different scales while significantly reducing the model parameters; additionally, it combines the dynamic detection head and deformable convolutional v3's detection head (DCNv3-Dyhead) to enhance the feature expression capability and the utilization of contextual information to further improve the detection accuracy. Experimental results show that on a dataset containing 20 real transmission line defects, the method increases the average accuracy (mAP@0.5) to 72.2%, an increase of 4.3%, compared with the lightest baseline YOLOv8n model; the number of model parameters is only 2.8 M, a reduction of 9.15%, and the number of processed frames per second (FPS) reaches 103, which meets the real time detection demand. In the scenario of multi type defect detection, it effectively balances detection accuracy and performance with quantitative generalizability.

From currents to controversies: Unveiling performances of and perceptions on China's investments in the Philippine grid

The electrical grid system is a technology and a complex amalgamation of various factors, including geography, relationships, institutions, and socio-political-economic influences. Although China's involvement in international grids has primarily been analysed from the political economy perspective, little attention has been given to its impact on the electricity systems of host countries and how it affects their performance. This article addresses this gap by combining insights from international relations, political economy, and electricity systems transitions to analyse databases, news accounts and interview data on the performance and perceptions of energy actors regarding China's involvement in the Philippine electricity grid. Dissatisfaction with delays in project implementations, declining contributions to the development of in-house technological capabilities of the national grid company and transmission lines, diversification of local economic elites and oligarchs, and a lack of transparency have intensified mutual distrust between the Chinese grid company and prominent energy actors in the Philippines and failed to mitigate national security concerns. Our findings imply that changes in the constellation of local energy actors amid the electricity sector reform and integration of renewable energy in grids affect the role of the grid and justification for international grid investors. The perception of unsatisfactory performances raises concerns about national security during the worsening of bilateral relations.

Smart grid line fault detection based on deep learning image recognition algorithm

Abstract Amid the rapid evolution of smart grids, stringent demands for reliability, safety, and efficiency have escalated for transmission lines. Nevertheless, given their extensive coverage and the complexity of their environments, traditional inspection techniques struggle to meet the rigorous standards of real-time monitoring and precision. This paper introduces a novel fault detection approach for smart grid transmission lines, leveraging advanced deep-learning image recognition algorithms. This method improves the YOLOv5 series models by combining the Convolution Attention Module (CBAM), Bidirectional Feature Pyramid Network (BiFPN), and MobileNet-V3 as the feature extraction network of YOLOv5 to achieve defect detection of hardware on transmission lines. CBAM improves the model’s sensitivity to tiny defects by focusing on key areas and features. BiFPN improves detection accuracy and robustness through efficient fusion of multi-scale features. Furthermore, the integration of MobileNet-V3 enhances both the efficiency and precision of feature extraction, ultimately elevating the overall model’s performance. Experimental results show that compared with the original YOLOv5 model, the improved algorithm can achieve 92.89% accuracy and 84.10% recall, which is better than other mainstream target detection algorithms. Especially in the detection of defects in small targets and complex backgrounds, the improved model shows stronger robustness and adaptability. This provides strong technical support for the efficient operation and maintenance of smart grids.

Improved YOLOv7 model for insulator defect detection

<abstract> <p>Insulators are crucial insulation components and structural supports in power grids, playing a vital role in the transmission lines. Due to temperature fluctuations, internal stress, or damage from hail, insulators are prone to injury. Automatic detection of damaged insulators faces challenges such as diverse types, small defect targets, and complex backgrounds and shapes. Most research for detecting insulator defects has focused on a single defect type or a specific material. However, the insulators in the grid's transmission lines have different colors and materials. Various insulator defects coexist, and the existing methods have difficulty meeting the practical application requirements. Current methods suffer from low detection accuracy and mAP0.5 cannot meet application requirements. This paper proposes an improved you only look once version 7 (YOLOv7) model for multi-type insulator defect detection. First, our model replaces the spatial pyramid pooling cross stage partial network (SPPCSPC) module with the receptive filed block (RFB) module to enhance the network's feature extraction capability. Second, a coordinate attention (CA) mechanism is introduced into the head part to enhance the network's feature representation ability and to improve detection accuracy. Third, a wise intersection over union (WIoU) loss function is employed to address the low-quality samples hindering model generalization during training, thereby improving the model's overall performance. The experimental results indicate that the proposed model exhibits enhancements across various performance metrics. Specifically, there is a 1.6% advancement in mAP_0.5, a corresponding 1.6% enhancement in mAP_0.5:0.95, a 1.3% elevation in precision, and a 1% increase in recall. Moreover, the model achieves parameter reduction by 3.2 million, leading to a decrease of 2.5 GFLOPS in computational cost. Notably, there is also an improvement of 2.81 milliseconds in single-image detection speed. This improved model can detect insulator defects for diverse materials, color insulators, and partial damage shapes in complex backgrounds.</p> </abstract>

