Dust Deposition Diagnosis of Photovoltaic Modules Using Similarity-Based Modeling (SBM) Approach

This paper focuses on the photovoltaic (PV) system and studies the dust depositing diagnosis of the PV modules. An unsupervised data-driven method called Similarity-Based Modeling (SBM) was used to deal with this problem and some improvements of this method were adopted. SBM is a nonparametric empir...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) s. 495 - 500
Hlavní autoři: Wang, Zhonghao, Xu, Zhengguo
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.07.2019
Témata:
Cleaning
Data models
Diagnosis
Dust deposition
Estimation
Inverters
Photovoltaic system
Photovoltaic systems
Similarity-Based Modeling
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This paper focuses on the photovoltaic (PV) system and studies the dust depositing diagnosis of the PV modules. An unsupervised data-driven method called Similarity-Based Modeling (SBM) was used to deal with this problem and some improvements of this method were adopted. SBM is a nonparametric empirical modeling technology that uses pattern recognition from historical data to generate estimates of the current values of each variable in a set of modeled data sources. The motivation to use SBM is that the mainstream approaches now, contrast experiments approaches and theoretical formulas approaches have many disadvantages. Contrast experiments are complicated and need experiment systems with high-cost. Theoretical formulas approaches are not accurate enough. SBM overcomes them to some extent. Numerical experiments are also studied to testify that the proposed method has good performance in the application.
DOI:10.1109/SAFEPROCESS45799.2019.9213429