Electrical Inspection Research Articles

Target detection of helicopter electric power inspection based on the feature embedding convolution model.

This study aims to improve the helicopter electric power inspection process by using the feature embedding convolution (FEC) model to solve the problems of small scope and poor real-time inspection. First, simulation experiments and model analysis determine the keyframe and flight trajectory. Second, an improved FEC model is proposed, extracting features from aerial images in large ranges in real time and accurately identifying and classifying electric power inspection targets. In the simulation experiment, the accuracy of the model in electric power circuit and equipment detection is improved by 30% compared with the traditional algorithm, and the inspection range is expanded by 26%. In addition, this study further optimizes the model with reinforcement learning technology, conducts a comparative analysis of different flight environments and facilities, and reveals the diversity and complexity of inspection objectives. The performance of the optimized model in fault detection is increased by more than 36%. In conclusion, the proposed model improves the accuracy and scope of inspection, provides a more scientific strategy for electric power inspection, and ensures inspection efficiency.

Analysis of user acceptance of information and communications technology for electrical safety inspection based on a choice experiment and hierarchical Bayesian model

The development of information and communications technology has resulted in changes in electrical safety inspection. Electrical safety inspectors plan to introduce smart devices that detect causes of extensive electrical fires in real time without problems of power disconnections and on-site visits. However, smart inspection is expected to encounter acceptance issues because users are unaccustomed to it and obligated to pay additional costs for device installation and data communications. This study aims to investigate smart inspection acceptance using survey data. A hierarchical Bayesian model is employed to explore the effects of users' characteristics on its acceptance. The respondents prefer attributes of smart inspection to those of the prevailing method of on-site inspection, excluding monthly inspection costs. They prefer more frequent and extensive inspections, and prefer to avoid power disconnections and physical interaction. Accordingly, the government should inform users that smart inspection is convenient and accurate. It is also attractive to users who wish to avoid on-site visits because of privacy issues. The acceptance rate can increase if the government reduces inspection costs using the existing smart devices and communications infrastructure, and offers real-time electrical safety information using smart phones and in-home displays.

Domain-Specific Few-Shot Table Prompt Question Answering via Contrastive Exemplar Selection

As a crucial task in natural language processing, table question answering has garnered significant attention from both the academic and industrial communities. It enables intelligent querying and question answering over structured data by translating natural language into corresponding SQL statements. Recently, there have been notable advancements in the general domain table question answering task, achieved through prompt learning with large language models. However, in specific domains, where tables often have a higher number of columns and questions tend to be more complex, large language models are prone to generating invalid SQL or NoSQL statements. To address the above issue, this paper proposes a novel few-shot table prompt question answering approach. Specifically, we design a prompt template construction strategy for structured SQL generation. It utilizes prompt templates to restructure the input for each test data and standardizes the model output, which can enhance the integrity and validity of generated SQL. Furthermore, this paper introduces a contrastive exemplar selection approach based on the question patterns and formats in domain-specific contexts. This enables the model to quickly retrieve the relevant exemplars and learn characteristics about given question. Experimental results on the two datasets in the domains of electric energy and structural inspection show that the proposed approach outperforms the baseline models across all comparison settings.

Design of intelligent controller for obstacle avoidance and navigation of electric patrol mobile robot based on PLC

Currently, the obstacle avoidance control of patrol robots based on intelligent vision lacks professional controller module assistance. Therefore, this paper proposes a design method of intelligent controller for obstacle avoidance and navigation of electrical inspection mobile robot based on PLC control. The controller designs a laser range finder to determine the required position of electrical patrol inspection. Use PLC as the core controller, and combine sensors, actuators, communication module and PLC selection module in the process of hardware design to achieve autonomous navigation and obstacle avoidance functions of the robot. Then design the software including the PLC compiler system and the virtual machine module. Based on the above steps, design the control module of obstacle avoidance navigation, which realizes the key link of robot autonomous navigation. The test results show that the controller can successfully avoid obstacles, improve the efficiency and quality of inspection, and achieve accurate and fast obstacle avoidance navigation for the electrical inspection mobile robot.

