Power Line Research Articles

Development of a Low-Cost Automated Demand Response Controller for Home Energy Management

This research focuses on developing a low-cost automated demand response controller (DRC) with OpenADR 2.0a capability to enable existing infrared-controlled (IR-controlled) air conditioners (ACs) in homes and buildings to participate in automated demand response programs (ADRPs). The DRC consists of four modules: a smart socket module, an infrared module, a temperature sensor, and a voltage/current module. It can receive, analyze, and respond to demand response (DR) events and perform necessary demand and energy control strategies via IR. Power line communication (PLC) is used for communication without additional wiring. The system is tested under two conditions: participating in ADRPs via OpenADR and not participating in ADRPs. An 8.8% load reduction is observed with different temperature settings when not participating in ADRPs, and energy reductions of 21% to 46% are achieved using various cooling/fanning duty cycles in ADRPs. The proposed system can be integrated with any DR algorithm to meet demand management requirements under the OpenADR program, contributing to significant demand reductions.

Image processing‐based noise‐resilient insulator defect detection using YOLOv8x

AbstractAccurate and efficient insulator defect detection is critical for power grid reliability, but it can be affected by the presence of noises in captured images and can be difficult to employ for real‐time operation due to the slow processing of the detection scheme. This paper proposes a novel framework based on the YOLOv8x object detection scheme, addressing the challenge of detecting small defects in complex aerial images and providing a noise mitigation scheme. A Gaussian blur and Laplacian sharpening‐based hybrid scheme is proposed to mitigate the impacts of noises in insulator images. Experimental results indicate that the proposed framework can achieve a mean average precision (mAP) of 98.4% on noise‐free images, surpassing benchmark models, such as YOLOv5x and YOLOv7 by 2.1% and 3.9%, respectively. Also, while the performance of a conventional system can decrease to a mAP of 93.3% in the worst case, the implementation of the proposed mitigation scheme ensures a mAP of 96.7% for that case. With an inference speed of 56.9 ms per image, this approach offers a promising solution for real‐time power line inspection, contributing to enhanced power grid maintenance and safety.

Axial-UNet++ Power Line Detection Network Based on Gated Axial Attention Mechanism

The segmentation and recognition of power lines are crucial for the UAV-based inspection of overhead power lines. To address the issues of class imbalance, low sample quantity, and long-range dependency in images, a specialized semantic segmentation network for power line segmentation called Axial-UNet++ is proposed. Firstly, to tackle the issue of long-range dependencies in images and low sample quantity, a gated axial attention mechanism is introduced to expand the receptive field and improve the capture of relative positional biases in small datasets, thereby proposing a novel feature extraction module termed axial-channel local normalization module. Secondly, to address the imbalance in training samples, a new loss function is developed by combining traditional binary cross-entropy loss with focal loss, enhancing the precision of image semantic segmentation. Lastly, ablation and comparative experiments on the PLDU and Mendeley datasets demonstrate that the proposed model achieves 54.7% IoU and 80.1% recall on the PLDU dataset, and 79.3% IoU and 93.1% recall on the Mendeley dataset, outperforming other listed models. Additionally, robustness experiments show the adaptability of the Axial-UNet++ model under extreme conditions and the augmented image dataset used in this study has been open sourced.

A Biomimetic Pose Estimation and Target Perception Strategy for Transmission Line Maintenance UAVs

High-voltage overhead power lines serve as the carrier of power transmission and are crucial to the stable operation of the power system. Therefore, it is particularly important to detect and remove foreign objects attached to transmission lines, as soon as possible. In this context, the widespread promotion and application of smart robots in the power industry can help address the increasingly complex challenges faced by the industry and ensure the efficient, economical, and safe operation of the power grid system. This article proposes a bionic-based UAV pose estimation and target perception strategy, which aims to address the lack of pattern recognition and automatic tracking capabilities of traditional power line inspection UAVs, as well as the poor robustness of visual odometry. Compared with the existing UAV environmental perception solutions, the bionic target perception algorithm proposed in this article can efficiently extract point and line features from infrared images and realize the target detection and automatic tracking function of small multi-rotor drones in the power line scenario, with low power consumption.

