IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i15p3992-d393645.html
   My bibliography  Save this article

Bollinger Bands Based on Exponential Moving Average for Statistical Monitoring of Multi-Array Photovoltaic Systems

Author

Listed:
  • Silvano Vergura

    (Department of Electrical and Information Engineering, Polytechnic University of Bari, st. E. Orabona 4, I-70125 Bari, Italy)

Abstract

Monitoring the performance of a photovoltaic (PV) system when environmental parameters are not available is very difficult. Comparing the energy datasets of the arrays belonging to the same PV plant is one strategy. If the extension of a PV plant is limited, all the arrays are subjected to the same environmental conditions. Therefore, identical arrays produce the same energy amount, whatever the solar radiation and cell temperature. This is valid for small- to medium-rated power PV plants (3–50 kWp) and, moreover, this typology of PV plants sometimes is not equipped with a meteorological sensor system. This paper presents a supervision methodology based on comparing the average energy of each array and the average energy of the whole PV plant. To detect low-intensity anomalies before they become failures, the variability of the energy produced by each array is monitored by using the Bollinger Bands (BB) method. This is a statistical tool developed in the financial field to evaluate the stock price volatility. This paper introduces two modifications in the standard BB method: the exponential moving average (EMA) instead of the simple moving average (SMA), and the size of the width of BB, set to three times the standard deviation instead of four times. Until the produced energy of each array is contained in the BB, a serious anomaly is not present. A case study based on a real operating 19.8 kWp PV plant is discussed.

