| 引用本文: | 许立武,李开成,罗奕,等.基于不完全S变换与梯度提升树的电能质量复合扰动识别[J].电力系统保护与控制,2019,47(6):24-31.[点击复制] |
| XU Liwu,LI Kaicheng,LUO Yi,et al.Classification of complex power quality disturbances based on incomplete S-transform and gradient boosting decision tree[J].Power System Protection and Control,2019,47(6):24-31[点击复制] |
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| 摘要: |
| 针对电能质量复合扰动中特征选择困难和分类准确率不高的问题，提出基于不完全S变换和梯度提升树的特征选择和分类器构建方法。首先通过选取特定频率的不完全S变换得到扰动的时频矩阵。再从时频矩阵中提取53种原始特征量，并基于梯度提升树对各个特征的重要性进行度量，选取重要特征。最后根据选取的特征集训练和构建梯度提升树，得到扰动分类器。仿真实验表明，对于包括8种复合扰动在内的共17种扰动类型，该方法的分类准确率高于CART决策树、随机森林(RF)、多层感知机(MLP)等现有方法。不同噪声条件下的分类结果表明，该方法具有良好的抗噪性能和算法鲁棒性，展现出良好的应用前景。 |
| 关键词: 电能质量 扰动识别 梯度提升树 不完全S变换 特征选择 |
| DOI：10.7667/PSPC180414 |
| 投稿时间：2018-04-13修订日期：2018-06-07 |
| 基金项目:国家自然科学基金项目资助(51277080) |
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| Classification of complex power quality disturbances based on incomplete S-transform and gradient boosting decision tree |
| XU Liwu,LI Kaicheng,LUO Yi,XIAO Xiangui,ZHANG Chan,CAI Delong |
| (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China) |
| Abstract: |
| In order to solve the problem of feature selection and classification accuracy in complex power quality disturbances recognition, a method of feature selection and classifier construction based on incomplete S-transform and Gradient Boosting Decision Tree (GBDT) is proposed. Firstly, the time-frequency matrixes of the disturbances are obtained by the incomplete S-transform at the specific frequencies. Then the original feature set of 53 features is extracted from the time-frequency matrix, and the importance level of each feature is measured by GBDT to select optimal features. Finally, the disturbance classifier is trained and constructed by GBDT based on the selected feature set. The simulation results show that the classification accuracy of the proposed method is higher than the CART, the Random Forest (RF), the Multilayer Perceptron (MLP), and so on, for a total of 17 disturbance classes including 8 classes of combined disturbances. Moreover, the classification results in different noise conditions indicate that the method has good anti-noise performance and algorithm robustness, which shows good prospects for application. This work is supported by National Natural Science Foundation of China (No. 51277080). |
| Key words: power quality disturbances classification gradient boosting decision tree (GBDT) incomplete S-transform feature selection |
