基于特征分析与MGAT-TCN模型的非侵入式户用光伏辨识

倪旖旎; 邬峥涛; 李子晨; 夏杨红

doi:10.19912/j.0254-0096.tynxb.2024-2387

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基于特征分析与MGAT-TCN模型的非侵入式户用光伏辨识

更新时间：2026-07-21

- 基于特征分析与MGAT-TCN模型的非侵入式户用光伏辨识
- Vol. 47, Issue 5, Pages: 683-696(2026)
- 作者机构：
- 作者简介：
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- DOI：10.19912/j.0254-0096.tynxb.2024-2387
  CLC： TM714
- Online First：04 June 2026，
  
  Published：2026
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倪旖旎, 邬峥涛, 李子晨, et al. 基于特征分析与MGAT-TCN模型的非侵入式户用光伏辨识[J]. 2026, 47(5): 683-696.
DOI：

倪旖旎, 邬峥涛, 李子晨, et al. 基于特征分析与MGAT-TCN模型的非侵入式户用光伏辨识[J]. 2026, 47(5): 683-696. DOI： 10.19912/j.0254-0096.tynxb.2024-2387.

摘要

该文针对未加装单独监测装置的表后小型分布式光伏设备

以电表净负荷数据为基础

提出一种基于特征分析和多头图注意力网络和时间卷积网络（MGAT-TCN）模型的非侵入式户用光伏辨识方法。首先

在对光伏设备并网及状态切换过程仿真分析的基础上

提出基于多功能复合滑动窗的两阶段CUSUM事件检测算法对光伏状态切换与并网行为进行实时定位;其次

提出基于MGAT-TCN光伏辨识模型

该方法能通过多头图注意力网络有效处理光伏设备相互耦合的多维特征

从而提高辨识准确率与效率。最后

方法于自搭建非侵入式实验平台进行测试。结果表明

所提事件检测算法能够准确定位光伏行为

且相较于传统事件检测算法减少了至少0.3 s的时间定位误差;同时

所提的MGAT-TCN辨识模型对光伏设备的辨识准确率高于典型的神经网络方法至少2.66个百分点

且可达到96%以上。

Abstract

This paper focuses on behind-the-meter PV monitoring and proposes a non-intrusive residential PV identification method based on feature analysis and a hybrid model combining a Multi-Graph Attention Network with a Temporal Convolutional Network （MGAT-TCN）. First

the grid-connection and state-switching characteristics of household two-stage PV devices are analyzed to construct and rank a comprehensive feature set. Next

a two-stage cumulative sum （CUSUM） event detection algorithm based on a multifunctional composite sliding window

is proposed to detect PV state-switching and grid-connecting behaviors. Subsequently

the MGAT-TCN model is employed to accurately identify these behaviors using an optimized subset of features. Finally

the accuracy and real-time performance of the proposed method are validated on a self-built non-intrusive experimental platform. The results demonstrate that the proposed event detection algorithm accurately locates PV behaviors

reducing time-localization errors by at least 0.3s compared to traditional algorithms. Furthermore

the MGAT-TCN model achieves an identification accuracy of over 96%

outperforming typical neural network methods by at least 2.66%.

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references

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