A New Prediction Model for Transformer Winding Hotspot Temperature Fluctuation Based on Fuzzy Information Granulation and an Optimized Wavelet Neural Network

Li Zhang, Wenfang Zhang, Jinxin Liu, Tong Zhao, Liang Zou and Xinghua Wang
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Li Zhang: School of Electrical Engineering, Shandong University, Jinan 250061, China
Wenfang Zhang: School of Electrical Engineering, Shandong University, Jinan 250061, China
Jinxin Liu: School of Electrical Engineering, Shandong University, Jinan 250061, China
Tong Zhao: School of Electrical Engineering, Shandong University, Jinan 250061, China
Liang Zou: School of Electrical Engineering, Shandong University, Jinan 250061, China
Xinghua Wang: School of Electrical Engineering, Shandong University, Jinan 250061, China

Energies, 2017, vol. 10, issue 12, 1-13

Abstract: Winding hotspot temperature is the key factor affecting the load capacity and service life of transformers. For the early detection of transformer winding hotspot temperature anomalies, a new prediction model for the hotspot temperature fluctuation range based on fuzzy information granulation (FIG) and the chaotic particle swarm optimized wavelet neural network (CPSO-WNN) is proposed in this paper. The raw data are firstly processed by FIG to extract useful information from each time window. The extracted information is then used to construct a wavelet neural network (WNN) prediction model. Furthermore, the structural parameters of WNN are optimized by chaotic particle swarm optimization (CPSO) before it is used to predict the fluctuation range of the hotspot temperature. By analyzing the experimental data with four different prediction models, we find that the proposed method is more effective and is of guiding significance for the operation and maintenance of transformers.

Keywords: transformer winding; hotspot temperature; prediction model; fuzzy information granulation; wavelet neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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