Induced geoelectric field: influence of polarization in coast effect and proximity effect

During a magnetic storm, the induced geoelectric field drives induced currents that couple with artificial metallic networks such as power lines, pipelines, telecommunication cables and railway lines with the consequent risk, in case of strong events, of possible blackouts, damages and impairments of those systems. It is known that magnetic storms are global phenomena and their effects in specific regions with specific ground conductivity may be different; in particular, when a magnetic storm involves regions composed of two adjacent media characterized by very large difference of conductivity between them (e. g. sea and land) the induced geoelectric field is largely influenced by this conductivity contrast and depends, primarily, on its orientation with respect to the discontinuity line. By representing the source as an incident plane wave normally directed with respect to the air-land/sea interface, two kinds of polarizations are usually considered in literature: H/E polarization i.e. with the incident magnetic/electric field parallel to the discontinuity. In this paper, we wish to study a more general case i.e. when the source is characterized by an electric field forming a generic angle with the discontinuity line so that the resulting field is a combination of both H and E polarizations; to do this, and by adopting a 2D approach based on the generalized thin sheet model, the paper combines, published formulas for H polarization while new approximated analytic formulas for E polarization are derived. Moreover, we express the induced geoelectric field as a function of a generic direction which represents the layout orientation of a potentially affected plant (power line, pipeline…); this is important, because that is the first step necessary to evaluate the level of geomagnetically induced currents (GIC) in the plant itself. In conclusion, the novelty of this approach is that the resulting induced geoelectric field on the Earth’s surface can be expressed using only analytical formulas; a further advantage of the proposed approach to the problem is that it is quite simple and particularly suitable for the evaluation of GIC in terrestrial conductor networks.

Investigation of the Effect of Diverse Dictionaries and Sparse Decomposition Techniques for Power Quality Disturbances

The quality of power signals is strongly influenced by nonlinear loads in Electrical Power systems. Representation of electrical signals using different Sparse techniques is an interesting area of research as it moderates the volume of data to be stored. The storage of signals in Sparse form will make data storage easier and more efficient. Earlier studies concentrated on blindly choosing Overcomplete Hybrid Dictionaries (OHDs) for Sparse representation. The effect of different dictionaries in representing electrical signals has also not been reviewed in them. This paper presents an investigation of the effect of various dictionaries and the sparsity constant on the representation of electrical signals. The validation for statements presented in this paper is carried out by representing power signals with diverse power line disturbances like Swell, DC offset, and random oscillation, with the help of various dictionaries in the simulation platform. The Sparse representation of the power signals was generated using the Orthogonal Matching Pursuit algorithm. The resultant Sparse representation was then compared with the original signal. The difference between them was found to be negligible with the help of different metrics. The ratio of the obtained signal from Sparse representation, the original signal (A/R ratio), and the Mean Squared Error were taken as the metrics. The MATLAB platform was used for performing the simulation study.

A Novel Synchronization Strategy for Distributed Energy Resources in Weak Grids Using Remote Strong Grid Sensing

This paper proposes a novel strategy for the current injection-based control of distributed energy resources connected to weak grids via a voltage source converter. The current injection controller is no longer synchronized with the point of common coupling but with the strong grid point voltage. The strong grid synchronization control strategy improves the output dynamics of the voltage source converter and recovery after faults in weak grids. The phase difference between the voltage source converter and the strong grid voltages caused by the long power lines does not affect the power control. Furthermore, a time delay-compensation method is proposed which tolerates the communication time delay introduced by the transmission of the synchronization signal from the strong grid point. The performance of the proposed control strategy is verified in detail using MATLAB 2023b simulations and real-time digital simulations on a medium voltage model and also validated in an experiment on a low-voltage grid-feeding inverter setup.

Management and quality assurance of observational data recorded by geomagnetic observatories in Egypt

The National Research Institute of Astronomy and Geophysics (NRIAG) is an Egyptian research institution that operates two Geomagnetic Observatories (GMOs), namely the Misallat (MLT), and Abu‑Simbel (ABS) observatories. Data availability, reliability and quality are the basis for any scientific research. So, it is essential to check and manage the quality of Geomagnetic Data (GMD) and prepare them in a proper Data Format (DF) before publishing them for research studies. This paper focuses on the management and quality assurance of the GMD recorded by the MLT and ABS observatories, and presents the current efforts deployed for managing, examining and processing them as well as converting their original DF into international DF. The raw data has been processed mathematically using MATLAB scripts. Results show that GMD from the MLT and ABS observatories suffer from various non‑natural disturbances. Electric power lines, industrial and human activities nearby the MLT observatory as well as some internal problems with the measuring devices, might have caused these disturbances. In addition, the study examined the impact of these disturbances on the quality of recorded data. The comparison of raw data from the two observatories and other INTERMAGNET data showed a good correlation between them before 2015. From 2015 until October 2022, the noise level mostly at the MLT became very high and the natural Ultra Low Frequency (ULF) emissions at the MLT data were completely hidden by the background noise. The study recommends that the Magnetically Clean Environment (MCE) must be resorted again as well as fixing the measuring instruments especially at the MLT observatory to provide high quality data that can be suitable for all aspects of scientific research and satisfy the INTERMAGNET requirements.

