No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Vision-Based Analysis of Utility Poles Using Drones and Digital Twin Modeling in the Context of Power Distribution Infrastructure Systems
... Unit System SoS Aerospace Aircraft tire [38] Air rudder [39,40] Aero-engine bearing [41] Aircraft cabin [42] Hydraulic valve [43] Air bearing [44] Turbofan [45] N/A UAV [46][47][48] Spacecraft [49] Rocket [50] City Utility pole [51] Bridge [52][53][54] Historical architecture [31][32][33][34] Building [55][56][57][58][59] Campus [60] City [61][62][63][64] Healthcare Coronary heart vessels [65] Heart [66] Cardiovascular system [67] N/A F. Tao et al. analyzed according to its hierarchical structure, which can be further subdivided into human-oriented and medical resources-oriented hierarchies. ...
... However, the corresponding papers are far less frequent. For other fields, all modeling aspects are not thoroughly addressed, with the exception that they all shed light on model construction, e.g., model construction in the field of the city [51,55,60,61,110,111], and model construction in the chemical industry [35,36]. Simultaneously, the papers are attainable whose general methodologies are feasible for one and several modeling aspects within all application fields ( [112] for model fusion, [113][114][115][116][117] for model management, and [118] for model construction, verification, modification, and management). ...
... Point cloud is a dense collection of points collected by certain measurement means. The point cloud derived from laser scanning [31,32] and photographic scanning (Photogrammetry [32], Pinhole camera modeling [51]) is called a dense point cloud, where the number of points is larger and denser. Through point cloud, the target surface can be characterized, and finally, 3D physical entities are represented directly and efficiently. ...
The digital twin is an emerging and vital technology for digital transformation and intelligent upgrade. Driven by data and model, the digital twin can perform monitoring, simulation, prediction, optimization, and so on. Specifically, the digital twin modeling is the core for accurate portrayal of the physical entity, which enables the digital twin to deliver the functional services and satisfy the application requirements. Therefore, this paper provides systematic research of current studies on the digital twin modeling. Since the digital twin model is a faithful reflection of the digital twin modeling performance, a comprehensive and insightful analysis of digital twin models is given first from the perspective of the application field, hierarchy, discipline, dimension, universality, and functionality. Based on the analysis of digital twin models, current studies on the digital twin modeling are classified and analyzed according to the six modeling aspects within the digital twin modeling theoretical system proposed in our previous work. Meanwhile, enabling technologies and tools for the digital twin modeling are investigated and summarized. Finally, observations and future research recommendations are presented.
... The use of UAVs is becoming more frequent in IM-based projects, such as the visual inspection of local distribution power networks [5], photovoltaic fields [6], vision-based positioning of an aerial platform on transmission lines [7], cracks in wind turbines [8], and structural health monitoring [9]. Kim and Ham studied wind-induced damage analysis by a vision inspection system based on received information by UAVs [5]. ...
... The use of UAVs is becoming more frequent in IM-based projects, such as the visual inspection of local distribution power networks [5], photovoltaic fields [6], vision-based positioning of an aerial platform on transmission lines [7], cracks in wind turbines [8], and structural health monitoring [9]. Kim and Ham studied wind-induced damage analysis by a vision inspection system based on received information by UAVs [5]. The leaning/damage of the utility poles generated power loss in local distribution networks. ...
... Myeong et al. presented structural health monitoring using a wall-climbing drone prototype [9]. The mentioned research works were engaged with vision inspection and monitoring without intervention [5][6][7][8][9]. ...
This work presents the application of an aerial manipulation robot for the semi-autonomous installation of clip-type bird flight diverters on overhead power line cables. A custom-made prototype is designed, developed, and experimentally validated. The proposed solution aims to reduce the cost and risk of current procedures carried out by human operators deployed on suspended carts, lifts, or manned helicopters. The system consists of an unmanned aerial vehicle (UAV) equipped with a custom-made tool. This tool allows the high force required for the diverter installation to be generated; however, it is isolated from the aerial robot through a passive joint. Thus, the aerial robot stability is not compromised during the installation. This paper thoroughly describes the designed prototype and the control system for semi-autonomous operation. Flight experiments conducted in an illustrative scenario validate the performance of the system; the tests were carried out in an indoor testbed using a power line cable mock-up.
... In terms of information processing, Kim and Ham (2020) proposed using drones and DT models to analyze utility poles. Their method estimated the maximum inclination angle of the utility pole through the image of the utility pole collected by the drone. ...