Analysis and denoising of electric noise in phased array ultrasonic testing system

Phased array ultrasonic testing (PAUT) is an advanced technique used for non-destructive testing (NDT) to detect and assess the integrity of materials or structures. In composite materials with significant acoustic attenuation, electrical noise significantly impacts PAUT detection accuracy, highlighting a crucial issue. This paper presents a comprehensive analysis of electrical noise, explains the relationship between electrical noise and inspection parameters, and proposes recommendations to reduce it. A time-varying filter empirical mode decomposition (TVF-EMD) joint wavelet thresholding method is proposed for denoising electrical noise after time-corrected gain (TCG). The ultrasonic echo signal goes through TVF-EMD decomposition, resulting in intrinsic mode functions (IMFs). Energy entropy is utilized to identify the signal-dominant IMFs, and wavelet thresholding is applied to further reduce noise within these selected IMFs. The proposed method effectively removes noise and ensures signal integrity by validating simulated and experimental signals.

Failures and performance of different aged PV modules operated under northern Algerian climate conditions: Analysis, assessment, and recommended solutions

This paper analyzes, assesses, and compares the failures and performance of various PV module types deployed at six PV installations located in different regions of northern Algeria, under Mediterranean climatic conditions. Qualitative data has been considered by selecting the best and worst-performing PV module samples for each of the six PV module types, along with other PV samples exhibiting specific permanent failures. Subsequently, the following IEC 61215 inspections were conducted: visual failures inspection, electrical performance inspection, thermal failures inspection, and insulation failures inspection. The results of these failure inspections indicate that different types of PV modules exhibit varying performance. Interestingly, the disparities are more pronounced between the PV module types than among the PV module samples within the same PV module type. This gap is at least ten times higher in terms of yearly degradation rates. However, some of the PV module types with high failure occurrence, severity, and exposure duration exhibit high performance compared to other types that have low failure occurrence, severity, and exposure duration. Meanwhile, there is no obvious and clear correlation between the different PV inspections, especially for PV module types. Thus, our analysis of the obtained results leads us to conclude that the significant disparity in failures and performance among the tested PV module types is primarily attributable to differences in their quality rather than the influence of local climate conditions or working conditions. Out of the six PV module types, only one will meet the 25-year warranty provided by the manufacturer, providing valuable insights into the prevalence of poor-quality PV modules in PV installations and those available in Algeria. Finally, a list of recommended solutions has been proposed to the Algerian government and other countries facing similar challenges to integrate into their national infrastructures and frameworks (quality, technical, qualification, and policies). These solutions aim to ensure the high quality, performance, and long lifespan of PV modules and PV installations.

Segmentation and Tracking Based on Equalized Memory Matching Network and Its Application in Electric Substation Inspection

With the wide application of deep learning, power inspection technology has made great progress. However, substation inspection videos often present challenges such as complex backgrounds, uneven lighting distribution, variations in the appearance of power equipment targets, and occlusions, which increase the difficulty of object segmentation and tracking, thereby adversely affecting the accuracy and reliability of power equipment condition monitoring. In this paper, a pixel-level equalized memory matching network (PEMMN) for power intelligent inspection segmentation and tracking is proposed. Firstly, an equalized memory matching network is designed to collect historical information about the target using a memory bank, in which a pixel-level equalized matching method is used to ensure that the reference frame information can be transferred to the current frame reliably, guiding the segmentation tracker to focus on the most informative region in the current frame. Then, to prevent memory explosion and the accumulation of segmentation template errors, a mask quality evaluation module is introduced to obtain the confidence level of the current segmentation result so as to selectively store the frames with high segmentation quality to ensure the reliability of the memory update. Finally, the synthetic feature map generated by the PEMMN and the mask quality assessment strategy are unified into the segmentation tracking framework to achieve accurate segmentation and robust tracking. Experimental results show that the method performs excellently on real substation inspection scenarios and three generalized datasets and has high practical value.

ELECTRICAL TRANSMISSION LINE INSPECTION ROBOT

This paper reviews the use of Electrical Transmission Line Inspection Robots (ETLIRs) from to , emphasizing their importance in detecting faults early and ensuring efficient power transmission. It explores climbing, flying, and hybrid (climbing-flying) robots, highlighting their structures, operations, and limitations. While climbing robots offer proximity for reliable data, challenges like landing and obstacle avoidance persist. Hybrid robots show promise by combining climbing and flying capabilities. The paper also discusses challenges like power supply, obstacle detection, and control systems, and suggests future research directions such as enhancing battery capacity, improving fault detection, addressing electromagnetic shielding, implementing de-icing mechanisms, and developing advanced control techniques for wind disturbances.

