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

Greenhouses can grow many off‐season vegetables and fruits, which improves people's quality of life. Greenhouses can also help crops resist natural disasters and ensure the stable growth of crops. However, it is highly challenging to carefully control the greenhouse climate. Therefore, the proposal of a greenhouse climate prediction model provides a way to solve this challenge. We focus on the six climatic factors that affect crops growth, including temperature, humidity, illumination, carbon dioxide concentration, soil temperature and soil humidity, and propose a GCP_lstm model for greenhouse climate prediction. The climate change in greenhouse is nonlinear, so we use long short‐term memory (LSTM) model to capture the dependence between historical climate data. Moreover, the short‐term climate has a greater impact on the future trend of greenhouse climate change. Therefore, we added a 5‐min time sliding window through the analysis experiment. In addition, sensors sometimes collect wrong climate data. Based on the existence of abnormal data, our model still has good robustness. We experienced our method on the data sets of three vegetables: tomato, cucumber and pepper. The comparison shows that our method is better than other comparison models.

A long short-term memory-based model for greenhouse climate prediction

Remote sensing targets have different dimensions, and they have the characteristics of dense distribution and a complex background. This makes remote sensing target detection difficult. With the aim at detecting remote sensing targets at different scales, a new You Only Look Once (YOLO)-V3-based model was proposed. YOLO-V3 is a new version of YOLO. Aiming at the defect of poor performance of YOLO-V3 in detecting remote sensing targets, we adopted DenseNet (Densely Connected Network) to enhance feature extraction capability. Moreover, the detection scales were increased to four based on the original YOLO-V3. The experiment on RSOD (Remote Sensing Object Detection) dataset and UCS-AOD (Dataset of Object Detection in Aerial Images) dataset showed that our approach performed better than Faster-RCNN, SSD (Single Shot Multibox Detector), YOLO-V3, and YOLO-V3 tiny in terms of accuracy. Compared with original YOLO-V3, the mAP (mean Average Precision) of our approach increased from 77.10% to 88.73% in the RSOD dataset. In particular, the mAP of detecting targets like aircrafts, which are mainly made up of small targets increased by 12.12%. In addition, the detection speed was not significantly reduced. Generally speaking, our approach achieved higher accuracy and gave considerations to real-time performance simultaneously for remote sensing target detection.

Improved YOLO-V3 with DenseNet for Multi-Scale Remote Sensing Target Detection.

Efficient and low complex architecture for detection and classification of Brain Tumor using RCNN with Two Channel CNN

Defective insulators seriously threaten the safe operation of transmission lines. This paper proposes an insulator defect detection method based on an improved YOLOv4 algorithm. An insulator image sample set was established according to the aerial images from the power grid and the public dataset on the Internet, combining with the image augmentation method based on GraphCut. The insulator images were preprocessed by Laplace sharpening method. To solve the problems of too many parameters and low detection speed of the YOLOv4 object detection model, the MobileNet lightweight convolutional neural network was used to improve YOLOv4 model structure. Combining with the transfer learning method, the insulator image samples were used to train, verify, and test the improved YOLOV4 model. The detection results of transmission line insulator and defect images show that the detection accuracy and speed of the proposed model can reach 93.81% and 53 frames per second (FPS), respectively, and the detection accuracy can be further improved to 97.26% after image preprocessing. The overall performance of the proposed lightweight YOLOv4 model is better than traditional object detection algorithms. This study provides a reference for intelligent inspection and defect detection of suspension insulators on transmission lines.

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Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model

RCNN-based foreign object detection for securing power transmission lines (RCNN4SPTL)

This paper proposes a new deep learning network - RCNN4SPTL (RCNN -based Foreign Object Detection for Securing Power Transmission lines), which is suitable for detecting foreign objects on power transmission lines. The RCNN4SPTL uses RPN (Region Proposal Network) to generate aspect ratio of the region proposals to align with the size of foreign objects. The RCNN4SPTL uses an end to end training to improve its performance. Experimental results show that the RCNN4SPTL significantly improves the detection speed and recognition accuracy, compared with the original Faster RCNN.

RCNN-based foreign object detection for securing power transmission lines (RCNN4SPTL).

Analytical Determination of Interwell Connectivity Based on Interwell Influence

RPN-FCN based Rust detection on power equipment

It is important for monitoring and predicting equipment failures. The existing fault prediction method has poor efficiency and accuracy on processing imbalanced data. This paper proposes a feature pattern-based LSTM method (called FLSTM, Feature based Long Short Term Memory) to analyze failures through processing imbalanced data. The method constructs a time-series feature matrix as the input to the LSTM model. In addition, we propose a failure prediction system based on Hadoop environment. The experimental results show that the FLSTM can improve failure prediction with imbalanced big data and the failure prediction system performs well.

Jiangru Yuan

Papers

RCNN-based foreign object detection for securing power transmission lines (RCNN4SPTL)

RCNN-based foreign object detection for securing power transmission lines (RCNN4SPTL).

Analytical Determination of Interwell Connectivity Based on Interwell Influence

RPN-FCN based Rust detection on power equipment

FLSTM: Feature Pattern-Based LSTM for Imbalanced Big Data Analysis