With the fast development of smart grid, the power line mileage and power equipments get rapid growth. The contradiction between the large number of maintenance equipments and the small number of maintenance workers becomes increasingly prominent. Meanwhile, the traditional maintenance mode has the disadvantages of over or under maintenance, which will lead to the increasing failure risk of power transmission system. Faced with these issues, to enhance the intelligence and automation level of power line inspection, many researchers have devoted much effort to the research of automatic power line inspection and some state-of-the-art techniques about power line inspection are proposed to improve the inspection efficiency and quality, such as unmanned aerial vehicle (UAV), image processing, deep architecture, and so on. In this article, we analyzed and summarized the state-of-the-art techniques on power line inspection to provide a valuable reference for the researchers engaged in the smart grid. First, the common inspection tasks on power line inspection are reviewed. Second, the existing inspection platforms are examined in this article. Also, the advantages and disadvantages of different platforms are analyzed accordingly. Third, faced with different inspection tasks, different inspection sensors are integrated into the inspection platforms for data collection. Therefore, the common sensors on inspection platforms are discussed. Finally, to realize automatic power line inspection, different inspection methods are proposed or improved, and these advanced inspection methods are surveyed and discussed.

A Review on State-of-the-Art Power Line Inspection Techniques

Application of the best evacuation model of deep learning in the design of public structures

Object detection is one of the predominant and challenging problems in computer vision. Over the decade, with the expeditious evolution of deep learning, researchers have extensively experimented and contributed in the performance enhancement of object detection and related tasks such as object classification, localization, and segmentation using underlying deep models. Broadly, object detectors are classified into two categories viz. two stage and single stage object detectors. Two stage detectors mainly focus on selective region proposals strategy via complex architecture; however, single stage detectors focus on all the spatial region proposals for the possible detection of objects via relatively simpler architecture in one shot. Performance of any object detector is evaluated through detection accuracy and inference time. Generally, the detection accuracy of two stage detectors outperforms single stage object detectors. However, the inference time of single stage detectors is better compared to its counterparts. Moreover, with the advent of YOLO (You Only Look Once) and its architectural successors, the detection accuracy is improving significantly and sometime it is better than two stage detectors. YOLOs are adopted in various applications majorly due to their faster inferences rather than considering detection accuracy. As an example, detection accuracies are 63.4 and 70 for YOLO and Fast-RCNN respectively, however, inference time is around 300 times faster in case of YOLO. In this paper, we present a comprehensive review of single stage object detectors specially YOLOs, regression formulation, their architecture advancements, and performance statistics. Moreover, we summarize the comparative illustration between two stage and single stage object detectors, among different versions of YOLOs, applications based on two stage detectors, and different versions of YOLOs along with the future research directions. 

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Object detection using YOLO: challenges, architectural successors, datasets and applications

Damage estimation is part of daily operation of power utilities, often requiring a manual process of crew deployment and damage report to quantify and locate damages. Advancement in unmanned aerial vehicles (UAVs) as well as real-time communication and learning technologies could be harnessed towards efficient and accurate automation of this process. This paper develops a model to automate the process of estimating and localizing damages in power distribution poles, which utilizes the images taken by UAVs transferred in real-time to an intelligent damage classification and estimation (IDCE) unit. The IDCE unit integrates four convolutional neural networks to learn the states of poles from images, extract the image characteristics, and train an automated intelligent tool to replace manual fault location and damage estimation. The proposed model first determines the type of pole damages, including falling and burning, and then estimates the percentage of damage in each type. The IDCE unit also localizes damages in the poles by locating possible burning or arcing parts. A data set of 1615 images is utilized to train, validate and test the proposed model, which demonstrates high accuracy of the model in classifying and estimating damages in distribution poles.

Intelligent Damage Classification and Estimation in Power Distribution Poles Using Unmanned Aerial Vehicles and Convolutional Neural Networks

In the era of twenty-first century, artificial intelligence plays a vital role in the day to day life of human beings. Now days it has been used for many application such as medical, communication, object detection, object identification, object tracking. This paper is focused on the identification of diseases in bell pepper plant in large fields using deep learning approach. Bell Pepper farmers, in general do not notice if their plants are infected with bacterial spot disease. The spread of the disease usually causes a decrease in the yield. The solution is to detect if bacterial spot disease is present in the bell pepper plant at an early stage. We do some random sampling of few pictures from different parts of the farm. YOLOv5 is used for detecting the bacterial spot disease in bell pepper plant from the symptoms seen on the leaves. With YOLO v5 we are able to detect even a small spot of disease with considerable speed and accuracy. It takes the full image in a single instant and predicts bounding boxes and class probability. The input to the model is random picture from the farm by using a mobile phone. By viewing the output of the program, farmers can find out whether bacterial spot disease has in any way affected the plants in their farm. The proposed model is very useful for framers, as they can identify the plant diseases as soon as it appears and thus, do proper measures to prevent the spread of the disease. The motive of this paper is to come up with a method of detecting the bacterial spot disease in bell pepper plant from pictures taken from the farm.