Suggested Citation

  • Silvano Vergura, 2020. "Bollinger Bands Based on Exponential Moving Average for Statistical Monitoring of Multi-Array Photovoltaic Systems," Energies, MDPI, vol. 13(15), pages 1-14, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:15:p:3992-:d:393645
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/15/3992/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/15/3992/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Harrou, Fouzi & Sun, Ying & Taghezouit, Bilal & Saidi, Ahmed & Hamlati, Mohamed-Elkarim, 2018. "Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches," Renewable Energy, Elsevier, vol. 116(PA), pages 22-37.
    2. Silvano Vergura, 2018. "Hypothesis Tests-Based Analysis for Anomaly Detection in Photovoltaic Systems in the Absence of Environmental Parameters," Energies, MDPI, vol. 11(3), pages 1-18, February.
    3. Silvestre, Santiago & Kichou, Sofiane & Chouder, Aissa & Nofuentes, Gustavo & Karatepe, Engin, 2015. "Analysis of current and voltage indicators in grid connected PV (photovoltaic) systems working in faulty and partial shading conditions," Energy, Elsevier, vol. 86(C), pages 42-50.
    4. Vipul N. Rajput & Kartik S. Pandya & Junhee Hong & Zong Woo Geem, 2020. "A Novel Protection Scheme for Solar Photovoltaic Generator Connected Networks Using Hybrid Harmony Search Algorithm-Bollinger Bands Approach," Energies, MDPI, vol. 13(10), pages 1-24, May.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Andrea Kolková & Aleksandr Kljuènikov, 2021. "Demand forecasting: an alternative approach based on technical indicator Pbands," Oeconomia Copernicana, Institute of Economic Research, vol. 12(4), pages 1063-1094, December.
    2. Gianfranco Di Lorenzo & Erika Stracqualursi & Leonardo Micheli & Salvatore Celozzi & Rodolfo Araneo, 2022. "Prognostic Methods for Photovoltaic Systems’ Underperformance and Degradation: Status, Perspectives, and Challenges," Energies, MDPI, vol. 15(17), pages 1-6, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tingting Pei & Xiaohong Hao, 2019. "A Fault Detection Method for Photovoltaic Systems Based on Voltage and Current Observation and Evaluation," Energies, MDPI, vol. 12(9), pages 1-16, May.
    2. Livera, Andreas & Theristis, Marios & Makrides, George & Georghiou, George E., 2019. "Recent advances in failure diagnosis techniques based on performance data analysis for grid-connected photovoltaic systems," Renewable Energy, Elsevier, vol. 133(C), pages 126-143.
    3. Pedro Branco & Francisco Gonçalves & Ana Cristina Costa, 2020. "Tailored Algorithms for Anomaly Detection in Photovoltaic Systems," Energies, MDPI, vol. 13(1), pages 1-21, January.
    4. Singh, Rashmi & Sharma, Madhu & Rawat, Rahul & Banerjee, Chandan, 2018. "An assessment of series resistance estimation techniques for different silicon based SPV modules," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 199-216.
    5. Silvano Vergura, 2018. "Hypothesis Tests-Based Analysis for Anomaly Detection in Photovoltaic Systems in the Absence of Environmental Parameters," Energies, MDPI, vol. 11(3), pages 1-18, February.
    6. Yu, Cao & Wang, Haizheng & Yao, Jianxi & Zhao, Jian & Sun, Qian & Zhu, Honglu, 2020. "A dynamic alarm threshold setting method for photovoltaic array and its application," Renewable Energy, Elsevier, vol. 158(C), pages 13-22.
    7. Dhimish, Mahmoud & Holmes, Violeta & Dales, Mark, 2017. "Parallel fault detection algorithm for grid-connected photovoltaic plants," Renewable Energy, Elsevier, vol. 113(C), pages 94-111.
    8. Silvano Vergura & Mario Carpentieri, 2017. "Statistics to Detect Low-Intensity Anomalies in PV Systems," Energies, MDPI, vol. 11(1), pages 1-12, December.
    9. Dhimish, Mahmoud & Holmes, Violeta & Mehrdadi, Bruce & Dales, Mark & Mather, Peter, 2017. "Photovoltaic fault detection algorithm based on theoretical curves modelling and fuzzy classification system," Energy, Elsevier, vol. 140(P1), pages 276-290.
    10. Collin Barker & Sam Cipkar & Tyler Lavigne & Cameron Watson & Maher Azzouz, 2021. "Real-Time Nuisance Fault Detection in Photovoltaic Generation Systems Using a Fine Tree Classifier," Sustainability, MDPI, vol. 13(4), pages 1-15, February.
    11. Monadi, Mehdi & Zamani, M. Amin & Koch-Ciobotaru, Cosmin & Candela, Jose Ignacio & Rodriguez, Pedro, 2016. "A communication-assisted protection scheme for direct-current distribution networks," Energy, Elsevier, vol. 109(C), pages 578-591.
    12. Sairam, Seshapalli & Seshadhri, Subathra & Marafioti, Giancarlo & Srinivasan, Seshadhri & Mathisen, Geir & Bekiroglu, Korkut, 2022. "Edge-based Explainable Fault Detection Systems for photovoltaic panels on edge nodes," Renewable Energy, Elsevier, vol. 185(C), pages 1425-1440.
    13. Li, Yuanliang & Ding, Kun & Zhang, Jingwei & Chen, Fudong & Chen, Xiang & Wu, Jiabing, 2019. "A fault diagnosis method for photovoltaic arrays based on fault parameters identification," Renewable Energy, Elsevier, vol. 143(C), pages 52-63.
    14. Qamar Navid & Ahmed Hassan & Abbas Ahmad Fardoun & Rashad Ramzan & Abdulrahman Alraeesi, 2021. "Fault Diagnostic Methodologies for Utility-Scale Photovoltaic Power Plants: A State of the Art Review," Sustainability, MDPI, vol. 13(4), pages 1-22, February.
    15. Dipu Sarkar & Sagar Kudkelwar, 2021. "Optimal over current relay coordination in Microgrid using a novel hybrid Water Cycle-Moth Flame algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(3), pages 553-564, June.
    16. Martin Libra & Milan Daneček & Jan Lešetický & Vladislav Poulek & Jan Sedláček & Václav Beránek, 2019. "Monitoring of Defects of a Photovoltaic Power Plant Using a Drone," Energies, MDPI, vol. 12(5), pages 1-9, February.
    17. Silvano Vergura, 2022. "Criticalities of the Outdoor Infrared Inspection of Photovoltaic Modules by Means of Drones," Energies, MDPI, vol. 15(14), pages 1-19, July.
    18. Andrea Kolková & Aleksandr Kljuènikov, 2021. "Demand forecasting: an alternative approach based on technical indicator Pbands," Oeconomia Copernicana, Institute of Economic Research, vol. 12(4), pages 1063-1094, December.
    19. Li, Chenxi & Yang, Yongheng & Spataru, Sergiu & Zhang, Kanjian & Wei, Haikun, 2021. "A robust parametrization method of photovoltaic modules for enhancing one-diode model accuracy under varying operating conditions," Renewable Energy, Elsevier, vol. 168(C), pages 764-778.
    20. Sunme Park & Soyeong Park & Myungsun Kim & Euiseok Hwang, 2020. "Clustering-Based Self-Imputation of Unlabeled Fault Data in a Fleet of Photovoltaic Generation Systems," Energies, MDPI, vol. 13(3), pages 1-16, February.

    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:13:y:2020:i:15:p:3992-:d:393645. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.