基于ICOA-XGBoost的光伏阵列复合故障诊断研究

张子洵; 魏业文; 张轲钦; 方豪; 吴先用

doi:10.19912/j.0254-0096.tynxb.2024-0019

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (5) : 251-259. DOI: 10.19912/j.0254-0096.tynxb.2024-0019

基于ICOA-XGBoost的光伏阵列复合故障诊断研究

张子洵¹, 魏业文^1,2, 张轲钦¹, 方豪¹, 吴先用¹

作者信息 +

RESEARCH ON COMPOSITE FAULT DIAGNOSIS OF PHOTOVOLTAIC ARRAYS BASED ON ICOA-XGBoost

Zhang Zixun¹, Wei Yewen^1,2, Zhang Keqin¹, Fang Hao¹, Wu Xianyong¹

Author information +

文章历史 +

摘要

为提高光伏阵列复合故障诊断的准确率,提出一种基于改进长鼻浣熊算法（ICOA）优化极端梯度提升（XGBoost）的故障诊断方法。首先,通过分析光伏阵列在不同故障状态下的输出特性,构建一个9维故障特征向量作为模型的输入变量。然后,将结合改进Circle混沌映射、Levy飞行和t分布随机扰动的ICOA算法与麻雀搜索算法（SSA）、鲸鱼优化算法（WOA）和长鼻浣熊算法（COA）相比较,其在寻优能力、稳定性和收敛速度方面展现出优越性。随后,利用改进的ICOA算法优化XGBoost模型,有效解决了模型初始化参数的设置问题。实验结果显示,结合9维故障特征向量的ICOA-XGBoost模型在故障诊断精度上达到97.23%,优于其他对比模型,证实了所提方法的可行性和有效性。

Abstract

In order to enhance the accuracy of composite fault diagnosis in photovoltaic （PV） arrays, this study proposes a method utilizing an improved coati optimization algorithm （ICOA） to optimize eXtreme Gradient Boosting （XGBoost）. Initially, a 9-dimensional fault feature vector is constructed as the input variable of the model by analyzing the output characteristics of PV arrays under different fault states. Then, the ICOA algorithm, which integrates the improved Circle chaotic mapping, Levy flight and t-distributed stochastic perturbation, is compared with the sparrow search algorithm （SSA）, the whale optimization algorithm （WOA）, and the coati optimization algorithm （COA）, demonstrateing superiority in optimization ability, stability and convergence speed. Subsequently, the improved ICOA algorithm is used to optimize the XGBoost model, which effectively solves the problem of setting the initialization parameters of the model. The experimental evidence indicates that the ICOA-XGBoost model, which integrates 9-dimensional fault feature vectors, achieves a fault diagnosis accuracy of 97.23%, outperforming other models and confirming the proposed method’s feasibility and effectiveness.

导出引用

张子洵, 魏业文, 张轲钦, 方豪, 吴先用. 基于ICOA-XGBoost的光伏阵列复合故障诊断研究[J]. 太阳能学报. 2025, 46(5): 251-259 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0019

Zhang Zixun, Wei Yewen, Zhang Keqin, Fang Hao, Wu Xianyong. RESEARCH ON COMPOSITE FAULT DIAGNOSIS OF PHOTOVOLTAIC ARRAYS BASED ON ICOA-XGBoost[J]. Acta Energiae Solaris Sinica. 2025, 46(5): 251-259 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0019

中图分类号： TM615

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