Hybrid non-technical-loss detection in fog-enabled smart grids

Electricity theft is one of the major factors contributing to non-technical-losses (NTLs) in power distribution networks. NTL fraud includes frauds in which consumers profit unlawfully by manipulating smart meters (SMs), intruding networks, and so forth. This unlawful act not only undermines people’s efforts to conserve energy but also disrupts the regular billing cycle for power utilities, causing financial losses. In order to help utility companies solve the problems of inefficient electricity inspection and irregular power consumption, two NTL Detection schemes are proposed for NTL fraud prediction. Both schemes employed the auto-regressive integrated moving average (ARIMA) and the machine learning technique to predict the consumer behavior fraud pattern efficiently. Furthermore, extensive simulations are conducted on real-world electricity consumption data sets, which show that the proposed schemes outperformed state-of-the-art solutions and achieved an accuracy of 98%, a precision of 98.6%, a recall of 98.2%, an AUC of 97.9%, and an F1 score of 98.4%.

Optimizing 2D path planning for unmanned aerial vehicle inspection of electric transmission lines

The inspection of electric power transmission lines using unmanned aerial vehicles represents an innovative maintenance approach that several countries, including Morocco, are considering adopting. The primary aim is to enhance existing procedures, capitalize on their advantages, and address challenges associated with traditional methods. Given the precision, efficiency, and safety required for this application to ensure reliable results, we propose a functional architecture for unmanned aerial vehicle-based electric power transmission line inspection. This architecture encompasses key phases such as data collection, planning, and inspection, while identifying electromagnetic interferences and minimum approach distances as major constraints. To streamline mission planning, we introduce a trajectory optimization algorithm integrated into a mission planning assistance application for transmission line inspection. This algorithm presents a novel approach by hybridizing three algorithms: genetic algorithm, ant colony optimization and particle swarm optimization. Consequently, our work represents a significant contribution to the integration of unmanned aerial vehicles in electric power transmission lines inspection operations for electrical energy transport, facilitating potential advancements and discoveries in the field.

LSNetv2: Improving weakly supervised power line detection with bipartite matching

This paper addresses the crucial task of power line detection and localization in electrical infrastructure inspection using Unmanned Aerial Vehicles (UAVs) from weak supervision, polyline annotations. We first identify several limitations in the state-of-the-art approach LSNet. In particular, the inability of LSNet to detect line-crossings and lines in close proximity. To overcome these limitations, we propose LSNetv2, which enhances LSNet with multi-line segment detection capability facilitated via a bipartite matching loss. Additionally, we update LSNet’s regression loss in order to stabilize training by reducing the interdependence between predicted coordinates. Finally, LSNetv2 makes use of an increased receptive field to extract global information, improving overall detection performance. Through extensive evaluations on various power line detection datasets, LSNetv2 demonstrates superior performance and robustness. On the public datasets PLDU, PLDM and TTPLA, it achieved Fβ scores of 0.857, 0.875, and 0.671, respectively, while using only modified weak polyline annotation, establishing itself as an effective and efficient solution for power line detection in UAV-based electrical infrastructure inspections.

Thermal fault diagnosis of complex electrical equipment based on infrared image recognition

This paper realizes infrared image denoising, recognition, and semantic segmentation for complex electrical equipment and proposes a thermal fault diagnosis method that incorporates temperature differences. We introduce a deformable convolution module into the Denoising Convolutional Neural Network (DeDn-CNN) and propose an image denoising algorithm based on this improved network. By replacing Gaussian wrap-around filtering with anisotropic diffusion filtering, we suggest an image enhancement algorithm that employs Weighted Guided Filtering (WGF) with an enhanced Single-Scale Retinex (Ani-SSR) technique to prevent strong edge halos. Furthermore, we propose a refined detection algorithm for electrical equipment that builds upon an improved RetinaNet. This algorithm incorporates a rotating rectangular frame and an attention module, addressing the challenge of precise detection in scenarios where electrical equipment is densely arranged or tilted. We also introduce a thermal fault diagnosis approach that combines temperature differences with DeeplabV3 + semantic segmentation. The improved RetinaNet's recognition results are fed into the DeeplabV3 + model to further segment structures prone to thermal faults. The accuracy of component recognition in this paper achieved 87.23%, 86.54%, and 90.91%, with respective false alarm rates of 7.50%, 8.20%, and 7.89%. We propose a comprehensive method spanning from preprocessing through target recognition to thermal fault diagnosis for infrared images of complex electrical equipment, providing practical insights and robust solutions for future automation of electrical equipment inspections.

