一种基于CatBoost优化的光伏阵列故障诊断模型

彭自然; 许怀顺; 肖伸平

doi:10.12263/DZXB.20240236

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一种基于CatBoost优化的光伏阵列故障诊断模型

学术论文 | 更新时间：2025-12-24

- 一种基于CatBoost优化的光伏阵列故障诊断模型
- A CatBoost Optimization-Based Fault Diagnosis Model for Photovoltaic Arrays
- 电子学报 2024年52卷第7期页码：2418-2428
- 作者机构：
  
  1.湖南工业大学电气与信息工程学院，湖南株洲 412007
  2.湖南省电传动控制与智能装备重点实验室，湖南株洲 412007
- 作者简介：
  
  [ "彭自然男，1969年10月出生于湖南省益阳市.现为湖南工业大学电气与信息工程学院副教授、硕士生导师.研究方向为人工智能、信号处理、智能检测仪表、智能移动终端等方面.E-mail: pengziran@hut.edu.cn" ]
  [ "许怀顺男，1999年7月出生于山东省临沂市.现为湖南工业大学电气与信息工程学院硕士研究生.研究方向为人工智能、机器学习、光伏故障诊断等方面.E-mail: 1106238410@qq.com" ]
  [ "肖伸平男，1965年5月于湖南省永州市，现为湖南工业大学电气与信息工程学院教授、博士生导师.研究方向为时滞系统鲁棒控制理论及应用、电力时滞系统稳定性分析、工业网络控制、智能控制、过程控制等方面.E-mail: xsp@hut.edu.cn" ]
- 基金信息：
  
  国家重点研发计划基金(2019YFE0122600);湖南省教育厅重点科研项目(22A0423);湖南省自科基金(2023JJ60267;2022JJ50073)
- DOI：10.12263/DZXB.20240236
  中图分类号： TM615;TP18
- 收稿：2024-03-14，
  
  修回：2024-06-13，
  
  纸质出版：2024-07-25
- 稿件说明：
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彭自然, 许怀顺, 肖伸平. 一种基于CatBoost优化的光伏阵列故障诊断模型[J]. 电子学报, 2024, 52(07): 2418-2428.

PENG Zi-ran, XU Huai-shun, XIAO Shen-ping. A CatBoost Optimization-Based Fault Diagnosis Model for Photovoltaic Arrays[J]. Acta Electronica Sinica, 2024, 52(07): 2418-2428.
彭自然, 许怀顺, 肖伸平. 一种基于CatBoost优化的光伏阵列故障诊断模型[J]. 电子学报, 2024, 52(07): 2418-2428. DOI：10.12263/DZXB.20240236

PENG Zi-ran, XU Huai-shun, XIAO Shen-ping. A CatBoost Optimization-Based Fault Diagnosis Model for Photovoltaic Arrays[J]. Acta Electronica Sinica, 2024, 52(07): 2418-2428. DOI：10.12263/DZXB.20240236

摘要

大部分光伏电站地处偏僻、地形复杂的区域，受到外界环境的影响，易发生各种故障.而传统的光伏阵列故障诊断方法存在精度不高以及光伏数据利用率低等问题.针对以上问题，本文先是通过引入Levy飞行策略和步长因子动态调整策略改进麻雀搜索算法（Sparrow Search Algorithm，SSA），降低SSA算法陷入局部最优的风险，提升SSA算法的寻优能力.然后采用改进的Levy步长调整麻雀搜索算法（Levy Adjustment Sparrow Search Algorithm，LASSA）对CatBoost模型关键超参数进行寻优，提出了一种基于CatBoost并以LASSA为优化策略的光伏阵列故障诊断模型LASSA-CatBoost，以实现光伏阵列的短路、开路、老化和阴影遮挡故障的精确诊断.实验结果表明，LASSA-CatBoost模型的故障诊断准确率为99.7%，相较于优化前的CatBoost模型，准确率提高了3.6%.与现有的光伏阵列故障诊断模型相比，LASSA-CatBoost模型的准确性和稳定性更高.

Abstract

Most of the photovoltaic power stations are located in remote areas with complex terrain

which are affected by the external environment and prone to various faults. The traditional PV array fault diagnosis methods have the problems of low accuracy and low utilization of PV data. Aiming at the above problems

in this paper

we first improve the sparrow search algorithm (SSA) by introducing the Levy flight strategy and the dynamic adjustment strategy of the step factor to reduce the risk of the SSA algorithm falling into the local optimum and improve the optimization ability of the SSA algorithm. Then the improved levy adjustment sparrow search algorithm (LASSA) is used to optimize the key hyperparameters of the CatBoost model

and a photovoltaic array fault diagnosis model LASSA-based on CatBoost and using LASSA as the optimization strategy is proposed. CatBoost for accurate diagnosis of short-circuit

open-circuit

aging and shadow masking faults in PV arrays. The experimental results show that the fault diagnosis accuracy of the LASSA-CatBoost model is 99.7%

which is 3.6% higher compared to the CatBoost model before optimization. Compared with the existing PV array fault diagnosis models

the LASSA-CatBoost model has higher accuracy and stability.

关键词

Keywords

references

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