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基于优化极限学习机的电压暂降源识别方法
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四川大学电气工程学院,四川省 成都市 610065

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基金项目:

国家自然科学基金资助项目(51807126);四川大学专职博士后研发基金资助项目(2019SCU12003)。


Recognition Method of Voltage Sag Source Based on Optimized Extreme Learning Machine
Author:
Affiliation:

College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Fund Project:

This work is supported by National Natural Science Foundation of China (No. 51807126) and Full-time Postdoctoral Research and Development Fund of Sichuan University (No. 2019SCU12003).

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    摘要:

    准确识别电压暂降源对暂降责任分摊和治理决策至关重要。文中提出一种基于优化极限学习机的电压暂降源识别方法。通过直接提取电压暂降波形的时域特征和经S变换提取能量熵和奇异熵2种时频域特征,构建基于时域和时频域的特征向量,弥补现有方法仅采用时频变换提取特征,可能丢失仅存在于时域内的重要信息而影响识别精度的不足。针对极限学习机输入权值和隐含层偏置随机产生的不足,采用遗传算法对其进行优化,构建优化极限学习机模型,解决利用模式识别存在模型复杂和耗时较长,难以实现快速识别的问题。应用仿真数据和实测数据验证了所提特征向量和优化极限学习机模型的有效性;并与其他方法相比,证明所提模型简单、训练和分类识别速度快,识别精度更高,适用于边缘计算,可实现电压暂降源的快速准确识别。

    Abstract:

    It is important to accurately identify voltage sag sources for sag responsibility allocation and mitigation decision-making. This paper proposes a recognition method of voltage sag sources based on the optimized extreme learning machine (ELM). The time-domain features are directly extracted from the voltage sag waveforms, and the time-frequency domain features (include energy entropy and singular entropy) are extracted by S-transform. Then the feature vectors are built based on the time-domain and time-frequency domain features. The feature vectors make up for the shortcomings that the existing methods only use time-frequency transform to extract features and may lose the important information only existing in the time-domain, which will affect the recognition accuracy. The genetic algorithm is used to optimize the input weight and hidden layer bias of the ELM. The optimized ELM model is proposed to solve the problem of the pattern recognition, whose model is complex and time-consuming. The validity of the proposed feature vectors and optimized ELM model are verified by the simulated data and the measured data. Compared with other methods, it is proved that the proposed model is simple and fast in training and classification, and has higher recognition accuracy. It is suitable for edge calculation and can identify the voltage sag sources accurately and fast.

    表 2 Table 2
    图1 电压暂降分压模型Fig.1 Voltage divider model for voltage sag
    图2 基于GA-ELM的电压暂降源识别流程图Fig.2 Flow chart of voltage sag source recognition based on GA-ELM
    图3 电压暂降仿真模型Fig.3 Simulation models of voltage sag
    图4 实测电压暂降波形Fig.4 Measured waveforms of voltage sag
    图 短路故障引起的电压暂降波形Fig. Voltage sag waveform caused by short circuit fault
    图 变压器投运引起的暂降波形Fig. Voltage sag waveform caused by transformer operation
    图 感应电动机启动引起的暂降波形Fig. Voltage sag waveform caused by the induction motor starting
    图 ELM网络结构Fig. ELM network structure
    图 适应度进化曲线Fig. Fitness evolution curve
    表 1 识别结果对比Table 1 Comparison of recognition results
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引用本文

汪颖,王欢,张姝.基于优化极限学习机的电压暂降源识别方法[J/OL].电力系统自动化,http://doi.org/10.7500/AEPS20190809004.
WANG Ying,WANG Huan,ZHANG Shu.Recognition Method of Voltage Sag Source Based on Optimized Extreme Learning Machine[J/OL].Automation of Electric Power Systems,http://doi.org/10.7500/AEPS20190809004.

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  • 收稿日期:2019-08-09
  • 最后修改日期:2020-03-02
  • 录用日期:2019-12-04
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