Dataset for Antenna-Based Detection of Fault Types in Covered Conductors for 22 kV Voltage Power Lines

This abstract presents a dataset for the detection of fault types in XLPE-covered conductors utilized in 22 kV medium voltage power distribution systems. We employed an antenna-based approach for detecting partial discharges. The dataset encompasses 12 distinct fault categories, ranging from ground phase faults to inter-phase faults, and no-fault case with steel or covered conductor as fault. We also used three different antennas. Each sample is a single measurement from antenna, consisting of 106 data points as floating numbers. The utilization of the antenna-based method offers the potential for a more cost-effective and straightforward installation for the detection of partial discharges. The objective of dataset is to enhance the identification of fault types, thereby promoting broader adoption of covered conductors in overhead power distribution lines. Such adoption proves particularly beneficial in confined areas, including natural parks, where safety is a prime concern. It is noteworthy that this dataset represents an original contribution, as no prior publication has addressed detection for this specific range of fault types and method of detection.

Matching Trees to Streets by Street Type: A Case Study of Street Tree Suitability and Services in a Highly Urbanized City

Street trees are a representative form of urban green space that play an important role in mitigating the environmental impact of urbanization. Planting the right tree in the right place in urban streetscapes can improve tree health and ecosystem services. Here, we propose a novel approach to selecting appropriate street trees using street type classifications. In the highly urbanized area of Uijeongbu City, South Korea, 221.9 km of streets with 19,717 street trees were classified into 12 types based on road width, aspect ratio, land use, and the presence of power lines. Appropriate tree species were selected for each street type, taking into account tree traits and functions as well as street environments. Then, we analyzed the structure and ecosystem-regulating services of street trees by type, also comparing the services of appropriate and non-appropriate trees. As a result, all 12 street types were identified, but their distribution was uneven. Tree dimension was the key factor in determining appropriate species, and, for the second most common street type, characterized by narrow roads, low aspect ratios, and power lines, only four appropriate species were identified, indicating an urgent need for more options. Additionally, the most dominant species accounted for over 20%, averaging 44% across the 12 street types, further highlighting the necessity of introducing more diverse tree species. Overall, appropriate street trees generally provided higher service efficiency compared to non-appropriate trees across four ecosystem regulating services. These findings emphasize the need for policies and guidelines that promote street tree diversity and enhance the ecological benefits of street trees. This study provides a foundation for developing sustainable street tree management strategies that contribute to healthier and more resilient urban streetscapes.

The propagation of power line communications using the S-matrix approach

In this paper, we are interested in the above-mentioned problem by emphasizing the notions of transfer function and especially of power transmitted by HF signals. This work aims at an analysis of the propagation of PLC on power transmission lines and cables as well as on the distribution network. The objective finds its interest in the need for a telecontrol system specific to the power transmission network requiring a greater amount of information to broaden its scope and improve its performance. In this study, we are interested in the propagation of CPL (power line communication), and we deal with modeling HF signals in the electrical network. We present the mathematical elements for representing a tube (multi-wire line or cable) by matrix S. This will then be followed by a presentation on the S-matrix topological approach that we will use to model HF propagation in a multi-wire network (radial or mesh).

Investigation and application of big data and AI-Powered provincial power grid accurate planning support platform

ABSTRACT This study presents an accurate planning assistance platform for big data and artificial intelligence-based provincial power grids. The platform greatly increases the efficiency of data integration and management by building a planning big database containing fundamental geographic information, thematic data, remote sensing image data, and planning business data. Technically speaking, this paper addresses the issues of accuracy and speed in the processing of remote sensing image data by using deep learning algorithms like convolutional neural networks (CNN) and full convolutional neural networks (FCN) to achieve automatic classification of high-resolution optical remote sensing images and residential area extraction. In the meantime, the intelligent routing approach, which is based on cost surface modelling and multi-criteria decision-making, optimises the routing of power lines by carefully taking into account a wide range of variables, including land use, accessibility for vehicles, and environmental impact. In addition to resolving the primary issues with present power grid planning, this study’s exact planning support platform for provincial power grids greatly enhances real-time, accurate, and scientific planning.

