基于GS-SVM的光伏阵列积灰程度评估方法研究

张逴, 李树成, 魏东, 刘素民, 易建波

太阳能学报 ›› 2024, Vol. 45 ›› Issue (11) : 220-226.

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (11) : 220-226. DOI: 10.19912/j.0254-0096.tynxb.2023-1093

基于GS-SVM的光伏阵列积灰程度评估方法研究

  • 张逴1, 李树成1, 魏东1, 刘素民2, 易建波2
作者信息 +

EVALUATION METHOD OF ASH ACCUMULATION DEGREE OF PHOTOVOLTAIC ARRAY BASED ON GS-SVM

  • Zhang Chuo1, Li Shucheng1, Wei Dong1, Liu Sumin2, Yi Jianbo2
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文章历史 +

摘要

提出一种基于网格搜索(GS)和支持向量机(SVM)的阵列积灰程度评估方法,有效解决光伏阵列表面积灰与光伏出力之间的潜在经济性损益问题。通过对光伏阵列积灰以及混有其他故障的输出特性进行分析,揭示短路电流这一电气参数能反映光伏阵列积灰情况且不易受其他故障干扰。提出以短路电流、太阳辐照度以及温度3个参数作为输入特征量,含GS超参数优化技术的SVM积灰程度评估模型。经过实验测试分析,证明该方法准确率高于DecisionTree、GS-DecisionTree以及XGBoost评估方法,且应用中对数据采集量和训练样本量的需求低,易于推广应用。

Abstract

A method for evaluating the degree of array ash accumulation based on grid search (GS) and support vector machine (SVM) is proposed to effectively solve the potential economic gain/loss problem between the surface ash accumulation of PV arrays and the PV output power. By analyzing the output characteristics of PV arrays with ash accumulation and mixed with other faults, it is revealed that the electrical parameter of short-circuit current is able to reflect the ash accumulation of PV arrays and is not easily disturbed by other faults. An SVM model for assessing the degree of ash accumulation is proposed using three parameters, namely, short-circuit current, solar irradiance, and temperature, as input feature quantities and incorporating GS hyperparametric optimization techniques. After the experimental test and analysis, it is proved that the accuracy of this method is higher than DecisionTree, GS-DecisionTree, and XGBoost evaluation methods, and the application is easy to be popularized because of the low demand for the amount of data collection and the amount of training samples.

关键词

光伏组件 / 短路电流 / 网格搜索 / 支持向量机 / 积灰评估

Key words

photovoltaic modules / short-circuit current / grid search / support vector machine / ash accumulation evaluation

引用本文

导出引用
张逴, 李树成, 魏东, 刘素民, 易建波. 基于GS-SVM的光伏阵列积灰程度评估方法研究[J]. 太阳能学报. 2024, 45(11): 220-226 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1093
Zhang Chuo, Li Shucheng, Wei Dong, Liu Sumin, Yi Jianbo. EVALUATION METHOD OF ASH ACCUMULATION DEGREE OF PHOTOVOLTAIC ARRAY BASED ON GS-SVM[J]. Acta Energiae Solaris Sinica. 2024, 45(11): 220-226 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1093
中图分类号: TM715   