Fault Detection and Location of 35 kV Single-Ended Radial Distribution Network Based on Traveling Wave Detection Method

With the progress of society and the iterative improvement of infrastructure construction, the power grid transmission lines have also entered an era of intelligence. The national distribution system has made ensuring the regular operation of the distribution network as well as prompting troubleshooting and detection its top priority. Research on fault diagnosis for 35 kV single-ended radial distribution networks is still in its infancy compared to other hot topics in the industry, such as short-circuit fault detection and fault node localization. This study adopts the 35 kV single-ended radial distribution network as a model, detects fault lines via the traveling wave method, and accurately locates fault nodes using the wavelet conversion method, hoping to quickly identify and locate fault nodes in distribution networks. The experimental results demonstrate that the research method can quickly identify the faulty line and carry out further fault node location detection. The final obtained fault distance is 1.19 km with an actual error of only 0.16 km; the maximum relative errors are only 0.33 km and 0.21 km when the initial phase angle and transition resistance parameters are changed, respectively; and the error amplitude fluctuations are essentially stable. The experimental results also demonstrate that the research method can quickly identify the faulty line and carry out further fault node location.

A survey of sag monitoring methods for power grid transmission lines

AbstractThe transmission line is a fundamental asset in the power grid. The sag condition of the transmission line between two support towers requires accurate real‐time monitoring in order to avoid any health and safety hazards or power failure. In this paper, state‐of‐the‐art methods on transmission line sag monitoring are thoroughly reviewed and compared. Both the direct methods that use the direct video or image of the transmission line and the indirect methods that use the relationships between sag and line parameters are investigated. Sag prediction methods and relevant industry standards are also examined. Based on these investigation and examination, future research challenges are outlined and useful recommendations on the choices of sag monitoring methods in different applications are made.

Regional Multihazard Risk-Assessment Method for Overhead Transmission Line Structures Based on Failure Rate and a Bayesian Updating Scheme

In this study, a new method for multihazard risk assessment of an overhead transmission line grid located over a relatively large area is proposed. The main task of this method was to assess annual risk for overhead transmission lines using failure rates estimated from historical failure data and then modifying these estimated failure rates by reanalysis data and a Bayesian updating scheme. For this purpose, a comprehensive database of structural failures was collected for climatic, geologic, and man-made hazards to overhead transmission lines. Risk denotes the probability of exceeding the sum of direct losses due to repair costs of overhead transmission line components and indirect losses due to energy not supplied or unplanned power-off after outages. The performance of the proposed method was evaluated in a case study of a target area. The results showed that climatic hazards contributed the most to total losses in the study area. Furthermore, when using indirect losses to estimate the annual loss, the average annual loss may increase by up to about 45% relative to direct losses, depending on the type of hazard. This demonstrates the importance of investigating indirect losses from structural failures in the risk assessment of overhead transmission lines.

Multi-Objects Recognition and Self-Explosion Defect Detection Method for Insulators Based on Lightweight GhostNet-YOLOV4 Model Deployed Onboard UAV

In order to meet the application requirements on unmanned aerial vehicle (UAV) for daily inspection of insulators in overhead grid transmission lines, a lightweight GhostNet-YOLOV4 model is proposed to identify the insulators objects and detect their self-explosion defect meanwhile. In the proposed model, the lightweight GhostNet with full convolutional attention module (C-SE) model is embedded into the YOLOV4 as backbone feature extraction network, and the ordinary convolution and Mish activation function of enhanced feature extraction network in GhostNet-YOLOV4 are replaced by the depth separable convolution and ReLU6 activation function respectively. Meanwhile, the combination methods of data augmentation, label smoothing, K-Means algorithm, adaptive cosine annealing learning rate, and Focal Loss function are adopted to optimize the model. Finally, the class activation mapping (CAM) is used to visually analyze for the identification process of the proposed model. The example analysis results show that the object recognition accuracy mAP of the proposed model is 99.50% for insulator objects and self-explosion defect, and the image processing speed FPS is 2.53 frames/s. The proposed model meets the requirements of lightweight, detect accuracy and speed for UAV application.

