Abstract
With the increasing access volume of source-load-storage and control equipment in new distribution networks, the system structure is becoming more and more complicated and the harmonics show time-varying random characteristics, which makes it extremely difficult to analyze the harmonic transfer law in distribution networks. To clarify the transmission range and transfer law of harmonics between nodes in a new distribution network and guide the assessment of distribution network carrying capacity under distributed photovoltaic (PV) integration, this paper proposes a harmonic transfer characteristic analysis method considering the time-varying correlation characteristics of nodes. Firstly, the harmonic transfer coefficients between distribution network nodes are defined based on the harmonic transfer relationship between nodes, and the distribution network carrying capacity under distributed PV integration is obtained based on the transfer coefficients. Then, the modified cosine similarity is used to cluster the distribution network nodes and select the key nodes for regional division to realize the dimensionality reduction of the harmonic complex transmission relationship of the distribution network. Finally, this paper establishes a RBF-ARX-based harmonic transfer analysis model to evaluate the time-varying transfer characteristics of harmonics in the system by constructing input-output features considering time-varying correlations of nodes. The experimental results show that the proposed harmonic transfer characteristic analysis method can deeply explore the time-varying correlation characteristics of the harmonics between nodes, and efficiently analyze the complex harmonic transfer characteristics in distribution networks.
| Translated title of the contribution | 计及节点时变美联特征的配电网谐波传递特性分析 |
|---|---|
| Original language | English |
| Pages (from-to) | 4491-4501 |
| Number of pages | 11 |
| Journal | Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering |
| Volume | 46 |
| Issue number | 11 |
| DOIs | |
| State | Published - 5 Jun 2026 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- data-driven
- harmonic transfer coefficients
- machine learning
- time-varying correlation features
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