The importance of digital twin (DT) has been recognized by many industries throughout the world. In the context of construction, DT and relevant technologies have been implemented at different project stages. This study first clarifies the connotation of construction digital twin (CDT) and argues the subtle but important differences between CDT and BIM. Then, this study attempts to identify the recent research and development of CDT by reviewing 46 scholarly papers published in the past three years. The review reveals that: (1) nearly 70% of the reviewed studies focus on the operation and maintenance stage, and the most popular applications of CDT include energy management, facility operation and maintenance, and structural health monitoring; (2) not all studies present a complete CDT; and (3) the mechanisms to fuse different types of data for obtaining more credible decision-support information remain to be explored.
In the United States, hurricanes are the most devastating natural disasters causing billions of dollars worth of damage every year. More importantly, construction jobsites are classified among the most vulnerable environments to severe wind events. During hurricanes, unsecured and incomplete elements of construction sites, such as scaffolding, plywood, and metal rods, will become the potential wind-borne debris, causing cascading damages to the construction projects and the neighboring communities. Thus, it is no wonder that construction firms implement jobsite emergency plans to enforce preparedness responses before extreme weather events. However, relying on checklist-based emergency action plans to carry out a thorough hurricane preparedness is challenging in large-scale and complex site environments. For enabling systematic responses for hurricane preparedness, we have proposed a vision-based technique to identify and analyze the potential wind-borne debris in construction jobsites. Building on this, this paper demonstrates the fidelity of a new machine vision-based method to support construction site hurricane preparedness and further discuss its implications. The outcomes indicate that the convenience of visual data collection and the advantages of the machine vision-based frameworks enable rapid scene understanding and thus provide critical heads up for practitioners to recognize and localize the potential wind-borne debris in construction jobsites and effectively implement hurricane preparedness.
In the United States, hurricanes are the most devastating natural disasters causing billions of dollars worth of damage every year. More importantly, construction jobsites are classified among the most vulnerable environments to severe wind events. During hurricanes, unsecured and incomplete elements of construction sites, such as scaffoldings, plywoods, and metal rods, will become the potential wind-borne debris, causing cascading damages to the construction projects and the neighboring communities. Thus, it is no wonder that construction firms implement jobsite emergency plans to enforce preparedness responses before extreme weather events. However, relying on checklist-based emergency action plans to carry out a thorough hurricane preparedness is challenging in large-scale and complex site environments. For enabling systematic responses for hurricane preparedness, we have proposed a vision-based technique to identify and analyze the potential wind-borne debris in construction jobsites. Building on this, this paper demonstrates the fidelity of a new machine vision-based method to support construction site hurricane preparedness and further discuss its implications. The outcomes indicate that the convenience of visual data collection and the advantages of the machine vision-based frameworks enable rapid scene understanding and thus, provide critical heads up for practitioners to recognize and localize the potential wind-borne derbies in construction jobsites and effectively implement hurricane preparedness.
Inspecting and assessing existing utility poles has become increasingly important for reducing the vulnerability of power distribution infrastructure systems in disaster situations, which can enhance community resilience. Although vision-based systems have been applied to detect faults in power distribution infrastructures, little research currently exists on assessing component-and network-level failures of utility poles based on their geometric and environmental information. This paper aims to propose a new data-driven approach to support risk-informed decision-making for utility maintenance under extreme wind conditions. Large-scale open-source imagery from Google Street View is used to assess geometric properties of utility poles (i.e., leaning angle). Then the failure probability of utility poles is analyzed under varying conditions (e.g., age, leaning angle, and wind loads) in a three-dimensional virtual city model. The proposed method is tested through case studies in Texas to (1) validate an algorithm for estimating leaning angles of utility poles and (2) understand the progress of failures of leaning utility poles from a network perspective. The outcomes of the case studies demonstrate that the proposed method has the potential to leverage large-scale open-source visual data to assess the vulnerability of utility pole networks that may lead to cascading failures in power distribution infrastructure systems. Based on the proposed virtual environment, the method is expected to enable practitioners to facilitate risk-informed decision-making against disaster situations, which creates an opportunity for prioritizing maintenance tasks regarding power distribution infrastructures.
