| 引用本文: | 任洪男,王 禹,李 静,等.基于贝叶斯推断和谱聚类的户变关系动态辨识[J].电力系统保护与控制,2023,51(10):1-10.[点击复制] |
| REN Hongnan,WANG Yu,LI Jing,et al.Dynamic identification of household-transformer relationship based onBayesian inference and spectral clustering[J].Power System Protection and Control,2023,51(10):1-10[点击复制] |
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| 摘要: |
| 低压台区线路的日常维护以及改造升级会导致台区户变关系变动频繁。针对低压台区户变关系难以人工维护的问题，提出了一种基于贝叶斯推断和谱聚类的户变关系动态辨识方法。首先运用高斯核函数和IIR滤波器方程计算节点间电压数据相似度，然后利用贝叶斯推断构建节点间相似度时序矩阵，最后对其进行谱聚类进而实现户变关系的动态辨识。该方法能够沿时序方向根据最新时刻的电压数据进行户变关系的动态辨识，不仅能够对用户更改台区的情况做出快速响应，还能有效提高数据缺失和节点投入或退出台区情况下辨识的鲁棒性。以广东省惠州市4个台区为例进行的算例分析结果良好，验证了所提方法在工程实践中的可行性和可靠性。 |
| 关键词: 低压台区 户变关系 贝叶斯推断 谱聚类 动态辨识 |
| DOI：10.19783/j.cnki.pspc.221480 |
| 投稿时间：2022-09-15修订日期：2022-12-10 |
| 基金项目:国家自然科学基金项目资助(51907175)；浙江省自然科学基金项目资助(LY21F030002) |
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| Dynamic identification of household-transformer relationship based onBayesian inference and spectral clustering |
| REN Hongnan1，2,WANG Yu2,LI Jing2,CAI Hongda2,WEI Wei3 |
| (1. College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China;
2. School of Information and Electrical Engineering, Zhejiang University City College, Hangzhou 310015, China;
3. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China) |
| Abstract: |
| The relationship between households and transformers in low-voltage station areas is frequently changed because of routine maintenance, renovation and upgrading of the distribution network. Since it is difficult to maintain the household-transformer relationship in low-voltage station areas manually, a dynamic identification method for the relationship is proposed using Bayesian inference and spectral clustering technology. First, the Gaussian kernel function and IIR filter equation are used to calculate the voltage data similarity between nodes, and then Bayesian inference is used to construct the time series matrix of node similarity. Finally, the dynamic identification of the relationship can be implemented via the spectral clustering technology. The method can dynamically identify the household-transformer relationship using the latest voltage data along the time sequence direction, which not only can make a quick response to household changing the station area, but also can effectively improve the robustness of the identification in the case of missing data and node input or exit from the station areas. Taking four station areas in Huizhou City, Guangdong Province as an example, the results of the calculation example are good, and this verifies the feasibility and reliability of the proposed method for engineering practice. |
| Key words: low-voltage station areas household-transformer relationship Bayesian inference spectral clustering dynamic identification |
