基于VMD-COMTBO-BiLSTM光伏阵列故障诊断研究

史洪岩; 李尚达; 潘多涛; 王国刚

doi:doi:10.19912/j.0254-0096.tynxb.2024-1701

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基于VMD-COMTBO-BiLSTM光伏阵列故障诊断研究

更新时间：2026-03-06

- 基于VMD-COMTBO-BiLSTM光伏阵列故障诊断研究
- Vol. 47, Issue 2, Pages: 18-29(2026)
- 作者机构：
- 作者简介：
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1701
  CLC： TP615
- Online First：05 March 2026，
  
  Published：05 March 2026
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史洪岩, 李尚达, 潘多涛, et al. 基于VMD-COMTBO-BiLSTM光伏阵列故障诊断研究[J]. 2026, 47(2): 18-29.
DOI：

史洪岩, 李尚达, 潘多涛, et al. 基于VMD-COMTBO-BiLSTM光伏阵列故障诊断研究[J]. 2026, 47(2): 18-29. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1701.

摘要

为提高神经网络对光伏阵列故障诊断故障精度

提出一种通过仿真获取数据并进行变分模态分解（VMD）

并通过改进的登山优化算法（COMTBO）

用于双向长短期记忆（BiLSTM）网络的光伏阵列故障诊断。首先

在Matlab/Simulink软件环境下搭建光伏阵列模型

进行数据特征分析

模拟光伏阵列故障

并引入更多的故障特征组成故障数据

使用VMD将故障数据分解成多个分量

并选出最大的能量对应的模态分量作为输入量。然后

提出融合混沌映射、鱼鹰优化算法（OOA）和柯西变异策略的COMTBO算法来实现对BiLSTM的参数寻优

构建VMD-COMTBO-BiLSTM神经网络模型

使光伏阵列故障诊断率达98%

在实际实验中准确率也达99%以上。

Abstract

In order to improve the fault diagnosis accuracy of neural networks for photovoltaic arrays

a method for photovoltaic array fault diagnosis is proposed to obtain data through simulation and perform VMD decomposition

and to use an improved cauchy-osprey-mapping team-based optimization（COMTBO） for bidirectional long short-term memory（BiLSTM） network. Firstly

a photovoltaic array model is built in Matlab/Simulink software environment for data feature analysis

simulating photovoltaic array faults

and introducing more fault features to form fault data. Variational mode decomposition （VMD） is used to decompose the fault data into multiple components

and the modal component corresponding to the maximum energy is selected as the input quantity. Then

a COMTBO algorithm that integrates chaotic mapping

osprey optimization algorithm （OOA）

and cauchy mutation strategy is proposed to optimize the parameters of BiLSTM. A VMD-COMTBO BiLSTM neural network model is constructed to achieve a fault diagnosis rate of 98% for photovoltaic arrays. In actual experiments

the accuracy rate also reaches over 99%.

关键词

Keywords

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