Research and application of high voltage measurement technology in DC UHV preventive test

With the state’s high attention to clean energy and the requirement to reduce the loss of power grid transmission lines, DC UHV has developed rapidly in recent years. It refers to DC transmission and related technologies with voltage levels of ±800 kV (±750 kV) and above. In the power system, DC high-voltage transmission is inseparable from power equipment, but the higher the transmission voltage level, the higher the voltage of DC withstand voltage preventive test. Before the transmission of UHV DC transmission lines, if the DC voltage withstand device is 800 kV or above, the voltage withstand test shall not be carried out for the high-voltage line. The traditional direct comparison method using voltage divider cannot be used to measure the DC output voltage of DC UHV withstand voltage device on site. This paper analyzes the method of full coverage of DC UHV system in limited voltage range and the method of out of range calibration using scale factor. In order to solve the problem of voltage traceability in the DC high voltage generator, especially the voltage traceability of the field DC UHV device, when the voltage output range of the calibrated DC high voltage generator exceeds the measurement range of the standard, the standard value of the voltage calibration point beyond the range can be determined by comparison and obtained through the linearity test. The comparison with the standard shall be conducted at a voltage not lower than 20% of the rated voltage of the calibrated DC high voltage generator, and compared to the maximum voltage of the standard voltage divider. The comparison is carried out at a (a ≥ 2) voltage point. When comparing at a point, the highest voltage level is equal to the upper limit of the standard. Under a voltage point, according to the mathematical relationship between the voltage standard value of the tested device and the current flowing through the linear load resistance, the fitting function can be obtained through the linear regression method. For b (b ≥ 4) calibration points beyond the measurement range of DC standard voltage divider, the standard value can be “extrapolated” from the linear load resistance current and the fitting function Yang (2002). Through comparison and verification, the method is feasible, which is of great significance to promote the compilation of calibration specification for DC high voltage generator.

Correlation between extreme/ unusual ozone events and corona effect from electric power transmission & distribution grids. Assessing the health and environmental impact of high-voltage direct current (HVDC) grid on ozone ground levels in Sao Paulo metropolitan area, Brazil.

• BACKGROUND AND AIM 1- Ozone ground levels pose a risk for public health 2- The increasing ozone levels measured in parts of the metropolitan area of Sao Paulo have brought the attention of researchers. Proposed mechanisms do not provide a clear outcome for establishing root causes, and for clarifying sources of ozone. Spatial distribution of ozone also depends on wind patterns. 3- As an alternate hypothesis for explaining unusual high levels of ozone, the corona discharge from electric transmission grid, known as corona effect, can play a significative role in the absence of, or in coincidence with other concomitant factors, to explain ozone (O3) and nitric oxide (NO) unusual levels. Negative coronas emits more ozone than Positive coronas. 4- The Environmental Protection Agency- EPA, found in a report of 1973 (EPA-650/4-73-003), following a research carried out in areas of high concentration of transmission lines that "under minimal wind conditions, such transmission line concentrations can produce sizeable local ozone levels.” 5- Research applies geospatial and statistical analysis with the aim of better understanding correlations between exposures and ozone levels. • METHODS Geographic Information System (GIS)-based systems with average grid cell edge length of 10 km minimum. Global InMAP (Intervention Model for Air Pollution) with a variable resolution grid (4 km horizontal grid cell widths) in combination with GEOS- Chem simulations. Data analysis by machine-learning model. • RESULTS Applying Predictive Machine Learning algorithm as regression models, research found that coronas could generate significant ozone levels downwind of the power line. • CONCLUSIONS Evidence of correlation between unusual ozone measurements and location of key power electric grid transmission lines and distribution centers with potential for corona effect are found in Sao Paulo state and Sao Paulo city metropolitan area. • KEYWORDS Corona effect, ozone, electric grids, spatial simulation, wind patterns, air pollution

Fault analysis and comprehensive improvement of Xinjiang power grid transmission lines easy to ice

Fault analysis and comprehensive improvement of Xinjiang power grid transmission lines easy to ice

Design and short‐circuit current breaking test verification of a 72.5 kV single‐break fast vacuum circuit breaker

