An unsupervised approach for fault diagnosis of power transformers

2021 ◽  
Author(s):  
Luis Dias ◽  
Miguel Ribeiro ◽  
Armando Leitão ◽  
Luis Guimarães ◽  
Leonel Carvalho ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Power Transformers ◽  
Unsupervised Approach

scholarly journals A deep learning unsupervised approach for fault diagnosis of household appliances

2020 ◽  
Vol 53 (2) ◽  
pp. 10749-10754
Author(s):  
Francesco Cordoni ◽  
Gianluca Bacchiega ◽  
Giulio Bondani ◽  
Robert Radu ◽  
Riccardo Muradore
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Household Appliances ◽  
Unsupervised Approach

Incipient fault diagnosis in power transformers by data-driven models with over-sampled dataset

2021 ◽  
Vol 201 ◽  
pp. 107519
Author(s):  
Sofia Moreira de Andrade Lopes ◽  
Rogério Andrade Flauzino ◽  
Ruy Alberto Corrêa Altafim
Keyword(s):  
Fault Diagnosis ◽  
Data Driven ◽  
Power Transformers ◽  
Incipient Fault ◽  
Incipient Fault Diagnosis

scholarly journals A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 4017 ◽  
Author(s):  
Haikun Shang ◽  
Junyan Xu ◽  
Zitao Zheng ◽  
Bing Qi ◽  
Liwei Zhang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Evidence Theory ◽  
Gas Analysis ◽  
Power Transformers ◽  
Support Vector ◽  
Dissolved Gas ◽  
Dissolved Gas Analysis ◽  
Diagnosis Method ◽  
Multiclass Support Vector Machine

Power transformers are important equipment in power systems and their reliability directly concerns the safety of power networks. Dissolved gas analysis (DGA) has shown great potential for detecting the incipient fault of oil-filled power transformers. In order to solve the misdiagnosis problems of traditional fault diagnosis approaches, a novel fault diagnosis method based on hypersphere multiclass support vector machine (HMSVM) and Dempster–Shafer (D–S) Evidence Theory (DET) is proposed. Firstly, proper gas dissolved in oil is selected as the fault characteristic of power transformers. Secondly, HMSVM is employed to diagnose transformer fault with selected characteristics. Then, particle swarm optimization (PSO) is utilized for parameter optimization. Finally, DET is introduced to fuse three different fault diagnosis methods together, including HMSVM, hybrid immune algorithm (HIA), and kernel extreme learning machine (KELM). To avoid the high conflict between different evidences, in this paper, a weight coefficient is introduced for the correction of fusion results. Results indicate that the fault diagnosis based on HMSVM has the highest probability to identify transformer faults among three artificial intelligent approaches. In addition, the improved D–S evidence theory (IDET) combines the advantages of each diagnosis method and promotes fault diagnosis accuracy.

scholarly journals A Novel Fault Diagnosis System on Polymer Insulation of Power Transformers Based on 3-stage GA–SA–SVM OFC Selection and ABC–SVM Classifier

Polymers ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1096 ◽  
Author(s):  
Xiaoge Huang ◽  
Yiyi Zhang ◽  
Jiefeng Liu ◽  
Hanbo Zheng ◽  
Ke Wang
Keyword(s):  
Artificial Intelligence ◽  
Fault Diagnosis ◽  
Diagnostic Accuracy ◽  
Diagnostic System ◽  
Gas Analysis ◽  
Diagnostic Methods ◽  
Power Transformers ◽  
Svm Classifier ◽  
Diagnosis System ◽  
Optimal Feature

Dissolved gas analysis (DGA) has been widely used in various scenarios of power transformers’ online monitoring and diagnoses. However, the diagnostic accuracy of traditional DGA methods still leaves much room for improvement. In this context, numerous new DGA diagnostic models that combine artificial intelligence with traditional methods have emerged. In this paper, a new DGA artificial intelligent diagnostic system is proposed. There are two modules that make up the diagnosis system. The two modules are the optimal feature combination (OFC) selection module based on 3-stage GA–SA–SVM and the ABC–SVM fault diagnosis module. The diagnosis system has been completely realized and embodied in its outstanding performances in diagnostic accuracy, reliability, and efficiency. Comparing the result with other artificial intelligence diagnostic methods, the new diagnostic system proposed in this paper performed superiorly.

Fault diagnosis of oil-immersed power transformers using common vector approach

2020 ◽  
Vol 184 ◽  
pp. 106346 ◽  
Author(s):  
Ali Kirkbas ◽  
Akif Demircali ◽  
Selim Koroglu ◽  
Aydin Kizilkaya
Keyword(s):  
Fault Diagnosis ◽  
Power Transformers ◽  
Vector Approach
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