Study on Power Transformer Fault Diagnosis Based on Niche Genetic Algorithm

2009 ◽  
Author(s):  
Jiyin Zhao ◽  
Ruirui Zheng ◽  
Haihong Dong
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Transformer Fault

New Methold on Power Transformer Fault Diagnosis

2012 ◽  
Vol 217-219 ◽  
pp. 2623-2628
Author(s):  
Nan Lan Wang ◽  
Ming Shan Cai
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Wavelet Neural Network ◽  
Bp Algorithm ◽  
Oil Analysis ◽  
Wavelet Network ◽  
Dissolved Gas ◽  
Transformer Fault

This paper improves the simple genetic algorithm and combines genetic algorithm with BP algorithm to the wavelet neural network in the power transformer fault diagnosis by dissolved gas-in-oil analysis, Simulation result shows the problem was solved that wavelet network settles into local small extremum so easily that the network surging will increase and the network will not be convergent if the initialization is unreasonable, and overcomes the shortcoming that the speed is too slow if use genetic algorithm to train neural network independently.

scholarly journals Improved Genetic Algorithm and XGBoost Classifier for Power Transformer Fault Diagnosis

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhanhong Wu ◽  
Mingbiao Zhou ◽  
Zhenheng Lin ◽  
Xuejun Chen ◽  
Yonghua Huang
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Gas Analysis ◽  
Diagnostic Methods ◽  
Identification Accuracy ◽  
Basic Unit ◽  
Support Vector ◽  
Improved Genetic Algorithm ◽  
Transformer Fault

Power transformer is an essential component for the stable and reliable operation of electrical power grid. The traditional transformer fault diagnostic methods based on dissolved gas analysis are limited due to the low accuracy of fault identification. In this study, an effective transformer fault diagnosis system is proposed to improve identification accuracy. The proposed approach combines an improved genetic algorithm (IGA) with the XGBoost to form a hybrid diagnosis network. The combination of the improved genetic algorithm and the XGBoost (IGA-XGBoost) forms the basic unit of the proposed method, which decomposes and reconstructs the transformer fault recognition problem into several minor problems IGA-XGBoosts can solve. The results of simulation experiments show that the IGA performs excellently in the combined optimization of input feature selection and the XGBoost parameter, and the proposed method can accurately identify the transformer fault types with an average accuracy of 99.2%. Compared to IEC ratios, dual triangle, support vector machine and common vector approach the diagnostic accuracy of the proposed method is improved by 30.2, 47.2, 11.2, and 3.6%, respectively. The proposed method can be a potential solution to identify the transformer fault types.

Improved PSO-BP neural network for power transformer fault diagnosis

2010 ◽  
Vol 30 (3) ◽  
pp. 783-785 ◽  
Author(s):  
Zhong-yang XIONG ◽  
Qing-bo YANG ◽  
Yu-fang ZHANG
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Power Transformer ◽  
Transformer Fault ◽  
Improved Pso

scholarly journals Power transformer fault diagnosis system based on Internet of Things

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Guoshi Wang ◽  
Ying Liu ◽  
Xiaowen Chen ◽  
Qing Yan ◽  
Haibin Sui ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Internet Of Things ◽  
Power Transformer ◽  
Fault System ◽  
Ratio Method ◽  
Diagnosis System ◽  
Network Training ◽  
Fault Diagnosis System ◽  
Training Error ◽  
Transformer Fault

Abstract Transformer is the most important equipment in the power system. The research and development of fault diagnosis technology for Internet of Things equipment can effectively detect the operation status of equipment and eliminate hidden faults in time, which is conducive to reducing the incidence of accidents and improving people's life safety index. Objective To explore the utility of Internet of Things in power transformer fault diagnosis system. Methods A total of 30 groups of transformer fault samples were selected, and 10 groups were randomly selected for network training, and the rest samples were used for testing. The matter-element extension mathematical model of power transformer fault diagnosis was established, and the correlation function was improved according to the characteristics of three ratio method. Each group of power transformer was diagnosed for four months continuously, and the monitoring data and diagnosis were recorded and analyzed result. GPRS communication network is used to complete the communication between data acquisition terminal and monitoring terminal. According to the parameters of the database, the working state of the equipment is set, and various sensors are controlled by the instrument driver module to complete the diagnosis of transformer fault system. Results The detection success rate of the power transformer fault diagnosis system model established in this paper is as high as 95.6%, the training error is less than 0.0001, and it can correctly identify the fault types of the non training samples. It can be seen that the technical support of the Internet of Things is helpful to the upgrading and maintenance of the power transformer fault diagnosis system.

Power Transformer Fault Diagnosis Based on Vibration Correlation Analysis

2014 ◽  
Author(s):  
Kaixing Hong ◽  
Hai Huang
Keyword(s):  
Fault Diagnosis ◽  
Correlation Analysis ◽  
Fundamental Frequency ◽  
Vibration Analysis ◽  
Laboratory Tests ◽  
Power Transformer ◽  
Assessment Model ◽  
Vibration Method ◽  
Proposed Model ◽  
Transformer Fault

In this paper, a condition assessment model using vibration method is presented to diagnose winding structure conditions. The principle of the model is based on the vibration correlation. In the model, the fundamental frequency vibration analysis is used to separate the winding vibration from the tank vibration. Then, a health parameter is proposed through the vibration correlation analysis. During the laboratory tests, the model is validated on a test transformer, and manmade deformations are provoked in a special winding to compare the vibrations under different conditions. The results show that the proposed model has the ability to assess winding conditions.

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