基于多层极限学习机的电能质量扰动多标签分类算法

电力系统中电能质量扰动分类特征选择标准不统一、泛化能力差、分类效果与分类效率有待提高。为了解决这些问题,一方面,引入多层极限学习机自编码器,优化输入权重,完成电能质量扰动信号的特征提取。另一方面,引入多标签排位分类算法,充分考虑各标签之间的相关性,完成电能质量扰动的分类。基于两种算法,设计出基于多层极限学习机的多标签分类模型,并得到多层极限学习机的最优网络结构和多标签分类的最佳分类阈值。实验结果表明,所提方法适用于电能质量单一扰动和复合扰动的分类,改善了分类效果和分类效率,具有较高的分类精度、良好的抗噪能力和泛化能力。

Power quality disturbances multi-label classification algorithm based on a multi-layer extreme learning machine

In a power system, the characteristic selection criteria for power quality disturbance classification are not uniform. Generalization ability is weak, and the classification effect and efficiency need to be improved. In order to solve these problems, first a multi-layer extreme learning machine auto-encoder is used to optimize the input weights and extract the characteristics of electric power quality disturbances. Secondly a multi-label classification algorithm based on ranking is used to consider the correlation between labels and to classify various power quality disturbances. Combining the two algorithms, a multi-label classification model based on multi-level extreme learning machine is designed, and the optimal network structure of multi-level extreme learning machine and the optimal classification threshold of multi-label classification are obtained. The experimental results show that the proposed method can be applied to the classification of single and compound disturbances of power quality and improve the classification effect and efficiency with higher classification accuracy and excellent robustness and anti-noise ability. This work is supported by National Natural Science Foundation of China (No. 51275084) and 2020 Science and Technology Research Program of Henan Province (No. 202102210134).