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
查看参考文献21篇
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文摘
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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-Cat-Boost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. |
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其他语种文摘
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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. |
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来源
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电子学报
,2024,52(7):2418-2428 【核心库】
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DOI
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10.12263/DZXB.20240236
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关键词
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光伏阵列
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故障诊断
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I-V特性曲线
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CatBoost
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Levy adjustment sparrow search algorithm
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地址
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1.
湖南工业大学电气与信息工程学院, 湖南, 株洲, 412007
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湖南省电传动控制与智能装备重点实验室, 湖南省电传动控制与智能装备重点实验室, 湖南, 株洲, 412007
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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0372-2112 |
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学科
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电工技术;自动化技术、计算机技术 |
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基金
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国家重点研发计划基金
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湖南省教育厅重点项目
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湖南省自然科学基金
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文献收藏号
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CSCD:7805471
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参考文献 共
21
共2页
|
|
1.
Firth S K. A simple model of PV system performance and its use in fault detection.
Solar Energy,2010,84(4):624-635
|
CSCD被引
6
次
|
|
|
|
|
2.
Chen X. A novel series arc fault detection method for photovoltaic system based on multi-input neural network.
International Journal of Electrical Power & Energy Systems,2022,140:108018
|
CSCD被引
2
次
|
|
|
|
|
3.
Chine W. Fault diagnosis in photovoltaic arrays.
2015 International Conference on Clean Electrical Power (ICCEP),2015:67-72
|
CSCD被引
1
次
|
|
|
|
|
4.
Mellit A. Fault detection and diagnosis methods for photovoltaic systems: A review.
Renewable and Sustainable Energy Reviews,2018,91:1-17
|
CSCD被引
10
次
|
|
|
|
|
5.
胡国兵. 基于自相关函数图特征的频谱感知算法研究.
电子学报,2023,51(5):1327-1333
|
CSCD被引
1
次
|
|
|
|
|
6.
Pei T T. Module block fault locating strategy for large-scale photovoltaic arrays.
Energy Conversion and Management,2020,214:112898
|
CSCD被引
5
次
|
|
|
|
|
7.
Prokhorenkova L. CatBoost: Unbiased boosting with categorical features.
Advances in Neural Information Processing Systems,2018,31:6638-6648
|
CSCD被引
13
次
|
|
|
|
|
8.
Madeti S R K. A monitoring system for online fault detection in multiple photovoltaic arrays.
Renewable Energy Focus,2022,41:160-178
|
CSCD被引
1
次
|
|
|
|
|
9.
Wang M. Online fault diagnosis of PV array considering label errors based on distributionally robust logistic regression.
Renewable Energy,2023,203:68-80
|
CSCD被引
1
次
|
|
|
|
|
10.
Vergura S. Correct settings of a joint unmanned aerial vehicle and infrared camera system for the detection of faulty photovoltaic modules.
IEEE Journal of Photovoltaics,2021,11(1):124-130
|
CSCD被引
4
次
|
|
|
|
|
11.
Momeni H. Fault diagnosis in photovoltaic arrays using GBSSL method and proposing a fault correction system.
IEEE Transactions on Industrial Informatics,2019,16(8):5300-5308
|
CSCD被引
1
次
|
|
|
|
|
12.
徐先峰. 基于半监督学习标签传播-极端随机树算法的光伏阵列故障诊断及定位.
电网技术,2023,47(3):1038-1047
|
CSCD被引
4
次
|
|
|
|
|
13.
吴亚钧. 基于IDBO-LightGBM的光伏阵列故障诊断方法.
电源学报,2024:1-15
|
CSCD被引
1
次
|
|
|
|
|
14.
钱亮. 基于HHO-ELM的光伏阵列故障诊断方法研究.
电源技术,2024,48(2):345-350
|
CSCD被引
2
次
|
|
|
|
|
15.
Wang J. Fault diagnosis method of photovoltaic array based on support vector machine.
Energy Sources, Part A: Recovery, Utilization, and Environmental Effects,2023,45(2):5380-5395
|
CSCD被引
2
次
|
|
|
|
|
16.
顾崇寅. 基于CatBoost算法的光伏阵列故障诊断方法.
电力系统自动化,2023,47(2):105-114
|
CSCD被引
18
次
|
|
|
|
|
17.
郭步豪.
基于梯度提升机器学习算法的ECG身份识别,2020
|
CSCD被引
3
次
|
|
|
|
|
18.
Mehmood A. A diode-based fault detection, classification, and localization method for photovoltaic array.
IEEE Transactions on Instrumentation and Measurement,2021,70:1-12
|
CSCD被引
1
次
|
|
|
|
|
19.
张长胜. 多策略融合的改进天鹰优化算法.
电子学报,2023,51(5):1245-1255
|
CSCD被引
3
次
|
|
|
|
|
20.
Zhou J. Proposing several hybrid SSA-Machine learning techniques for estimating rock cuttability by conical pick with relieved cutting modes.
Acta Geotechnica,2023,18(3):1431-1446
|
CSCD被引
3
次
|
|
|
|
|
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