基于DOG -LSSVM回归和人工蜂群的电力变压器油溶解气体预测

Yiyi Zhang, Liuliang Zhao, Jiake Fang, Jian Jiao, Changyi Liao, Xin Li
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引用次数: 0

摘要

为了准确预测变压器的发展趋势和故障早期趋势,提出了一种基于DOG小波核函数和基于最小二乘向量机回归(LSSVR)的人工蜂群算法(ABC)预测变压器油中溶解气体含量的方法。本文首先利用ABC法对LSSVR的参数进行优化,然后利用DOG法构建LSSVR模型,最后通过测量平均绝对误差百分比(MAPE)和平方相关系数$(r^{2})$对预测性能进行评价,证明了该方法的准确性和有效性。
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Forecasting of Dissolved Gases in Power Transformer Oil Based on DOG -LSSVM Regression and Artificial Bee Colony
In order to accurately forecast the development trend of transformers and the trend in the early stage of faults, this paper provides a method to predict the dissolved gas content in transformer oil with DOG wavelet kernel function and artificial bee colony algorithm (ABC) based on least squares vector machine regression (LSSVR). This paper first optimizes the parameters of LSSVR by ABC, then constructs the LSSVR model with the DOG, finally evaluates the prediction performance based on the measurement of the average absolute error percentage (MAPE) and the square correlation coefficient $(r^{2})$ to prove the accuracy and effectiveness of this method.
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