基于梯度提升回归树-自训练贝叶斯优化支持向量机的光伏阵列故障诊断方法

曹岚; 周成功; 袁斌霞; 沈银钢

doi:doi:10.19912/j.0254-0096.tynxb.2024-0278

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基于梯度提升回归树-自训练贝叶斯优化支持向量机的光伏阵列故障诊断方法

更新时间：2025-12-02

- 基于梯度提升回归树-自训练贝叶斯优化支持向量机的光伏阵列故障诊断方法
- 基于梯度提升回归树-自训练贝叶斯优化支持向量机的光伏阵列故障诊断方法
- 太阳能学报 2025年46卷第6期页码：289-297
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-0278
  中图分类号： TK514
- 网络出版：2025-07-07，
  
  纸质出版：2025
- 稿件说明：
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曹岚, 周成功, 袁斌霞, 等. 基于梯度提升回归树-自训练贝叶斯优化支持向量机的光伏阵列故障诊断方法[J]. 太阳能学报, 2025,46(6):289-297.

曹岚, 周成功, 袁斌霞, et al. 基于梯度提升回归树-自训练贝叶斯优化支持向量机的光伏阵列故障诊断方法[J]. 2025, 46(6): 289-297.
曹岚, 周成功, 袁斌霞, 等. 基于梯度提升回归树-自训练贝叶斯优化支持向量机的光伏阵列故障诊断方法[J]. 太阳能学报, 2025,46(6):289-297. DOI： doi:10.19912/j.0254-0096.tynxb.2024-0278.

曹岚, 周成功, 袁斌霞, et al. 基于梯度提升回归树-自训练贝叶斯优化支持向量机的光伏阵列故障诊断方法[J]. 2025, 46(6): 289-297. DOI： doi:10.19912/j.0254-0096.tynxb.2024-0278.

摘要

提出一种基于梯度提升回归树-自训练贝叶斯优化支持向量机的光伏阵列故障诊断方法。实验结果表明

在无法直接测得开路电压的情况下

梯度提升回归树-自训练贝叶斯优化支持向量机算法预测开路电压并将其作为特征参数进行故障诊断的准确率为94.96%

运行时间为4.876 s

具有较好的准确率和较短的诊断时间。

Abstract

A fault diagnosis method for PV arrays

based on the gradient-boosted regression tree-self-training Bayesian optimized support vector machine

is proposed aiming at the difficulty of measuring open-circuit voltage directly by conventional monitoring systems. The experimental results show that in the case where the open-circuit voltage cannot be directly measured

the accuracy rate of the gradient-boosted regression tree-self-trained Bayesian optimized support vector machine algorithm in predicting open-circuit voltage and using it as a characteristic parameter for fault diagnosis is 94.96%

and a running time of 4.876 s

which has better accuracy and a shorter diagnosis time.

关键词

Keywords

references

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