Fifty-hertz magnetic fields induce DNA damage through activating mPTP associated mitochondrial permeability transition in senescent human fetal lung fibroblasts

With the rapid development and using of electromagnetic technology, artificial electromagnetic fields (EMFs) have become an emerging environmental factor in our daily life. Extremely-low-frequency (ELF) magnetic fields (MFs), generally generated by power lines and various electric equipment, is one of the most common EMFs in the environment which were concerned for the potential impact on human health. Base on limited evidence, ELF-MFs have been classified as possible carcinogen to human by International Agency for Research on Cancer (IARC), but the mechanisms have not been fully elucidated. Senescent cells are a group of special cells, characterized by cell cycle arrest, senescence-associated secretory phenotype (SASP), accumulation of macromolecular damage, and metabolic disturbance, play important role in fetal development, tissue aging, and even carcinogenesis. Thus, EMFs may promote carcinogenesis by affecting senescent cells, however, there are few studies. In this study, we found that exposure to 50 Hz MFs at 1.0 mT for 24 h could induce significant DNA damage in senescent but not non-senescent human fetal lung fibroblast suggested that senescent cells are more sensitive to 50 Hz MFs on DNA damage, and further results revealed that reactive oxygen species (ROS) generation mediated by mitochondrial permeability transition pore (mPTP) activation play critical role in this process. Our results indicated that cellular senescence can lead to cell sensitivity to the DNA damage effect of 50 Hz MFs, however, whether this play important role in mediating the carcinogenesis of EMFs await further study.

Optimal Configuration for Shared Electric-hydrogen Energy Storage for Multiple Integrated Energy Systems With Mobile Hydrogen Transportation

The flexible operation and storage of hydrogen and electric energy provide an effective path for the development of low-carbon energy and transportation systems. This paper introduces a configuration method for electric-hydrogen shared energy storage supporting the multiple energy and capacity demands of integrated energy systems (IESs). A Stackelberg game-based shared energy framework with gaseous hydrogen transportation by transportation network for shared energy storage operators (SESO) and IESs is established. In this framework, the electric power and electric storage sharing are accomplished by power lines, while the shared hydrogen is achieved by the transportation of mobile trucks. The target of the energy storage operator is to maximize its trading benefits with IESs and lower the life-cycle cost of the whole system, while the IESs aim to meet their energy demand and lower the operation cost via energy storage sharing. To solve this planning model, a two-level sine cosine based grey wolf optimizer (GWO-SCA)-bisectional method is provided to realize the system configuration, mobile hydrogen planning, and economic trading of the shared energy operator. A case study with 3 IESs, real-world geographic roads, and environmental conditions is carried out to verify the effectiveness of the method and the life-cycle configuration and operation economy of the shared energy storage. The comparisons show that the proposed energy storage sharing frame can achieve a higher energy utilization ratio of 92% and the proposed method can solve the two-level problem more efficiently, the calculation time is reduced by 80.2%.

Calculation of heat loss for choosing a heating system in the chamber

Abstract The work studied the features of the chamber (linear dimensions, wall materials, etc.). It was revealed that the smaller the temperature difference between the environment and the air temperature inside the chamber, the lower the percentage of losses in the chamber. It has been established that due to the convective action, the hot air in the chamber predominantly rises upward. In this case, the heating elements were connected in series with each other. Power was supplied from a 220 V network through power lines. We have developed a system for monitoring the temperature inside the chamber and lamp operation in the Automation Studio 5.2 program. This system allows one to regulate the air temperature inside the chamber. Calculations were also carried out to identify the effect of temperature differences on heat loss in the chamber device. Experiments and engineering calculations have shown that in a small chamber the temperature required for seed germination cannot be achieved immediately. In the two - position closed chamber load plot, the energy consumption area is 9.4% larger than the energy consumption areas of the subsequent periodic energy consumption areas. It can be seen that heating the chamber itself requires additional electricity.