参考文献

[1] KAZEM H A, CHAICHAN M T.Experimental analysis of the effect of dust’s physical properties on photovoltaic modules in Northern Oman[J]. Solar energy, 2016, 139:68-80.
[2] SCHILL C, BRACHMANN S, KOEHL M.Impact of soiling on IV-curves and efficiency of PV-modules[J].Solar energy,2015,112:259-262.
[3] 张长庚. 考虑含光伏接入的配电网故障特性研究[J]. 电网与清洁能源, 2022, 38(12): 138-146.
ZHANG C G.Research on fault characteristics of the distribution network considering access of photovoltaic[J]. Power system and clean energy, 2022, 38(12): 138-146.
[4] 范思远, 王煜, 曹生现, 等. 积灰对光伏组件输出特性影响建模与分析[J]. 仪器仪表学报, 2021, 42(4): 83-91.
FAN S Y, WANG Y, CAO S X, et al.Effect modeling and analysis of dust accumulation on output characteristics of photovoltaic modules[J]. Chinese journal of scientific instrument, 2021, 42(4): 83-91.
[5] BABATUNDE A A, ABBASOGLU S, SENOL M, Analysis of the impact of dust, tilt angle and orientation on performance of PV plants[J]. Renewable and sustainable energy reviews, 2018, 90: 1017-1026.
[6] 王中豪, 徐正国, 章筠. 基于改进型相似性建模的光伏积灰监测方法[J]. 浙江大学学报, 2022, 56(4): 718-726.
WANG Z H, XU Z G, ZHANG Y.Improved similarity-based modeling approach for dust deposition monitoring of photovoltaic modules[J]. Journal of Zhejiang University(engineering science), 2022, 56(4): 718-726.
[7] DUNDERDALE C, BRETTENNY W, CLOHESSY C, et al.Photovoltaic defect classification through thermal infrared imaging using a machine learning approach[J]. Progress in photovoltaics: research and applications, 2020, 28(3): 177-188.
[8] 王艳, 申宗旺, 赵洪山, 等. 基于改进CNN-SVM 的光伏组件红外图像故障诊断方法[J]. 华北电力大学学报(自然科学版), 2024, 51(3): 110-117.
WANG Y, SHEN Z W, ZHAO H S, et al.Fault diagnosis method of photovoltaic modules infrared image based on improved CNN-SVM[J]. Journal of North China Electric Power University(natural science edition), 2024, 51(3): 110-117.
[9] HE Y Z, DENG B Y, WANG H J, et al.Infrared machine vision and infrared thermography with deep learning: a review[J]. Infrared physics and technology, 2021, 116:103754.
[10] SIMONAZZI M, CHIORBOLI G, COVA P, et al.Smart soiling sensor for PV modules[J]. Microelectronics reliability, 2020, 114: 113789.
[11] 张博, 黄帅, 丛伟伦, 等. 基于最小二乘支持向量回归的光伏电站清洗时间动态优化方法[J]. 太阳能学报, 2021, 42(9): 55-61.
ZHANG B,HUANG S, CONG W L, et al.Dynamic optimization of PV plant cleaning time based on LS-SVR[J]. Acta energiae solaris sinica, 2021, 42(9): 55-61.
[12] 陈金鑫, 潘国兵, 欧阳静, 等. 自然降雨下光伏组件积灰预测方法研究[J]. 太阳能学报,2021, 42(2): 431-437.
CHEN J X, PAN G B,OUYANG J, et al.Study on prediction algorithm of dustfall on PV modules under natural rainfall[J]. Acta energiae solaris sinica, 2021, 42(2): 431-437.
[13] 吴春华, 袁同浩, 陈雪娟, 等. 光伏电站不均匀积灰检测及优化控制[J]. 太阳能学报, 2017, 38(3): 774-780.
WU C H, YUAN T H, CHEN X J, et al.PV plant uneven fouling detection and control optimization[J]. Acta energiae solaris sinica, 2017, 38(3): 774-780.
[14] 丛伟伦, 张博, 夏亚东, 等. 基于马尔可夫链的光伏电站遮挡实时诊断算法[J]. 太阳能学报, 2020, 41(4): 67-72.
CONG W L, ZHANG B, XIA Y D, et al.Diagnosis algorithm for real-time shaded analysis of photovoltaic power station based on Markov chain[J]. Acta energiae solaris sinica,2020, 41(4): 67-72.
[15] 裴晓东, 张文龙, 刘海龙, 等. 基于SVM-PSO的电网设备早期故障分类方法[J]. 电网与清洁能源, 2022, 38(4): 68-75.
PEI X D, ZHANG W L, LIU H L, et al.A classification method of early faults of power grid equipment based on SVM-PSO[J]. Power system and clean energy,2022,38(4): 68-75.
[16] 余玲珍, 杨靖, 龙道银, 等. 典型故障条件下光伏阵列建模与仿真[J]. 电网与清洁能源, 2020, 36(10): 103-111, 118.
YU L Z, YANG J,LONG D Y, et al.Modeling and simulation of photovoltaic arrays under typical fault conditions[J]. Power system and clean energy, 2020, 36(10): 103-111, 118.
[17] 赵明智, 王帅, 张志明, 等. 沙漠沙尘对光伏组件温度性能影响实验研究[J]. 太阳能学报, 2020, 41(6): 293-298.
ZHAO M Z, WANG S, ZHANG Z M, et al.Experimental study on effect of desert sand on temperature performance of photovoltaic modules[J]. Acta energiae solaris sinica,2020, 41(6): 293-298.
[18] 牛海明, 崔青汝, 刘厚旭. 积灰对光伏电池板输出特性影响研究[J]. 热力发电, 2021, 50(2): 110-117.
NIU H M,CUI Q R, LIU H X.Effect of ash accumulation on output performance of photovoltaic panels[J]. Thermal power generation, 2021, 50(2): 110-117.
[19] 陈庆文, 喻凯, 田莉莎, 等. 光伏电站光伏组件串联数的优化设计研究[J]. 太阳能, 2022(12): 72-77.
CHEN Q W, YU K, TIAN L S, et al.Study on optimized design for PV modules series numbers of PV power stations[J]. Solar energy, 2022(12): 72-77.
[20] 顾崇寅, 徐潇源, 王梦圆, 等. 基于CatBoost算法的光伏阵列故障诊断方法[J]. 电力系统自动化, 2023, 47(2): 105-114.
GU C Y, Xu X Y, WANG M Y, et al.CatBoost algorithm based fault diagnosis method for photovoltaic arrays[J]. Automation of electric power systems, 2023, 47(2): 105-114.

基金

四川省科技厅重点研发计划(2021YFG0098);国家重点研发计划(2018YFB0905000)

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