A novel swarm budorcas taxicolor optimization-based multi-support vector method for transformer fault diagnosis.

Abstract

To address the challenge of low recognition accuracy in transformer fault detection, a novel method called swarm budorcas taxicolor optimization-based multi-support vector (SBTO-MSV) is proposed. Firstly, a multi-support vector (MSV) model is proposed to realize multi-classification of transformer faults based on dissolved gas data. Then, a swarm budorcas taxicolor optimization (SBTO) algorithm is proposed to iteratively search the optimal model parameters during MSV model training, so as to obtain the most effective transformer fault diagnosis model. Experimental results on the IEC TC 10 dataset demonstrate that the SBTO-MSV method markedly outperforms traditional methods and state-of-the-art machine learning algorithms with the best average accuracy of 98.1%, effectively highlighting the superior classification performance of SBTO-MSV model and excellent parameter searching ability of SBTO algorithm. Additionally, validation on the collected dataset and UCI dataset further confirms the excellent classification performance and generalization ability of the SBTO-MSV model. This advancement provides robust technical support for improving transformer fault diagnosis and ensuring the reliable operation of power systems.