Liu Jiefeng, Zheng Hanbo, Zhang Yiyi, Li Xin, Fang Jiake, Liu Yang, Liao Changyi, Li Yuquan, Zhao Junhui
Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, Guangxi, China.
National Demonstration Center for Experimental Electrical Engineering Education, Guangxi University, Nanning 530004, Guangxi, China.
Polymers (Basel). 2019 Jan 8;11(1):85. doi: 10.3390/polym11010085.
A solution for forecasting the dissolved gases in oil-immersed transformers has been proposed based on the wavelet technique and least squares support vector machine. In order to optimize the hyper-parameters of the constructed wavelet LS-SVM regression, the imperialist competition algorithm was then applied. In this study, the assessment of prediction performance is based on the squared correlation coefficient and mean absolute percentage error methods. According to the proposed method, this novel procedure was applied to a simulated case and the experimental results show that the dissolved gas contents could be accurately predicted using this method. Besides, the proposed approach was compared to other prediction methods such as the back propagation neural network, the radial basis function neural network, and generalized regression neural network. By comparison, it was inferred that this method is more effective than previous forecasting methods.
提出了一种基于小波技术和最小二乘支持向量机的油浸式变压器溶解气体预测方法。为了优化所构建的小波最小二乘支持向量机回归模型的超参数，随后应用了帝国主义竞争算法。在本研究中，基于平方相关系数和平均绝对百分比误差方法对预测性能进行评估。根据所提出的方法，将这种新方法应用于一个模拟案例，实验结果表明该方法能够准确预测溶解气体含量。此外，将所提出的方法与其他预测方法进行了比较，如反向传播神经网络、径向基函数神经网络和广义回归神经网络。通过比较推断，该方法比以前的预测方法更有效。
