As the failure of power line insulators leads to the failure of power transmission systems, an insulator inspection system based on an aerial platform is widely used. Insulator defect detection is performed against complex backgrounds in aerial images, presenting an interesting but challenging problem. Traditional methods, based on handcrafted features or shallow-learning techniques, can only localize insulators and detect faults under specific detection conditions, such as when sufficient prior knowledge is available, with low background interference, at certain object scales, or under specific illumination conditions. This paper discusses the automatic detection of insulator defects using aerial images, accurately localizing insulator defects appearing in input images captured from real inspection environments. We propose a novel deep convolutional neural network (CNN) cascading architecture for performing localization and detecting defects in insulators. The cascading network uses a CNN based on a region proposal network to transform defect inspection into a two-level object detection problem. To address the scarcity of defect images in a real inspection environment, a data augmentation method is also proposed that includes four operations: 1) affine transformation; 2) insulator segmentation and background fusion; 3) Gaussian blur; and 4) brightness transformation. Defect detection precision and recall of the proposed method are 0.91 and 0.96 using a standard insulator dataset, and insulator defects under various conditions can be successfully detected. Experimental results demonstrate that this method meets the robustness and accuracy requirements for insulator defect detection.

Detection of Power Line Insulator Defects Using Aerial Images Analyzed With Convolutional Neural Networks

Insulators are critical electric components in transmission lines. Recognizing insulators and detecting the faults timely and accurately is essential for maintaining the safety and stability of transmission lines. Traditional methods have low accuracy and poor applicability in insulator recognition and fault detection. An insulator recognition and fault detection model was proposed in the article aiming at improving the insulator recognition and fault detection accuracy. First, based on the faster region convolutional neural network (RCNN), the feature pyramid networks (FPNs) were used to improve the Faster RCNN model and locate the insulators with complex background image. Then, the target area was clipped to remove the redundant background noise, and the hue, saturation, and value (HSV) color space adaptive threshold algorithm was applied for image segmentation due to the influence of light, background noise, and shooting angle. Finally, line detection, image rotation, and vertical projection were used to finish the insulator fault detection. The experimental results show that the proposed insulator recognition and fault detection model can recognize the insulators and detect fault types with better accuracy and achieve a mean average precision (mAP) of 90.8% for glass insulators and 91.7% for composite insulators on the testing dataset. Additionally, the proposed method meets the intelligent inspection of insulator faults in transmission lines and has good engineering application value.

An Insulator in Transmission Lines Recognition and Fault Detection Model Based on Improved Faster RCNN

Hybrid-YOLO for classification of insulators defects in transmission lines based on UAV

Unmanned aerial vehicles (UAVs) have become important tools for power transmission line inspection. Cameras installed on the platforms can efficiently obtain aerial images containing information about power equipment. However, most of the existing inspection systems cannot perform automatic real-time detection of transmission line components. In this paper, an automatic transmission line inspection system incorporating UAV remote sensing with binocular visual perception technology is developed to accurately detect and locate power equipment in real time. The system consists of a UAV module, embedded industrial computer, binocular visual perception module, and control and observation module. Insulators, which are key components in power transmission lines as well as fault-prone components, are selected as the detection targets. Insulator detection and spatial localization in aerial images with cluttered backgrounds are interesting but challenging tasks for an automatic transmission line inspection system. A two-stage strategy is proposed to achieve precise identification of insulators. First, candidate insulator regions are obtained based on RGB-D saliency detection. Then, the skeleton structure of candidate insulator regions is extracted. We implement a structure search to realize the final accurate detection of insulators. On the basis of insulator detection results, we further propose a real-time object spatial localization method that combines binocular stereo vision and a global positioning system (GPS). The longitude, latitude, and height of insulators are obtained through coordinate conversion based on the UAV’s real-time flight data and equipment parameters. Experiment results in the actual inspection environment (220 kV power transmission line) show that the presented system meets the requirement of robustness and accuracy of insulator detection and spatial localization in practical engineering.

Real-Time Detection and Spatial Localization of Insulators for UAV Inspection Based on Binocular Stereo Vision

Modeling and analysis of an impact-acoustic method for bolt looseness identification

Insulators are important equipment used to electrically isolate and mechanically hold wires in high-voltage power transmission systems. Faults caused by the deterioration of the insulators induce very serious problems to the power transmission line. Techniques were introduced for the acoustic detection of insulator faults by acoustic radiation noises. Radiation noises were measured from normal state insulators and fault state insulators in an anechoic chamber. The insulators used were two porcelain insulators, a cut-out switch, two line posters, and a lightning arrester. A new acoustic technique determines the direction of the insulator faults using source localization with three-dimensional microphone arrays. The advantage is to classify the fault state insulators without human inspection by considering the amount of total noises and 120-Hz harmonic components. The fault detection was determined by neural network to diagnose the state automatically. The proposed technique was evaluated by distinct, real datasets and the efficacy was validated. The noise source was detected with 100.0% accuracy and the classification ratio achieved 96.7% for three typical conditions.

Acoustic Fault Detection Technique for High-Power Insulators

The convolution code(CC) of code rate 1/2 as a forward error correction (FEC) in Quadrature Phase Shift Keying (QPSK) is applied to decrease bit error rate (BER) by background noise and multipath in shallow water acoustic channel. Ratio of transmitting signal bandwidth to channel coherence bandwidth is defined as frequency selectivity index. BER and bit energy-to-noise ratio gain of transmitted signal according to frequency selectivity index are evaluated. In the results of indoor water tank experiment, BER is well matched theoretical results at frequency selectivity index less than about 1.0. And bit energy-to-noise ratio gain is also matched theoretical value of 5 dB. BER is effectively decreased at frequency selective multipath channel with frequency selectivity index higher than 1.0. But bit energy-to-noise ratio greater than a certain size in terms of CC weaving is effective in reducing bit errors. In the results, the defined frequency selectivity index in this study could be applied to evaluate a performance of CC in multipath channel. Also it could effectively reduced BER in a low speed underwater acoustic communication system without an equalizer.

/pdf/performance-of-convolution-coding-underwater-acoustic-45t0sezbjn.pdf

Performance of Convolution Coding Underwater Acoustic Communication System on Frequency Selectivity Index

In this paper, performance of COFDM (Coded Orthogonal Frequency Division Multiplexing) which is OFDM with a forward error correction code, is studied in frequency selective fading underwater acoustic communication channel. The OFDM is a multiplexing technique resistant to frequency selective multipath channel. In OFDM, a broadband information signal is transformed into several narrow band signals and transmits narrow band signals whose bandwidths are less than the channel coherence bandwidth. However, its performance is degraded in a specific narrow band signal due to its deep fading by multipath. To mitigate this degradation, COFDM which is OFDM with convolution code as a forward error correction code, is evaluated. The performance of COFDM is found to be better than that of OFDM in multipath channel.

Performance of COFDM in Underwater Acoustic Channel with Frequency Selective Fading

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Acoustic Fault Detection Technique for High-Power Insulators

Performance of Convolution Coding Underwater Acoustic Communication System on Frequency Selectivity Index

Performance of COFDM in Underwater Acoustic Channel with Frequency Selective Fading