IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i22p4316-d286305.html
   My bibliography  Save this article

A Novel Photovoltaic Array Outlier Cleaning Algorithm Based on Sliding Standard Deviation Mutation

Author

Listed:
  • Aoyu Hu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
    School of Renewable Energy, North China Electric Power University, Beijing 102206, China)

  • Qian Sun

    (State Grid HeNan Electric Power Company Research Institute, Zhengzhou 450000, China)

  • Hao Liu

    (State Grid HeNan Electric Power Company Research Institute, Zhengzhou 450000, China)

  • Ning Zhou

    (State Grid HeNan Electric Power Company Research Institute, Zhengzhou 450000, China)

  • Zhan’ao Tan

    (Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China)

  • Honglu Zhu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
    School of Renewable Energy, North China Electric Power University, Beijing 102206, China)

Abstract

There is a large number of outliers in the operation data of photovoltaic (PV) array, which is caused by array abnormalities and faults, communication issues, sensor failure, and array shutdown during PV power plant operation. The outlier will reduce the accuracy of PV system performance analysis and modeling, and make it difficult for fault diagnosis of PV power plant. The conventional data cleaning method is affected by the outlier data distribution. In order to solve the above problems, this paper presents a method for identifying PV array outliers based on sliding standard deviation mutation. Considering the PV array output characteristics under actual environmental conditions, the distribution of array outliers is analyzed. Then, an outlier identification method is established based on sliding standard deviation calculation. This method can identify outliers by analyzing the degree of dispersion of the operational data. The verification part is illustrated by case study and algorithm comparison. In the case study, multiple sets of actual operating data of different inverters are cleaned, which is selected from a large grid-connected power station. The cleaning results illustrate the availability of the algorithm. Then, the comparison against the quantile-algorithm-based outlier identification method explains the effectiveness of the proposed algorithm.

Suggested Citation

  • Aoyu Hu & Qian Sun & Hao Liu & Ning Zhou & Zhan’ao Tan & Honglu Zhu, 2019. "A Novel Photovoltaic Array Outlier Cleaning Algorithm Based on Sliding Standard Deviation Mutation," Energies, MDPI, vol. 12(22), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:22:p:4316-:d:286305
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/22/4316/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/22/4316/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yao, Qingtao & Zhu, Haowei & Xiang, Ling & Su, Hao & Hu, Aijun, 2023. "A novel composed method of cleaning anomy data for improving state prediction of wind turbine," Renewable Energy, Elsevier, vol. 204(C), pages 131-140.
    2. Hee-Won Lim & Il-Kwon Kim & Ji-Hyeon Kim & U-Cheul Shin, 2022. "Simulation-Based Fault Detection Remote Monitoring System for Small-Scale Photovoltaic Systems," Energies, MDPI, vol. 15(24), pages 1-12, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:22:p:4316-:d:286305. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.