Diagnosis and Classification Decision Analysis of Overheating Defects of Substation Equipment Based on Infrared Detection Technology

Substation equipment is not only the main part of the power grid but also the essential part to ensure the development of the national economy and People's Daily life of one of the important infrastructure. How to ensure its normal operation and find the sudden failure has become a hot issue to be solved urgently. For thermal fault diagnosis needs to classify and identify different power equipment first, this paper designed an SVM infrared image classifier, which can effectively identify three types of common power equipment. The classifier extracts HOG features from the infrared images of power equipment processed by the above segmentation and combines them with SVM multiclassification to achieve the purpose of improving the recognition accuracy. The experiment uses the classifier to identify three kinds of equipment, and the results show that the comprehensive recognition accuracy of the classifier is more than 95.3%, which is better than the traditional classification method and meets the demand for classification accuracy. In this paper, the traditional method of relative temperature difference is improved by using the temperature data of the infrared image, which can automatically judge the thermal failure level of electric power equipment. Experiments show that the diagnosis system designed in this paper can classify faults and give treatment suggestions while judging whether there are thermal faults for three types of power equipment, which verifies the feasibility and effectiveness of the substation infrared diagnosis technology designed in this paper.

Image Recognition Technology with Its Application in Defect Detection and Diagnosis Analysis of Substation Equipment

Detection of substation equipment can promptly and effectively discover equipment overheating defects and prevent equipment failures. Traditional manual diagnosis methods are difficult to deal with the massive infrared images generated by the autonomous inspection of substation robots and drones. At present, most of the infrared image defect recognition is based on traditional machine learning algorithms, with low recognition accuracy and poor generalization capability. Therefore, this paper develops a method for identifying infrared defects of substation equipment based on the improvement of traditional ones. First, based on the Faster RCNN, target detection is performed on 6 types of substation equipment including bushings, insulators, wires, voltage transformers, lightning rods, and circuit breakers to achieve precise positioning of the equipment. Afterwards, different classes are identified based on the sparse representation-based classification (SRC), so the actual label of the input sample can be obtained. Finally, based on the temperature threshold discriminant algorithm, defects are identified in the equipment area. The measured infrared images are used for experiments. The average detection accuracy achieved by the proposed method for the 6 types of equipment reaches 92.34%. The recognition rate of different types of equipment is 98.57%, and the defect recognition accuracy reaches 88.75%. The experimental results show the effectiveness and accuracy of the proposed method.

Application of Augmented Reality (AR) Technology in Power Grid Emergency Training

Strong reality has been applied to training operations, and the use of virtual and augmented reality in aerospace, manufacturing and shipbuilding industries has yielded significant results. This paper mainly studies the application of Augmented Reality (AR) technology in power grid emergency training. This paper designs and implements an intelligent operation and maintenance system based on mobile augmented reality under the Android operating system. Augmented reality technology is applied to substation equipment operation and maintenance. Through the design and development of modules such as data management, equipment identification, holographic display of equipment information, integrated management and remote assistance, the application of Augmented Reality technology in substation equipment operation and maintenance is realized. Based on augmented reality and identification technology, the auxiliary information is transmitted to the intelligent terminal display of field operators in real time to assist the power grid emergency training and improve efficiency.

Event Monitoring of Transformer Discharge Sounds based on Voiceprint

This paper investigates the operation inspection and anomaly diagnosis of transformers in substations, and carries out an application study of artificial intelligence-based sound recognition technology in transformer discharge diagnosis to improve the timeliness and diagnostic capability of intelligent monitoring of substation equipment operation. In this study, a sound parameterization technology in the field of sound recognition is used to implement automatic discharge sound detections. The sound samples are pre-processed and then Mel-frequency cepstrum coefficients (MFCCs) are extracted as features, which are used to train Gaussian mixture models (GMMs). Finally, the trained GMMs are used to detect discharge sounds in the place of transformers in substations. The test results demonstrate that the audio anomaly detection based on MFCCs and GMMs can be used to effectively recognize anomalous discharge in the high scenario of transformers.

