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.
Highlights
The power transformer has always been the focus of monitoring and protection as an important piece of power transmission equipment
A transformer winding hotspot temperature (TWHT) prediction model was established based on the generalized regression neural network (GRNN) [7]
Since the study on the time series prediction and fluctuation range prediction of TWHT is insufficient, we propose a new prediction model for the fluctuation range of TWHT based on fuzzy information granulation and the chaotic particle swarm optimized wavelet neural network (FIG-chaotic particle swarm optimization (CPSO)-WNN)
Summary
The power transformer has always been the focus of monitoring and protection as an important piece of power transmission equipment. A method of predicting the transformer top temperature based on the T-S fuzzy model was proposed [6]. Since the study on the time series prediction and fluctuation range prediction of TWHT is insufficient, we propose a new prediction model for the fluctuation range of TWHT based on fuzzy information granulation and the chaotic particle swarm optimized wavelet neural network (FIG-CPSO-WNN). In this model, effective information is extracted from the original data using the FIG technique and is used to construct the wavelet neural network (WNN) prediction model. The experiments and data analysis demonstrate that the model is more effective than other prediction models
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