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Hypothesis Tests-Based Analysis for Anomaly Detection in Photovoltaic Systems in the Absence of Environmental Parameters

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  • Silvano Vergura

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

Abstract

This paper deals with the monitoring of the performance of a photovoltaic plant, without using the environmental parameters such as the solar radiation and the temperature. The main idea is to statistically compare the energy performances of the arrays constituting the PV plant. In fact, the environmental conditions affect equally all the arrays of a small-medium-size PV plant, because the extension of the plant is limited, so any comparison between the energy distributions of identical arrays is independent of the solar radiation and the cell temperature, making the proposed methodology very effective for PV plants not equipped with a weather station, as it often happens for the PV plants located in urban contexts and having a nominal peak power in the 3÷50 kWp range, typically installed on the roof of a residential or industrial building. In this case, the costs of an advanced monitoring system based on the environmental data are not justified, consequently, the weather station is often also omitted. The proposed procedure guides the user through several inferential statistical tools that allow verifying whether the arrays have produced the same amount of energy or, alternatively, which is the worst array. The procedure is effective in detecting and locating abnormal operating conditions, before they become failures.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:485-:d:133231
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    References listed on IDEAS

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    3. Silvano Vergura, 2016. "A Complete and Simplified Datasheet-Based Model of PV Cells in Variable Environmental Conditions for Circuit Simulation," Energies, MDPI, vol. 9(5), pages 1-12, April.
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    Cited by:

    1. 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.
    2. Wilfried van Sark, 2019. "Photovoltaic System Design and Performance," Energies, MDPI, vol. 12(10), pages 1-6, May.
    3. 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.
    4. Jamel Riahi & Silvano Vergura & Dhafer Mezghani & Abdelkader Mami, 2021. "Smart and Renewable Energy System to Power a Temperature-Controlled Greenhouse," Energies, MDPI, vol. 14(17), pages 1-21, September.
    5. 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.
    6. Moayad Shammut & Mengqiu Cao & Yuerong Zhang & Claire Papaix & Yuqi Liu & Xing Gao, 2019. "Banning Diesel Vehicles in London: Is 2040 Too Late?," Energies, MDPI, vol. 12(18), pages 1-17, September.
    7. 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.

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