A classification and prediction model with the sparrow search-probabilistic neural network algorithm for transformer fault diagnosis Online publication date: Wed, 08-May-2024
by Ling Hu; Lanlan Yin; Feng Mo; Zhixun Liang; Zhong Ruan; Yuting Wang
International Journal of Sensor Networks (IJSNET), Vol. 44, No. 4, 2024
Abstract: We present a fault prediction model for transformers to improve the accuracy of transformer fault (TF) prediction. The model is predicated on a probabilistic neural network (PNN) that is optimised using three gas ratios with the help of the sparrow search algorithm (SSA). First, we monitor real-time gas concentrations and calculate the essential gas ratio by installing a smart gas sensor inside the transformer. The PNN is optimised by using the SSA algorithm. Subsequently, a mapping model between gas ratios and fault types is established. At last, we assess the model's prediction performance by calculating the mean square error. The results we got demonstrate that this method achieves a prediction accuracy of 90%, which is superior to the back propagation (BP) network, the k-nearest neighbour (KNN), and the support vector machine (SVM). This research offers an efficient and dependable approach for TF prediction.
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