Utility poles would collapse by their structural instability as well as time-dependent material deterioration. Particularly, the moment carrying capacity of leaning poles would be dramatically reduced during extreme wind events. In this paper, we regard leaning poles as warning signs of potential failures that can affect the power distribution network performance and estimate the failure probability of leaning poles. To analyze the moment behavior of leaning poles, we propose a new probabilistic framework for computing three types of loads by wind pressure, overturning force, and conductor tension. A set of fragility curves of utility poles with given ages and leaning angles are presented to assess the impact of leaning on the probability of failure. The proposed analytics are tested through a case study on the parts of the power distribution network in Houston, TX. By examining the progress of failure in the network, this method enables to analyze potentially vulnerable utility poles that are likely to threaten the power distribution system reliability under varying wind speed. Thus, this research has the potential to support risk-informed decision-making for power distribution infrastructure systems and ultimately enhance the urban community resilience to blackouts caused by the power distribution system disruption in extreme weather.
In order to improve the efficiency of post-disaster treatment of power distribution network, the application of UAV in disaster reduction and relief has been paid much attention by the power sector. Aiming at the loss assessment needs of overhead transmission lines in distribution network, this paper proposes an innovative solution of pole detection and counting in distribution network based on UAV inspection line video. Combined with the characteristics of YOLO’s rapid detection, the convolution neural network is applied to the image detection of the pole state. In addition, the pole data and corresponding images are obtained at the same time of detecting the inspection line video. Therefore, the power department can quickly count the losses to cope with the disaster. The anchor value is modified before image training by YOLO v3, and sets the corresponding ROI for the UAV inspection line standard. In order to quickly obtain the loss assessment of post-disaster pole lodging, this paper proposes a counting algorithm by using the continuous ordinate change of the bounding box of the same pole in front and rear frame of video, so that the classified counting of pole is accurate and the detection precision is above 0.9. The results obtained in video test show that this method is effective in detecting and counting the state of the pole of overhead transmission line in distribution network.
This paper proposes a novel hierarchical data fusion technique for the non-destructive testing (NDT) and condition assessment of timber utility poles. The new method analyzes stress wave data from multisensor and multiexcitation guided wave testing using a hierarchical data fusion model consisting of feature extraction, data compression, pattern recognition, and decision fusion algorithms. The researchers validate the proposed technique using guided wave tests of a sample of in situ timber poles. The actual health states of these poles are known from autopsies conducted after the testing, forming a ground-truth for supervised classification. In the proposed method, a data fusion level extracts the main features from the sampled stress wave signals using power spectrum density (PSD) estimation, wavelet packet transform (WPT), and empirical mode decomposition (EMD). These features are then compiled to a feature vector via real-number encoding and sent to the next level for further processing. Principal component analysis (PCA) is also adopted for feature compression and to minimize information redundancy and noise interference. In the feature fusion level, two classifiers based on support vector machine (SVM) are applied to sensor separated data of the two excitation types and the pole condition is identified. In the decision making fusion level, the Dempster–Shafer (D-S) evidence theory is employed to integrate the results from the individual sensors obtaining a final decision. The results of the in situ timber pole testing show that the proposed hierarchical data fusion model was able to distinguish between healthy and faulty poles, demonstrating the effectiveness of the new method.
Round timbers are extensively used as utility poles in Australia for electricity distribution and communication. Lack of information on their conditions results in great difficulties on asset management for industries. Despite the development of various non-destructive testing (NDT) techniques for evaluating the condition of piles, few NDTs are reported for applications on timber poles. This paper addresses challenges and issues on development of NDTs for condition assessment and embedded length of timber poles. For this paper, it is mainly focusing on determining the embedded length of the pole considering loss of the sufficient embedment length is a main factor compromising capacity and safety of timber poles. Since it is impractical for generating longitudinal waves by impacting from the top of poles, utilizing flexural wave from side impact on poles becomes attractive. However, the flexural wave is known by its highly dispersive nature. In this paper, one dimensional wave theory, guided wave theory and advanced signal processing techniques have been introduced in order to provide a solution for the problem. Two signal processing techniques, namely short kernel method and continuous wavelet transform, have been investigated for processing flexural wave signals to evaluate wave velocity and embedment length of timber poles in service.