Application of indoor and outdoor integrated collaborative positioning technology in power transportation detection

With the continuous acceleration of the construction of electric power Internet of Things, the structure and scale of electric power system is more massive, the complexity and danger coefficient of field operation is enhanced, and the risk of causing safety accidents is further increased. Therefore, based on wireless sensor network, collecting and transmitting information of personnel and site operation environment, establishing power operation tracking system is of great significance to improve the safety and efficiency of operation and inspection personnel. At present, most of the electric power operation and maintenance personnel are positioned by satellite or wireless WIFI, which has a large error or even cannot be positioned, so this paper proposes a 5G+Beidou fusion indoor-outdoor cooperative positioning technology for electric power operation and inspection service. The fusion model of Beidou carrier phase differential positioning and 5G cellular network positioning is established by using the improved Federal Kalman filter algorithm, which can avoid data pollution caused by Beidou receiver failure or 5G communication module failure in a complex environment, and avoid the inability to locate compared with the common Federal Kalman filter. phenomenon.

Identification Method of Unmanned Aerial Vehicle Graphical Control Strategy Based on Cloud Server

With the rapid development of unmanned aerial vehicle (UAV) technology, UAV has been widely used in agricultural plant protection, electric power inspection, security patrols, and other fields. However, the control system of the UAV is a complex human–computer interaction system, which requires higher requirements in practical applications. Due to differences in hardware design, software development, and other aspects among different manufacturers, these UAV control systems require high hardware requirements, resulting in a long development cycle. At the same time, in practical applications, due to various reasons, there are equipment failures that are difficult to detect and eliminate in a timely manner. This paper used the UAV graphical control strategy identification method based on cloud server technology, and used the support vector machine (SVM) algorithm to analyze its identification accuracy. The research results showed that when other conditions were the same, the number of researchers and experts who were satisfied with the drone trial effect of the cloud server was 42 and 10, respectively, accounting for 84% and 100%. It indicates that they believe that the cloud server can effectively improve the effectiveness of the drone graphical control strategy recognition method, indicating a positive relationship between the two.

Retracted: A Novel Framework for Automation Technology Based on Machine Vision and Robotics in Electrical Power Inspection Processing.

[This retracts the article DOI: 10.1155/2022/9158936.].

Real-time performance evaluation and optimization of electrical substation equipment inspection algorithm based on distributed computing

Abstract Substation equipment inspection faces problems such as large amounts of data, various information types, complex analysis and calculations, and low resource integration. Distributed computing system uses networks to realize computer connectivity and collaboration, achieving in-depth sharing of computing resources, software resources, information resources, and communication resources. Therefore, firstly, this article introduces segmentation method for load balancing and recommendation calculation based on path search; secondly, an algorithm model for substation equipment inspection based on distributed computing is constructed; then, the article analyzes and discusses multi-core cluster task allocation, heterogeneous task scheduling framework, fragmented transmission mechanism, and vertical expansion mechanism; finally, case analysis and performance evaluation are carried out. Distributed computing has significant advantages in the inspection algorithm of power substation equipment. It can efficiently process large-scale data, improve computing power, ensure data redundancy and fault tolerance, provide good scalability, optimize resource utilization, and support remote data processing. By integrating multi-source data, distributed computing improves the accuracy and comprehensiveness of the algorithm, enhances security, supports complex algorithms such as machine learning and deep learning, and realizes real-time monitoring and early warning. The results show that the proposed algorithm can improve the efficiency of distributed parallel computing and analysis of substation equipment inspections and can simulate, evaluate, and screen substation equipment failure scenarios that may cause serious consequences within an effective time.