Enhancing Power Line Insulator Health Monitoring with a Hybrid Generative Adversarial Network and YOLO3 Solution

Enhancing Power Line Insulator Health Monitoring with a Hybrid Generative Adversarial Network and YOLO3 Solution

RPAS And Lidar Technologies: An Alternative For Monitoring Power Line

This study explores the use of Remotely Piloted Aircraft Systems (RPAS) equipped with Light Detection and Ranging (LiDAR) technology for monitoring high-voltage power lines. By integrating RPAS and LiDAR, the inspection process becomes more efficient, accurate, and safer compared to traditional methods. LiDAR's ability to capture detailed 3D data provides valuable insights into the condition of transmission line corridors, including vegetation encroachment and structural damage. The research highlights the potential of these technologies to reduce operational costs, improve data accuracy, and enhance the management of power grids, offering a sustainable solution for infrastructure monitoring in challenging environments

Active sensitivity coefficient-guided reinforcement learning for power grid real-time dispatching

The integration of renewable energy sources into power systems and the expansion of grid scale have introduced a higher degree of operational unpredictability, thereby increasing the risk of safety hazards such as transmission line overloads. Existing reinforcement learning algorithms that incorporate expert knowledge aim to facilitate secure dispatching of the power grid. However, these algorithms, which adopt strategies akin to imitation learning during the learning process, do not effectively address systemic constraints such as line limits and power balance. In response to this issue, this study introduces a novel active sensitivity coefficient-guided reinforcement learning method for real-time power grid dispatch (SRL-Dispatching). Firstly, a sensitivity-based reinforcement learning framework is proposed using the Soft Actor-Critic (SAC) algorithm. Secondly, a sensitivity coefficient-based method for compressing the action space boundary is proposed to address the security implications associated with transmission line overloads. Subsequently, a feasible domain projection approach is introduced to ensure that dispatch operations adhere to safety constraints. By employing sensitivity factors to guide the learning process of the agent in managing line overloads and ensuring safe operation, the precision and robustness of dispatch strategies in the power system environment are significantly enhanced. Simulation results using the SG-126 power grid simulator indicate that SRL-Dispatching accelerates training by a factor of 9.8 compared to state-of-the-art RL methods, with comparable decision-making times. Across various load levels, SRL-Dispatching demonstrates superior performance in terms of renewable energy integration, line overload management, and power balance control.

Comparative analysis of deep learning and traditional airborne electromagnetic data processing: A case study

State-of-the-art airborne electromagnetic (AEM) systems conduct large-scale surveys and produce thousands of line kilometers of data. These large volumes of AEM data are generally subjected to manual inspection for identifying and culling data corrupted by couplings from various sources, such as fences and power lines, as well as the assessment for late-time noise. Although effective, this traditional AEM data processing is subjective and time consuming. This study evaluates the efficacy of a recently proposed unsupervised deep-learning method for the automated processing of AEM data on a large-scale survey conducted in New Zealand. The automated system processes 6471 line kilometers of data in approximately 15 min — a task that could extend more than 5–16 days if done manually by a trained expert. Although manual processing is not the ultimate benchmark, both processing schemes have an agreement of approximately 88% for the low-moment data and approximately 96% for the high-moment data. The inversion results for manual and automated system processing also indicate comparable data fits and resistivity models. The automated system demonstrates strong generalization capabilities; however, it faces challenges when data affected by various sources, such as anthropogenic activities, mimic credible 1D responses. This underscores the need for manual inspection in contexts wherein it is necessary to distinguish between true anomalies and noise. This study not only validates the automated system’s applicability across diverse geologic settings but also emphasizes the importance of balancing automated and manual approaches for optimal AEM data processing.

Multiphysics Modeling of Power Transmission Line Failures Across Four US States

The failure of overhead transmission lines in the United States can lead to significant economic losses and widespread blackouts, affecting the lives of millions. This study focuses on analyzing the failure of transmission lines, specifically considering the effects of wind, ambient temperature, and current demands, incorporating minimal and significant pre-existing damage. We propose a multiphysics framework to analyze the transmission line failures across sensitive and affected states of the United States, integrating historical data on wind and ambient temperature. By combining numerical simulation with historical data analysis, our research assesses the impact of varying environmental conditions on the reliability of transmission lines. Our methodology begins with a deterministic approach to model temperature and damage evolution, using phase-field modeling for fatigue and damage coupled with electrical and thermal models. Later, we adopt the probability collocation method to investigate the stochastic behavior of the system, enhancing our understanding of uncertainties in model parameters, conducting sensitivity analysis to identify the most significant model parameters, and estimating the probability of failures over time. This approach allows for a comprehensive analysis of factors affecting transmission line reliability, contributing valuable insights into improving power line’s resilience against environmental conditions.