基于改进K-均值和CNN-LSTM的分布式光伏集群划分与等值建模

周涛; 王帅; 倪俊; 徐超; 徐妍

doi:10.19912/j.0254-0096.tynxb.2025-0728

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基于改进K-均值和CNN-LSTM的分布式光伏集群划分与等值建模

更新时间：2026-10-15

- 基于改进K-均值和CNN-LSTM的分布式光伏集群划分与等值建模
- Vol. 47, Issue 9, Pages: 269-279(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.19912/j.0254-0096.tynxb.2025-0728
  CLC： TK511
- Online First：10 October 2026，
  
  Published：2026
- 稿件说明：
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周涛, 王帅, 倪俊, et al. 基于改进K-均值和CNN-LSTM的分布式光伏集群划分与等值建模[J]. 2026, 47(9): 269-279.
DOI：

周涛, 王帅, 倪俊, et al. 基于改进K-均值和CNN-LSTM的分布式光伏集群划分与等值建模[J]. 2026, 47(9): 269-279. DOI： 10.19912/j.0254-0096.tynxb.2025-0728.

摘要

针对目前分布式光伏多机等值建模存在的建模复杂与精度低等问题

该文提出一种融合改进K-均值和卷积神经网络-长短时记忆神经网络（CNN-LSTM）的分布式光伏集群智能划分与等值建模方法。首先

选取包括输出电压、输出电流、并网点有功及无功功率

以及逆变器外环控制参数在内的多维度特征作为分群依据

通过引入手肘法确定分布式光伏集群的最优聚类数目。采用一种利用动态时间弯曲距离计算距离矩阵优化初始聚类中心的K-均值聚类算法

实现了对分布式光伏集群的智能划分

有效降低了分群的主观性与复杂性。该文结合卷积神经网络（CNN）与长短期记忆网络（LSTM）的优势

CNN用于捕捉空间维度特征

而LSTM则提取时间序列数据的动态特性

从而构建一个既能反映空间分布又能精准预测时间演变的CNN-LSTM分布式光伏集群等效模型。最后在PSCAD中仿真对比聚类等值模型与该文方法的分布式光伏集群等效模型的精度

该文采用模型在3种不同扰动下提升有功功率等值精度1.30、0.32与5.89个百分点

提升无功功率等值精度0.85、0.80与4.91个百分点

验证了该方法在提高分布式光伏系统建模效率与精度方面的有效性与实用性

为大规模分布式光伏并网系统的分析与优化提供了技术参考。

Abstract

The distributed nature and uncertainty of distributed photovoltaic（PV） systems pose new challenges to their equivalent modeling methods. In response to the existing issues of complex modeling and low accuracy in multi-machine equivalent modeling of distributed PV

this paper proposes an intelligent partitioning and equivalent modeling method for distributed PV clusters that integrates improved -means and CNN-LSTM. Firstly

multi-dimensional features including output voltage

output current

active power and reactive power at the point of interconnection

and outer-loop control parameters of inverters are selected as the basis for clustering. The optimal number of clusters for distributed PV clusters is determined by introducing the elbow method. A -means clustering algorithm that uses dynamic time warping distance to calculate the distance matrix and optimize the initial cluster centers is adopted to achieve intelligent partitioning of distributed PV clusters

effectively reducing the subjectivity and complexity of clustering. This paper combines the advantages of CNN and LSTM

where CNN is used to capture the spatial dimension features in distributed PV data

and LSTM focuses on extracting the dynamic characteristics of time series data

thereby constructing a CNN-LSTM equivalent model for distributed PV clusters that can reflect spatial distribution and accurately predict temporal evolution. Finally

the accuracy of the equivalent model of the clustering method and the distributed PV cluster equivalent model proposed in this paper is compared through simulation in PSCAD. The proposed model improves the equivalent accuracy of active power by 1.3

0.32

and 5.89 percentage points

and the equivalent accuracy of reactive power by 0.85

0.80

and 4.91 percentage points under three different disturbances

verifying the effectiveness and practicality of this method in improving the modeling efficiency and accuracy of distributed PV systems. This provides a technical reference for the analysis and optimization of large-scale distributed PV grid-connected systems.

关键词

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