Advancements in Electric Vehicle PCB Inspection: Application of Multi-Scale CBAM, Partial Convolution, and NWD Loss in YOLOv5

In the rapidly evolving electric vehicle industry, the reliability of electronic systems is critical to ensuring vehicle safety and performance. Printed circuit boards (PCBs), serving as a cornerstone in these systems, necessitate efficient and accurate surface defect detection. Traditional PCB surface defect detection methods, like basic image processing and manual inspection, are inefficient and error-prone, especially for complex, minute, or irregular defects. Addressing this issue, this study introduces a technology based on the YOLOv5 network structure. By integrating the Convolutional Block Attention Module (CBAM), the model’s capability in recognizing intricate and small defects is enhanced. Further, partial convolution (PConv) replaces traditional convolution for more effective spatial feature extraction and reduced redundant computation. In the network’s final stage, multi-scale defect detection is implemented. Additionally, the normalized Wasserstein distance (NWD) loss function is introduced, considering relationships between different categories, thereby effectively solving class imbalance and multi-scale defect detection issues. Training and validation on a public PCB dataset showed the model’s superior detection accuracy and reduced false detection rate compared to traditional methods. Real-time monitoring results confirm the model’s ability to accurately detect various types and sizes of PCB surface defects, satisfying the real-time detection needs of electric vehicle production lines and providing crucial technical support for electric vehicle reliability.

Lightweight Detection Methods for Insulator Self-Explosion Defects.

The accurate and efficient detection of defective insulators is an essential prerequisite for ensuring the safety of the power grid in the new generation of intelligent electrical system inspections. Currently, traditional object detection algorithms for detecting defective insulators in images face issues such as excessive parameter size, low accuracy, and slow detection speed. To address the aforementioned issues, this article proposes an insulator defect detection model based on the lightweight Faster R-CNN (Faster Region-based Convolutional Network) model (Faster R-CNN-tiny). First, the Faster R-CNN model's backbone network is turned into a lightweight version of it by substituting EfficientNet for ResNet (Residual Network), greatly decreasing the model parameters while increasing its detection accuracy. The second step is to employ a feature pyramid to build feature maps with various resolutions for feature fusion, which enables the detection of objects at various scales. In addition, replacing ordinary convolutions in the network model with more efficient depth-wise separable convolutions increases detection speed while slightly reducing network detection accuracy. Transfer learning is introduced, and a training method involving freezing and unfreezing the model is employed to enhance the network's ability to detect small target defects. The proposed model is validated using the insulator self-exploding defect dataset. The experimental results show that Faster R-CNN-tiny significantly outperforms the Faster R-CNN (ResNet) model in terms of mean average precision (mAP), frames per second (FPS), and number of parameters.

Radiometric calibration and measurement algorithm for electrical inspection solar-blind ultraviolet cameras

Radiometric calibration and measurement algorithm for electrical inspection solar-blind ultraviolet cameras

인천 서구 탱크로리 폭발사고로 본 과산화수소 분해반응에 대한 위험성에 관한 연구

In 2020, a significant accident occurred at a chemical plant in Seo-gu, Incheon, involving a tank explosion during the recovery of a mixture of hydrogen peroxide and sodium hydroxide in a tank lorry. This accident caused multiple casualties and extensive property damage. This study investigated the cause of the chemical explosion through a reproduction experiment (reactivity and temperature change) and a component analysis of the field samples. The results revealed a runaway reaction approximately 17 minutes after mixing hydrogen peroxide and sodium hydroxide, resulting in a temperature increase to 85 °C. Component analysis using NMR (Nuclear Magnetic Resonance) and pH analysis confirmed the similarity between the components of the product after the chemical reaction and the sample collected on site. Mechanical and electrical inspections of the exploded tank lorry exhibited a typical brittle fracture shape in the broken steel plate, indicating a structural failure. Electrical assessment ruled out the possibility of explosion due to static electricity and electrical singularities. We infer that the tank lorry explosion occurred due to the accelerated decomposition reaction caused by the positive catalytic action of sodium hydroxide mixed with hydrogen peroxide, which rapidly increased pressure within the tank.