| 引用本文: | 汪 颖,陈春林,肖先勇.电压暂降源异质堆叠集成学习识别法[J].电力系统保护与控制,2021,49(15):1-8. |
| WANG Ying,CHEN Chunlin,XIAO Xianyong.Heterogeneous stacking integrated learning identification method for voltage sag sources[J].Power System Protection and Control,2021,49(15):1-8 |
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| 摘要: |
| 电压暂降源分类识别存在可获得信息不完备的问题。针对现有单一识别法弱学习特点和组合识别法一致性强的问题，提出一种基于异质堆叠集成学习的暂降源识别方法，提升识别模型的泛化能力和鲁棒性。把线路故障分为普通故障和雷击故障，以10类单一电压暂降源的识别为目标，选取9个表征特征差异的波形统计参数，构建27维识别特征向量。引入堆叠集成算法，以5种差异性强的单一识别法为基分类器，用随机森林法作元分类器，建立异质堆叠集成识别模型。通过PSCAD仿真数据和实测数据验证，并与现有6种识别法比较，结果表明，该方法识别精度高，噪声鲁棒性良好，具有良好的工程实用性。 |
| 关键词: 电压暂降源识别 异质性 差异性 堆叠集成 识别精度 噪声鲁棒性 |
| DOI：DOI: 10.19783/j.cnki.pspc.201263 |
| 分类号: |
| 基金项目:国家自然科学基金项目资助(51807126) |
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| Heterogeneous stacking integrated learning identification method for voltage sag sources |
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WANG Ying, CHEN Chunlin, XIAO Xianyong
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(College of Electrical Engineering, Sichuan University, Chengdu 610065, China)
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| Abstract: |
| The classification and identification of voltage sag sources has the problem of having incomplete information. Given the weak learning characteristics of the existing single identification method and the strong consistency of the combined identification method, a sag source identification method based on heterogeneous stacking ensemble learning is proposed to improve the generalization ability and robustness of the recognition model. Line faults are subdivided into common faults and lightning faults, and ten types of single voltage sag sources are used as the identification target. Nine waveform statistical parameters that can characterize the differences are selected to construct a 27-dimension recognition vector. The stacking ensemble algorithm is introduced, five highly differentiated single recognition methods are used as base-classifiers, and random forest is selected as the meta-classifier to establish a heterogeneous stacking ensemble recognition model. Through the verification of a PSCAD simulation model and measured data, and comparison with six typical sag source identification methods, it is shown that the proposed method has high identification accuracy and good anti-noise performance with good engineering value.
This work is supported by the National Natural Science Foundation of China (No. 51807126). |
| Key words: voltage sag source identification heterogeneity difference Stacking integration identification accuracy noise robustness |