Fast clearing of a short-circuit fault is vital to increase the transmission capacity of power grid transmission lines and improve the transient stability of a power system. A fast vacuum circuit breaker (FVCB) is a good candidate to realize quick elimination of a short-circuit fault. The original contribution of this paper is to present the design of a 72.5 kV single-break FVCB and its verification of short-circuit breaking performance. A specially designed 72.5 kV vacuum interrupter, with floating shields and reduced mass of moving components, was developed. An electromagnetic repulsion actuator was used to provide a high initial opening speed, V1, of 5 m/s over half of the full-contact gap and a relatively low average opening speed, V2, of 1.59 m/s in the full contact gap of 30 mm. The high-designed V1 and low-designed V2 were achieved with the aid of a deliberately designed oil buffer. The opening and closing time of the designed 72.5 kV FVCB was 1.2 ± 0.04 ms and 18 ± 0.20 ms respectively. The terminal fault test duty of T100s(b), carried out on the 72.5 kV FVCB, demonstrated both the 40 kArms and 80 kArms short-circuit current breaking performance. The short-circuit current breaking arcing time windows of FVCB, including the minimum arcing time, the maximum arcing time, and the middle arcing time, was verified to fulfill the requirement of the international standard, IEC 62271-100 2017, and the Chinese standard, GB/T 1984–2014, both for the 40 kArms and 80 kArms switching cases, respectively. Further development of the 72.5 kV single-break FVCB would benefit the development of ultra- or extra-high voltage FVCBs by reducing essential numbers of series-connected breaking units.

Hamilton-Based Stability Criterion and Attraction Region Estimation for Grid-Tied Inverters Under Large-Signal Disturbances

As the crucial interface of renewable energy integration, the stable operation of power converters under grid faults and external disturbances has become much more important. However, synchronization instability of grid-tied inverters has been found caused by the phase-locked loop (PLL) under some critical operating conditions, such as the grid voltage sag, current surge, and transmission line faults. Due to the strong nonlinearity, transient stability analysis of inverters with PLL becomes much more complicated under large-signal perturbations and weak grid conditions. In this article, the large-signal synchronization stability of grid-tied inverters is investigated from the energy perspective. Based on the structural resemblance between the PLL and synchronous generator, the energy function is constructed to establish a port-Hamiltonian model for the grid-tied inverter. For the nonlinear inverter system, an explicit Hamilton-based stability criterion is developed for grid-tied converters according to the concept of dissipativity and LaSalle’s theorem. Moreover, the analytical stability boundary is acquired based on the proposed stability criterion and attraction region estimation, covering both controller parameters and grid parameters. To verify the effectiveness of the proposed analysis approach, extensive simulation and experimental results are presented. Compared with the conventional large-signal methods, the proposed Hamilton-based approach has explicit physical meaning and high accuracy.

Inequality Constraints Based Method for Fast Estimation of Droop Slope Stability Regions for MMC-Based MTDC Systems

This paper proposes an inequality constraints based method to efficiently and quickly estimate stability regions of droop control slopes for modular multilevel converter (MMC)-based multi-terminal dc (MTDC) systems. At first, a general small-signal model of the MMC-MTDC system is developed, which consists of the dc network and the MMCs with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$dq$</tex-math></inline-formula> controllers and multiple droop controllers. When deriving corresponding nonlinear state-space models, the edge-node matrix is introduced for the dc network modeling with arbitrary grid topology and transmission line model. Then, based on the eigenvalues sensitivity and the Taylor Series of eigenvalues, a set of inequality constraints are proposed to expeditiously estimate the suprema of the droop slopes and identify the droop slope stability regions for the MMC-MTDC system. To verify the state-space model, a comparison of dynamic responses between the math model calculation in MATLAB and the EMT simulation in PSCAD/EMTDC is conducted, which demonstrates the accuracy and the correctness of the developed small-signal model. The effectiveness of the proposed parameter stability region estimation method is demonstrated by several examinations including the supremum tests of droop slopes, the stability region sketches on accuracy, and the predicted unstable operations in PSCAD/EMTDC.

