| 引用本文: | 邬程欢,贺 民,龚明波,等.基于极限区间与改进PCA-SOM的电气运行参数对
专变健康影响的量化评价方法[J].电力系统保护与控制,2021,49(17):101-108.[点击复制] |
| WU Chenghuan,HE Min,GONG Mingbo,et al.Quantitative evaluation based on limit interval and improved PCA-SOM of electrical operatingparameter health status of a special transformer[J].Power System Protection and Control,2021,49(17):101-108[点击复制] |
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| 摘要: |
| 专用变压器作为工业园区电力系统的重要组成部分，对园区内电网的稳定性与安全性有着十分重要的影响。针对专变实时电气运行参数的波动性与异质性，提出了一种基于极限区间的专变数据标准化方法。考虑了专变电气运行参数的动态集结方式与评价特征裕度，为后续专变健康量化评价方法提供完整且公平的评价信息。为了将专变电气运行参数对专变自身健康的影响进行清晰量化，将专家经验评价与神经网络相结合提出了一种基于极限区间与改进PCA-SOM的电气运行参数对专变健康影响的量化评价方法。在自组织映射神经网络的基础上，通过引入主成分分析法中的主成分贡献度对目标函数进行修正，并且结合各神经元的专家经验评分得到专用变压器的健康状态实时量化评分。最后采用“浙电云”大数据平台上采集的专变数据进行实验。结果表明该方法相比其他方法有着更好的评价效果，更能准确反映专变的实时电气运行参数的变化对专变健康状况的影响。 |
| 关键词: 自组织映射神经网络 主成分分析法 极限区间 专用变压器 健康状况实时评估 |
| DOI：DOI: 10.19783/j.cnki.pspc.201268 |
| 投稿时间：2020-10-20修订日期：2020-10-20 |
| 基金项目:国家重点研发计划项目资助(2017YFA0700300);
浙江省重点研发计划项目资助(2021C01112) |
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| Quantitative evaluation based on limit interval and improved PCA-SOM of electrical operatingparameter health status of a special transformer |
| WU Chenghuan,HE Min,GONG Mingbo,LIN Yinghe,PAN Guobing,OUYANG Jing |
| (1. State Grid Zhejiang Power Co., Ltd. Ningbo Power Supply Company, Ningbo 315000, China; 2. Zhejiang Huayun
Information Technology Co., Ltd., Hangzhou 310012, China; 3. Zhejiang Provincial Key Laboratory of Special
Purpose Equipment and Advanced Manufacturing Technology, Ministry of Education,
Zhejiang University of Technology, Hangzhou 310014, China) |
| Abstract: |
| As a component of a power system, the special transformer has a very important impact on the stability and safety. Given the volatility and heterogeneity of real-time data of special transformers, a standardization method based on limit intervals is proposed. Considering the dynamic aggregation and limit interval of the real-time data of the special transformer, this method can provide complete information for quantitative evaluation. To clearly quantify the influence of specific electrical operation parameters on their own health, we combine expert experience evaluation with a neural network to propose a quantitative evaluation method based on limit interval and improved Self-Organizing Map-Principal Component Analysis (PCA-SOM) electrical operation parameters. Based on the SOM neural network, this method uses the contribution rate of each principal component in PCA to modify the objective function, and combines the artificial evaluation of each neuron to calculate a real-time evaluation of the health status of the dedicated transformer. Finally, the experiment uses real-time data from a special transformer on the “Zhedian Cloud” big data system. The results show that the method in this paper has higher accuracy than other methods and can better reflect the real-time health status of the special transformer.
This work is supported by the National Key Research and Development Program of China (No. 2017YFA0700300) and the Key Research and Development Program of Zhejiang Province (No. 2021C01112). |
| Key words: self-organizing map neural network principal component analysis limit intervals special transformer real-time evaluation of health status |
