
Calculation of Fundamental Frequency Amplitude of Transformer Surface Vibration Based on ABC-ELM
Author(s) -
Yutao Lu,
Xin Wang,
Zhong Li
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1693/1/012221
Subject(s) - transformer , amplitude , vibration , extreme learning machine , fundamental frequency , voltage , control theory (sociology) , acoustics , engineering , computer science , artificial neural network , physics , electrical engineering , artificial intelligence , optics , control (management)
The fundamental frequency amplitude of transformer surface vibration is an important indicator for analyzing and diagnosing transformer faults. This article proposed a model for calculating the fundamental frequency amplitude of transformer surface vibration (ABC-ELM) based on Artificial Bee Colony (ABC) optimized Extreme Learning Machine (ELM). Considering the influence factors of transformer vibration, the ELM model was constructed by using the operating voltage, load current and oil temperature as input vectors, and the fundamental frequency amplitude of the transformer surface vibration as output vectors. The data measured by transformer was used for experiments, and the weights and hidden layer biases of the ELM input layer were optimized using ABC. Experimental results showed that ABC-ELM had higher calculation accuracy and smaller error fluctuations than ELM and BP neural networks, which proves the effectiveness of ABC-ELM model in calculating the fundamental frequency amplitude of transformer surface vibration.