This paper presents a machine-learning-based approach for the structural health monitoring (SHM) of in-situ timber utility poles based on guided wave (GW) propagation. The proposed non-destructive testing method combines a new multi-sensor testing system with advanced statistical signal processing techniques and state-of-the-art machine learning algorithms for the condition assessment of timber utility poles. Currently used pole inspection techniques have critical limitations including the inability to assess the underground section. GW methods, on the other hand, are techniques potentially capable of evaluating non-accessible areas and of detecting internal damage. However, due to the lack of solid understanding on the GW propagation in timber poles, most methods fail to fully interpret wave patterns from field measurements. The proposed method utilises an innovative multi-sensor testing system that captures wave signals along a sensor array and it applies machine learning algorithms to evaluate the soundness of a pole. To validate the new method, it was tested on eight in-situ timber poles. After the testing, the poles were dismembered to determine their actual health states. Various state-of-the-art machine learning algorithms with advanced data pre-processing were applied to classify the poles based on the wave measurements. It was found that using a support vector machine classifier, with the GW signals transformed into autoregressive coefficients, achieved a very promising maximum classification accuracy of 95.7±3.1% using 10-fold cross validation on multiple training and testing instances. Using leave-one-out cross validation, a classification accuracy of 93.3±6.0% for bending wave and 85.7±10.8% for longitudinal wave excitation was achieved. KEYWORDS
Power line inspection is an essential procedure in the power lines maintenance area, especially thinking about service availability and energy efficiency. Aerial inspection of electric power transmission lines is typically performed using human-piloted helicopters, which is a procedure that is both expensive and prone to accidents taking risks to human beings' lives. The work presents a solution based on UAS (unmanned aircraft system) for inspecting power lines. In this context a R & D project of an unmanned aircraft system to be used for performing complete aerial inspection of overhead power lines is being executed by ITA (Instituto Tecnológico de Aeronáutica) (Technological Institute of Aeronautics) in Brazil. Special attention is dedicated to the communication system conception in order to comply with Remotely Piloted Aircraft System definition in the context of long endurance operations of the system. It presents a solution based on LTA (lighter than air) platform in order to extend the communication range beyond line of sight.
Timber utility poles represent a significant part of Australia's infrastructure for electricity distribution and telecommunication networks. Annual replacement of timber poles are undertaken to avoid pole failures and approximately 40–50 million dollars are spent annually on maintenance and asset management. However, currently practiced inspection techniques for timber poles are mostly subjective in nature and have a lack of reliability, hence, the condition-assessment results provide great difficulties for the asset owners to plan the renewal works. Commonly used wave-propagation based non-destructive testing (NDT) techniques for piles and other concrete structures were adopted and modified to assess the condition of timber poles. The effectiveness of the developed method to detect defects relies on the propagation and reflection of generated shear waves by a transverse impact. However, the orthotropic material properties of timber, the effect of soil embedment, the availability of natural imperfections in timber, and a highly dispersive nature of shear wave propagation make the analysis complex. Therefore, advanced signal-processing techniques are required to identify the damage, the damage location and to accurately assess the damage severity. This paper focuses on damage detection of timber poles using Hilbert-Huang transform as an effective time-frequency analysis technique. A numerical model for an in-situ pole system is developed to simulate the shear wave propagation and to evaluate the capability of the proposed signal-processing technique for an accurate detection of damages. The developed numerical model is validated with the experimental data from the pole yard test and with the data from field-testing. The validated model is then used to simulate different defect profiles, locations and multiple damages to further evaluate the effectiveness of the proposed damage detection technique.
In recent years, emerging mobile devices and camera-equipped platforms have offered a great convenience to visually capture and constantly document the as-is status of construction sites. In this regard, visual data are regularly collected in the form of numerous photos or lengthy videos. However, massive amounts of visual data that are being collected from jobsites (e.g., data collection on daily or weekly bases by Unmanned Aerial Vehicles, UAVs) has provoked visual data overload as an inevitable problem to face. To address such data overload issue in the construction domain, this paper aims at proposing a new method to automatically retrieve photo-worthy frames containing construction-related contents that are scattered in collected video footages or consecutive images. In the proposed method, the presence of objects of interest (i.e., construction-related contents) in given image frames are recognized by the semantic segmentation, and then scores of the image frames are computed based on the spatial composition of the identified objects. To improve the filtering performance, high-score image frames are further analyzed to estimate their likelihood to be intentionally taken. Case studies in two construction sites have revealed that the accuracy of the proposed method is close-to-human judgment in filtering visual data to retrieve photo-worthy image frames containing construction-related contents. The performance metrics demonstrate around 91% of accuracy in the semantic segmentation, and we observed enhanced human-like judgment in filtering construction visual data comparing to prior works. It is expected that the proposed automated method enables practitioners to assess the as-is status of construction sites efficiently through selective visual data, thereby facilitating data-driven decision making at the right time.
