Calculation of Fundamental Frequency Amplitude of Transformer Surface Vibration Based on ABC-ELM

Abstract

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.