Application of Optimization Technology for Overhaul Decision of Substation Equipment Based on Machine Learning

With the continuous improvement of living standards and the continuous increase of electricity load, the number of power transmission and transformation equipment also increases rapidly. The original maintenance mode is not enough to guarantee the safe operation of the huge power grid. This paper mainly studies the research and application of machine learning based maintenance decision optimization technology for substation equipment. Starting from the technical principles of online monitoring and condition maintenance of substation equipment, this paper has realized an intelligent monitoring and maintenance early warning system combined with deep learning model. The main functions of this system include monitoring device management, operation monitoring and comprehensive display, etc., which can effectively carry out online monitoring and state early warning of substation equipment. It greatly improves the intelligent degree of operation and management of substation equipment, saves the cost of traditional manual monitoring, and effectively prevents the economic loss caused by substation equipment failure, which has far-reaching significance for promoting the construction of smart power grid.

Design of Effective 33/11 kV Injection Substations Using International Standards

This research focuses on the design of an effective 33/11 kV modelled injection substation that conforms to an appropriate standard for equipment protection, the safety of personnel and power quality compliance. This is to provide a solution to one of the major problems industries in Nigeria faces due to sudden voltage fluctuations in the power system which results in damages to equipment and thus outage of power supply and damages to substation equipment. The methodology involved designing an effective 33/11 kV injection substation and associated distributive substation elements using international codes and applicable algorithms. 60% loading of transformer and additional 1.25 factor of future expansion (F.E) were considered too. The results showed that a 7.5 MVA injection transformer was designed to operate at 60%. Also, the results revealed that the injection substation would feed 15 numbers of 500 kVA distributive transformers. Each distribution substation was sized in accordance with the 7.5MVA injection transformer philosophy in this work. This research concludes that the injection substation must be loaded at 60% with an additional 1.25 F.E. in order to increase the transformer life span, and the 7.5MVA injection substation can crater for 15nos of 500 kVA distribution transformers in this research. Each substation will reliably and effectively carry the expected load demand. This research recommends that injection substations should be designed for areas with high energy requirements for reliable power quality. It recommends that substations should conform to 60% loading at the initial years of usage and that the substation design should conform to appropriate standards used in this work.

System Diagnostics of Supporting-Rod Porcelain Insulation at Digital Substations

This article is dedicated to an important problem of increasing the reliability of supporting-rod porcelain insulation at electric power substations. Porcelain insulation is installed on open switchgears (OSGs) of substations as part of the main switching equipment. Obsolescence and physical wear results in the destruction of supporting-rod porcelain insulators (SRIs), which often leads to serious consequences: shutdown of substation bus systems, emergency shutdown of substation equipment, reduction of power plant loads, as well as they pose a threat to operational personnel during operational switching. When supporting-rod insulation is replaced in due time, it significantly increases the reliability of the main substation equipment. Possible causes of failures were analysed using a graphical technique (Ishikawa diagrams). It has been established that sudden temperature changes of the ambient air are an important negative aspect, especially the transition of temperature values through 0ºC. The work represents a mathematical model of the development of microfractures in the insulation body of a ceramic insulator. The influence of external forces on the insulator leads to the appearance of additional stresses in it, to the destruction of new particles and to the sudden growth of microfractures. The article gives main SRI diagnostic techniques currently used in the electric power industry. According to statistics, it has been established that a relevant approach is the transition to digital technologies, which provides an automated information processing process without deactivating a piece of equipment. The article describes and proves that the proposed system of diagnostics of supporting-rod porcelain insulation at digital substations without deactivating a piece of equipment is a relevant area of scientific development. Within the framework of this scientific study, a patent for invention No. 2743887 dated 20 April 2020 was obtained.

Substation Equipment Monitoring And Controllig Using IoT

This project is based embedded system used for monitoring the voltage, current, and temperature and oil level of a transformer. Furthermore it is capable of recognizing the break downs caused due to overload, high temperature, over voltage and oil level intimation of transformer. The design generally consists of units, one in the substation unit, called as display unit, display units in the substation is where the voltage, current and temperature are monitored continuously by AVR microcontroller and is displayed through the display unit. The ultimate objective is to monitor the electrical parameters continuously and hence to guard the burning of transformer or power transformer due to the constraints such as overload, over temperature, input high voltage and double protection of CB operation by using the Internet of Things (IoT).