This paper presents a study coming from an academic and industry partnership with the goals of exploring potential uses for UAS in distribution system operation and maintenance, establishing baseline costs and capabilities via equipment field tests, and simulating the cost benefits of increased maintenance. Existing UAS technology is shown to be capable of providing situational awareness for disaster response as well as increasing the number of maintenance inspections and speeding them up.
To maintain the reliability, availability, and sustainability of electricity supply, electricity companies regularly perform visual inspections on their transmission and distribution networks. These inspections have been typically carried out using foot patrol and/or helicopter-assisted methods to plan for necessary repair or replacement works before any major damage, which may cause power outage. This solution is quite slow, expensive, and potentially dangerous. In recent years, numerous researches have been conducted to automate the visual inspections by using automated helicopters, flying robots, and/or climbing robots. However, due to the high accuracy requirements of the task and its unique challenges, automatic vision-based inspection has not been widely adopted. In this paper, with the aim of providing a good starting point for researchers who are interested in developing a fully automatic autonomous vision-based power line inspection system, we conduct an extensive literature review. First, we examine existing power line inspection methods with special attention paid to highlight their advantages and disadvantages. Next, we summarize well-suited tasks and review potential data sources for automatic vision-based inspection. Then, we survey existing automatic vision-based power line inspection systems. Based on that, we propose a new automatic autonomous vision-based power line inspection concept that uses Unmanned Aerial Vehicle (UAV) inspection as the main inspection method, optical images as the primary data source, and deep learning as the backbone of data analysis and inspection. Then, we present an overview of possibilities and challenges of deep vision (deep learning for computer vision) approaches for both UAV navigation and UAV inspection and discuss possible solutions to the challenges. Finally, we conclude the paper with an outlook for the future of this field and propose potential next steps for implementing the concept.
Recording geometrically and aesthetically accurate models of cultural heritage sites is important for both their conservation and understanding of historical importance. Rapid imaging systems are required to capture models efficiently so that large sites can be documented with sufficient resolution. Increasing availability of consumer-level drones has fostered digital data collection to replace traditional surveying methods. While such systems allow photogrammetric data to be gathered, they often prove inadequate for large scale rapid imaging. We propose an aerial system infrastructure based on: adapted aerial platforms; optimizations for flight path generation and UAV operations; high-bandwidth LTE transmission; methods of efficient point cloud generation, to rapidly document large scale heritage sites.
Power line inspection is an essential job for the maintenance of electric grids. However, the job is not easy due to wide range of grid distribution. Besides manually inspecting power lines, which is still the main method, helicopters are also used for the inspection from the sky. In the recent years, unmanned flying systems were used for power line inspection in order to reduce the risk. In recent years, some researchers have started to use unmanned helicopters to inspect power lines. Following their research, we present an applied inspection robot called SmartCopter based on an Unmanned Autonomous Helicopter (UAH) for the inspection of transmission lines. Our interest is on the combination of a UAH and an inspection system for the practical application in the field. This paper describes the design of the flying robot and presents the experimental results.
The purpose of this paper is to present the most important achievements in the field of distribution power line inspection by mobile robots. Stimulated by the need for fast, accurate, safe and low-cost power line inspection, which would increase the quality of power delivery, the field of automated power line inspection has witnessed rapid development over the last decade. This paper addresses automated helicopter inspection, inspection with flying robots and inspection with climbing robots. The first attempts to automate power line inspection were conducted in the field of helicopter inspection. In recent years, however, the research was mostly focused on flying and climbing robots. These two types of robots for automated power line inspection are critically assessed according to four important characteristics: design requirements, inspection quality, autonomy and universality of inspection. Besides, some general not yet identified problems and tasks of inspection robots, which should be addressed in the future, are presented. In conclusion, the two robot types have specific benefits and drawbacks so that none can currently be considered generally advantageous.
Power lines keep sparking wildfires. Why don’t CA utilities bury them? | The Sacramento Bee
- T Bizjak
- S Bollag
- D Kasler
Review of in-service assessment of timber poles
- S Baraneedaran
- E Gad
- I Flatley
- A Kamiran
High winds downed power line cause fire in Placer County
- M Teselle
Crews Working on Power Outage in Republic Caused by Leaning Pole
- H Sumpter
Mapping Local Vulnerabilities into a 3D City Model through Social Sensing and the CAVE system toward Digital Twin City
- J Kim
- H Kim
- Y Ham
- D Ph
- Kim J.