Comprehensive Evaluation of Reliability of Protection System in Smart Substation

The reliability of smart substations has a great significance on the safety and stability of smart grid operation. Taking the protection system in smart substation as an example, this paper constructs comprehensive reliability models to evaluate the reliability of smart substations with different architectures. The paper first illustrates two important aspects which affect the reliability of the protection system, namely the network architecture and the maintenance strategy. To satisfy these two aspects, the paper then adopt the Monte Carlo simulation combined with the Reliability Block Diagram method to make quantitative reliability analysis. At last, reliability of four power transformer protection systems applying different maintenance strategies with alternative architectures is evaluated. The simulation results show clearly that advanced maintenance strategies such as conditional maintenance will play a critical role in enhancing the reliability and availability of smart substation. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.510

An efficient YOLO v3-based method for the detection of transmission line defects

The UAV inspection method is gradually becoming popular in transmission line inspection, but it is inefficient only through real-time manual observation. Algorithms are available to achieve automatic image identification, but the detection speed is slow, and video image processing is not possible. In this paper, we propose a fast detection method for transmission line defects based on YOLO v3. The method first establishes a YOLO v3 target detection model and obtains the a priori size of the target candidate region by clustering analysis of the training sample library. The training process of the model is accelerated by adjusting the loss function to adjust the learning direction of the model. Finally, transmission line defect detection was achieved by building a transmission line defect sample library and conducting training. The test results show that compared with other deep learning models, such as Faster R-CNN and SSD, the improved model based on YOLO v3 has a huge speed advantage and the detection accuracy is not greatly affected, which can meet the demand for automatic defect recognition of transmission line inspection videos

Generative Adversarial Network for Image Raindrop Removal of Transmission Line Based on Unmanned Aerial Vehicle Inspection

In the process of UAV line inspection, there may be raindrops on the camera lens. Raindrops have a serious impact on the details of the image, reducing the identification of the target transmission equipment in the image, reducing the accuracy of the target detection algorithm, and hindering the practicability of UAV line inspection technology in cyber-physical energy systems. In this paper, the principle of raindrop image formation is studied, and a method of raindrop removal based on generation countermeasure network is proposed. In this method, the attention recurrent network is used to generate the raindrop attention map, and the context code decoder is used to generate the raindrop image. The experimental results show that the proposed method can remove the raindrops in the image and repair the background image of raindrop coverage area and can generate a higher quality raindrop removal image than the traditional method.</jats:p

Design and Implementation of Transmission Line Insulator Online Monitoring Platform Based on Image Analysis

Abstract The safe operation of insulators directly determines the safety level and reliability of the entire system. In this paper, digital image processing technology is used to analyze and process insulator images, and the performance of composite insulators is comprehensively evaluated from three aspects: degree of deterioration, surface pollution, and degree of water repellency, which is used as the basis for judging insulator detection. For this purpose, an insulator online monitoring system based on telemetry image analysis is specially designed to determine the degree of aging, surface pollution, and hydrophobicity of insulators and to conduct practical tests. At the same time, the system can realize the offline operation of the insulator live, avoid the danger of climbing and live trial operation, and ensure the personal safety of power maintenance personnel. The system hardware uses advanced integration technology and high-performance and reliable equipment. The software is developed using popular development tools, is simple to operate, and has important engineering value.</jats:p

High-Precision Recognition Algorithm for Equipment Defects Based on Mask R-CNN Algorithm Framework in Power System

In current engineering applications, target detection based on power vision neural networks has problems with low accuracy and difficult defect recognition. Thus, this paper proposes a high-precision substation equipment defect recognition algorithm based on the Mask R-CNN algorithm framework to achieve high-precision substation equipment defect monitoring. The effectiveness of the Mask R-CNN algorithm is compared and analyzed in substation equipment defect recognition and the applicability of the Mask R-CNN algorithm in edge computing. According to different types of substation equipment defect characteristics, substation equipment defect recognition guidelines were developed. The guideline helps to calibrate the existing training set and build defect recognition models for substation equipment based on different algorithms. In the end, the system based on a power edge vision neural network was built. The feasibility and accuracy of the algorithm was verified by model training and actual target detection results