Transformer Fault Diagnosis Based on RapidMiner and Modified ELM Algorithm

For transformer fault diagnosis, the three ratio method lacks of encoding, and artificial intelligence methods lack of anti-interference ability.Thus, a new method of transformer fault diagnosis based on RapidMiner and modified particle swarm optimization Extreme Learning Machine (RM-MPSO-ELM) is pr...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 1585; no. 1; pp. 12030 - 12036
Main Authors Zhong, Ji-you, Wei, Jin-xiao, Wu, Xiao-rong, Tang, Hao
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.07.2020
Subjects
Algorithms
Artificial intelligence
Artificial neural networks
Fault diagnosis
Machine learning
Parameter identification
Parameter modification
Particle swarm optimization
Physics
Support vector machines
Swarm intelligence
Transformers
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Summary:For transformer fault diagnosis, the three ratio method lacks of encoding, and artificial intelligence methods lack of anti-interference ability.Thus, a new method of transformer fault diagnosis based on RapidMiner and modified particle swarm optimization Extreme Learning Machine (RM-MPSO-ELM) is proposed. Firstly, RapidMiner picks out the most relevant input variables of the transformer fault. then, using the modified particle swarm algorithm to optimize the parameters for Extreme Learning Machine. Finally, using the ELM to identify the potential of transformer fault, the diagnostic performance of IEC three ratio method, support vector machine (SVM) method and different combinations of ELM algorithm are also compared. The results show that the proposed method achieves higher diagnosis precision.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1585/1/012030