Experimental Analysis on Mechanical Properties of Aluminum Conductor Steel-reinforced for Transmission Line

Under the background of “carbon neutralization”, the power transmission quality of power grid transmission lines becomes more and more important. This paper discusses the mechanical properties of aluminum conductor steel reinforced(ACSR), the main medium of electric energy transmission. This test simulates the real environment, takes the stress-strain curve and elastic modulus as the starting point, divides the test process into four stages, and compares the mechanical properties of the same conductor under different loads in detail. On this basis, the data difference and performance comparison of three different sections of ACSR are obtained. Finally, the commonness and difference of different cross-section conductors are analyzed, and the precautions in the test process are given. The conclusion of this paper can promote the quality improvement of power transmission conductors.

LightGBM Low-Temperature Prediction Model Based on LassoCV Feature Selection

Icing disasters on power grid transmission lines can easily lead to major accidents, such as wire breakage and tower overturning, that endanger the safe operation of the power grid. Short-term prediction of transmission line icing relies to a large extent on accurate prediction of daily minimum temperature. This study therefore proposes a LightGBM low-temperature prediction model based on LassoCV feature selection. A data set comprising four meteorological variables was established, and time series autocorrelation coefficients were first used to determine the hysteresis characteristics in relation to the daily minimum temperature. Subsequently, the LassoCV feature selection method was used to select the meteorological elements that are highly related to minimum temperature, with their lag characteristics, as input variables, to eliminate noise in the original meteorological data set and reduce the complexity of the model. On this basis, the LightGBM low-temperature prediction model is established. The model was optimized through grid search and crossvalidation and validated using daily minimum surface temperature data from Yongshan County (station number 56489), Zhaotong City, Yunnan Province. The root mean square error, MAE, and MAPE of the model minimum temperature prediction after feature selection are shown to be 1.305, 0.999, and 0.112, respectively. These results indicate that the LightGBM prediction model is effective at predicting low temperatures and can be used to support short-term icing prediction.

Characteristic Analysis and Prevention of Ice Galloping of Transmission Lines Based on Finite Element Method

Ice galloping is one of the disasters that have the greatest impact on the safe operation of transmission lines in winter. This article analyze the characteristics and prevention method of ice galloping of electric grid transmission lines based on finite element method. The amplitude and time response of transmission line during ice galloping can be obtained. A new type of efficient anti-galloping interface spacer is proposed, which can reduce the galloping amplitude. The effectiveness of the proposed method and model is verified by application examples.

Research on Visual Management system of Power Transmission Line based on two-dimensional GIS

In view of the imperfect construction of power transmission line visualization system, this paper studies the power transmission line visualization system based on open source GIS engine openlayers. At the same time, it makes full use of VUE, JavaScript front-end technology and express framework, Node.js, MariaDB, GeoServer and other server technologies to realize the two-dimensional GIS solution based on open source openlayers, which has a certain application value for two-dimensional GIS visualization management in the field of transmission lines. This scheme can meet the daily application needs of power grid transmission lines, and greatly facilitates the management of power grid transmission lines.

Stability Analysis of High-Frequency Interactions Between a Converter and HVDC Grid Resonances

This paper analyzes high-frequency interactions between a Modular Multilevel Converter (MMC) and High Voltage Direct Current (HVDC) grid resonances by studying their effect on the system stability. The recent appearance of converter-related instabilities due to high-frequency oscillations at the converter's ac side raises concerns about whether similar interactions can also take place at its dc side. To determine the risks imposed by such interactions within an HVDC grid, this paper assesses the impact of the MMC internal dynamics and dc system resonances on the stability using an analytical impedance-based method. The effect of fault current-limiting inductors, grid topology changes and transmission line length is investigated, indicating that these parameters considerably influence the electromagnetic characteristics of the HVDC grid and consequently the system stability. Furthermore, a sensitivity analysis of the MMC internal controller dynamics on the converter's non-passivity, causing the instability, is performed.

Analysis of Bird Damage Prevention Mechanism for Overhead Transmission Lines

With the continuous development of the power system, 500kV transmission lines have become the backbone of power grid transmission lines. The construction of a large number of transmission lines is intertwined with the activities of birds. Line failures caused by birds have become the main cause of power system trip accidents. First, it seriously threatens the safe operation of the power system, and its degree of damage is second only to lightning strikes and external damage. Through related research and analysis, the bird damage of 500kV transmission line is mainly caused by three aspects: bird droppings, bird body and bird pecking. This paper analyzes the formation mechanism of bird damage on 500kV transmission lines, and provides some guidance for the corresponding prevention measures for the protection of transmission lines from bird damage.