干旱区生态环境知识资源中心(Arid): On reliability enhancement of solar PV arrays using hybrid SVR for soiling based on WT and EMD methods

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DOI	10.1016/j.asej.2024.102716
	On reliability enhancement of solar PV arrays using hybrid SVR for soiling based on WT and EMD methods
	Redekar, Abhijeet; Dhiman, Harsh S.; Deb, Dipankar; Muyeen, S. M.
通讯作者	Dhiman, HS
来源期刊	AIN SHAMS ENGINEERING JOURNAL
ISSN	2090-4479
EISSN	2090-4495
出版年	2024
卷号	15 期号:6
英文摘要	Solar farms have PV arrays in arid and semi-arid regions where ensuring the system's reliability is paramount and face uncertain events like dust storms. The deposition of random dust patterns over panel arrays is called uneven soiling, which diminishes the power generation of such farms. This paper finds the most suitable hybrid algorithm model, the wavelet transform-based support vector regression variants (WT-SVR) algorithm, and the empirical model decomposition-based support vector regression variants (EMD-SVR) to predict the extent of soiling levels and uncertain events on PV arrays. The soiling dataset is taken from NREL's Soiling Station Number 3 in Imperial County, Calipatria, California, from December 30, 2014, to December 31, 2015. This research tested four SVR variants on soiling data, viz., epsilon SVR, LSSVR, TSVR, and epsilon TSVR, then compared with the benchmark random forest. The hyperparameters for each model are meticulously tuned to enhance the robustness of the trained algorithms. Results reveal that the WT-TSVR model outperforms the WT-SVR model in terms of wavelet transform decomposition by a margin of 91.6%. Similarly, the EMD-TSVR model showcases an 85.7% enhancement in performance over the EMD-SVR model based on empirical mode decomposition. All SVR variants outperform the benchmark model (RF). Furthermore, EMD models exhibit enhanced efficiency in forecasting random events compared to WT, which is attributed to their reduced computational time. This model applies to multi-cleaning agent robots, aligning with recommendations from the state-of-the-art literature.
英文关键词	Soiling forecasting Wavelet transform Support vector regression Empirical mode decomposition
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001267905500001
WOS关键词	SUPPORT VECTOR REGRESSION ; DUST ; MODULES ; IMPACT ; SELECTION
WOS类目	Engineering, Multidisciplinary
WOS研究方向	Engineering
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/402803
推荐引用方式 GB/T 7714	Redekar, Abhijeet,Dhiman, Harsh S.,Deb, Dipankar,et al. On reliability enhancement of solar PV arrays using hybrid SVR for soiling based on WT and EMD methods[J],2024,15(6).
APA	Redekar, Abhijeet,Dhiman, Harsh S.,Deb, Dipankar,&Muyeen, S. M..(2024).On reliability enhancement of solar PV arrays using hybrid SVR for soiling based on WT and EMD methods.AIN SHAMS ENGINEERING JOURNAL,15(6).
MLA	Redekar, Abhijeet,et al."On reliability enhancement of solar PV arrays using hybrid SVR for soiling based on WT and EMD methods".AIN SHAMS ENGINEERING JOURNAL 15.6(2024).