| 引用本文: | 张 逸,林 楠,张良羽,等.基于多源数据的馈线谐波频谱估计方法[J].电力系统保护与控制,2024,52(11):63-73.[点击复制] |
| ZHANG Yi,LIN Nan,ZHANG Liangyu,et al.Harmonic spectrum estimation method for feeders based on multi-source data[J].Power System Protection and Control,2024,52(11):63-73[点击复制] |
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| 摘要: |
| 随着分布式电源和非线性负荷的投运规模不断增加，电网谐波污染愈加严重和复杂，有必要对各馈线谐波电流进行监测。但基于经济性考虑，对变电站所有馈线均进行谐波电流监测并不可行。针对上述问题，提出了基于多源数据的馈线谐波频谱估计方法。该方法基于电网现有电能质量监测系统、调度系统、营销业务应用系统等多源数据，只需对母线谐波电压和进线谐波电流开展监测，即可估计出变电站所有馈线的谐波电流情况。首先，根据馈线谐波电流的相量运算关系和功率关系构建馈线谐波频谱估计的目标函数。其次，基于多源数据利用改进粒子群算法求解目标函数，进而得到各馈线的各次谐波电流含有率及馈线谐波电流间的相角差，实现了各馈线谐波电流频谱的估计。最后，通过仿真算例和实测算例验证了方法的可行性。算例表明该方法仅需在变电站母线处安装一台电能质量监测终端，结合现有业务系统的基本电气数据等信息，可实现馈线谐波频谱的估计，有助于节约变电站监测装置的配置成本。 |
| 关键词: 谐波监测 电能质量 粒子群算法 多源数据 谐波频谱 |
| DOI：10.19783/j.cnki.pspc.231414 |
| 投稿时间：2023-11-04修订日期：2024-01-02 |
| 基金项目:国家自然科学基金项目资助(51777035) |
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| Harmonic spectrum estimation method for feeders based on multi-source data |
| ZHANG Yi1,LIN Nan1,ZHANG Liangyu1,ZHANG Yan2,LIU Bijie3 |
| (1. School of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China; 2. Marketing Service
Center of State Grid Jilin Electric Power Co., Ltd., Changchun 130600, China; 3. Ningde Power Supply
Company, State Grid Fujian Electric Power Co., Ltd., Ningde 352100, China) |
| Abstract: |
| With the increasing scale of distributed power generation and non-linear load, power grid harmonic pollution is becoming more and more serious. However, for economic reasons, it is unrealistic to monitor harmonic current of all feeders of a substation. Given this, this paper proposes a harmonic spectrum estimation method of feeder lines based on multi-source data. This method uses multi-source data obtained from the power quality monitoring, dispatching and marketing business application systems, and can estimate the harmonic current content of each feeder by only monitoring data of bus harmonic voltage and incoming line harmonic current. First, the objective function of spectrum estimation is constructed according to the phasor operation relationship of feeder harmonic current and power relationship. Then, the improved particle swarm optimization algorithm is used to analyze the objective function based on multi-source data, and after that the harmonic current content of each feeder and the phase angle difference between the harmonic currents of the feeders are obtained. Finally, the feasibility of the proposed method is verified by simulation and measurement examples. The examples show that this method only requires the installation of a power quality monitoring terminal at the busbar of the substation. By combining basic electrical data from existing business systems, it can estimate the harmonic spectrum of the feeder, helping to save the configuration cost of substation monitoring devices. |
| Key words: harmonic monitoring power quality particle swarm optimization multi-source data harmonic spectrum |