Leaf-based disease detection in bell pepper plant using YOLO v5

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.

Distribution Line Pole Detection and Counting Based on YOLO Using UAV Inspection Line Video

The article “Distribution Line Pole Detection and Counting Based on YOLO Using UAV Inspection Line Video“, written by Binghuang Chen and Xiren Miao was originally published.

/pdf/correction-to-distribution-line-pole-detection-and-counting-1b729zw0eq.pdf

Correction to: Distribution Line Pole Detection and Counting Based on YOLO Using UAV Inspection Line Video

Precise forecasting of the thermal parameters is a critical factor for the safe operation and fault incipient warning of the ultra-high voltage (UHV) transformers. In this work, a novel multi-step forecasting method based on the long- and short-term time-series network (LSTNet) with the conditional mutual information (CMI) is proposed for the UHV transformer. To improve the computational efficiency and eliminate the redundancy, the CMI-based feature selection algorithm is applied to analyse the correlation between the original monitoring parameters and construct the optimal feature subset. LSTNet, which is composed of a convolutional layer, recurrent layer and recurrent-skip layer, is utilized to capture both the short-term nonlinear characteristics and the long-term periodic characteristics. The LSTNet model is established to forecast the variation tendency of the oil and winding temperatures for different locations in the UHV transformer. The results show that the proposed method significantly enhances the accuracy in both one-step and multi-step thermal parameters forecasting and achieves better performance in terms of the RMSE and MAE compared with other existing methods. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/elp2.12175

Forecasting thermal parameters for ultra‐high voltage transformers using long‐ and short‐term time‐series network with conditional mutual information

Detecting the components in aerial images is a crucial task in automatic visual inspection for power lines. Currently, deep learning models guided by external knowledge have achieved promising performances compared to directly applying the benchmark detectors. However, the component relationship, as human commonsense knowledge for object reasoning, is rarely investigated in this field. This study presents a graph-based relation guided network for power line component detection, which exploits correlations of regions, images, and categories. The visual relation module is employed to learn region-to-region relationship and enhance the visual features of each proposal that may contain components. Meanwhile, two guidance modules are proposed to capture image-to-region correlation and distinctively facilitate the category classification and position regression, which has not been considered in previous methods. Moreover, the category graphs built in these two modules are able to explore category-to-category dependencies that can further promote the network ability. Experimental results demonstrate that the proposed method can achieve more accurate and reasonable component detection compared to previous methods, which verifies the effectiveness of the proposed model incorporated with relation knowledge. 

Component Detection for Power Line Inspection Using a Graph-based Relation Guiding Network

For the maintenance of power lines, a core task is to diagnose the fault of different components from the aerial inspection images. Currently, deep-learning models trained on defect samples have achieved promising performances for automatic fault diagnosis. However, the slow accumulation process of fault data leads to a long-term challenge of data insufficiency in this field. In this article, a normalized multihierarchy embedding matching (NMHEM)-based anomaly detection method is proposed to inspect power line faults, which only utilizes defect-free samples during training. To impart the NMHEM with the ability to detect anomalous patterns in images, three main modules are introduced. First, the embedding generation module (EGM) is employed to extract deep hierarchical representations. Next, hierarchy-wise anomaly scores are calculated through the embedding matching module (EMM) to measure the anomalous degree, which can make the model more discriminative at different hierarchies. Finally, a normalizing module (NM) is developed and served as a credible scoring function indicating the probability of anomaly occurring, thus boosting the performance of the fault diagnosis. The proposed NMHEM adaptively aggregates local spatial and global semantic information which leverages the available nominal knowledge from normal data, achieving effective fault diagnosis of power lines. Experiments are conducted on the dataset that contains five key components. Results show that our method achieves 88.4% area under the curve (AUC) and 80.5% $F1$ -score, which outperforms other supervised and semisupervised methods.

Xiren Miao

Papers

Distribution Line Pole Detection and Counting Based on YOLO Using UAV Inspection Line Video

Correction to: Distribution Line Pole Detection and Counting Based on YOLO Using UAV Inspection Line Video

Forecasting thermal parameters for ultra‐high voltage transformers using long‐ and short‐term time‐series network with conditional mutual information

Component Detection for Power Line Inspection Using a Graph-based Relation Guiding Network

Fault Diagnosis in Power Line Inspection Using Normalized Multihierarchy Embedding Matching