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Tailored Algorithms for Anomaly Detection in Photovoltaic Systems

Author

Listed:
  • Pedro Branco

    (CSide, Rua Alexandre Herculano 17, C/V Esq., 1250-008 Lisboa, Portugal)

  • Francisco Gonçalves

    (CSide, Rua Alexandre Herculano 17, C/V Esq., 1250-008 Lisboa, Portugal)

  • Ana Cristina Costa

    (Nova Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal)

Abstract

The fastest-growing renewable source of energy is solar photovoltaic (PV) energy, which is likely to become the largest electricity source in the world by 2050. In order to be a viable alternative energy source, PV systems should maximise their efficiency and operate flawlessly. However, in practice, many PV systems do not operate at their full capacity due to several types of anomalies. We propose tailored algorithms for the detection of different PV system anomalies, including suboptimal orientation, daytime and sunrise/sunset shading, brief and sustained daytime zero-production, and low maximum production. Furthermore, we establish simple metrics to assess the severity of suboptimal orientation and daytime shading. The proposed detection algorithms were applied to a set of time-series of electricity production in Portugal, which are based on two periods with distinct weather conditions. Under favourable weather conditions, the algorithms successfully detected most of the time-series labelled with either daytime or sunrise/sunset shading, and with either sustained or brief daytime zero-production. There was a relatively low percentage of false positives, such that most of the anomaly detections were correct. As expected, the algorithms tend to be more robust under favourable rather than under adverse weather conditions. The proposed algorithms may prove to be useful not only to research specialists, but also to energy utilities and owners of small- and medium-sized PV systems, who may thereby effortlessly monitor their operation and performance.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:1:p:225-:d:304636
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    References listed on IDEAS

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    Cited by:

    1. Selma Tchoketch Kebir & Nawal Cheggaga & Adrian Ilinca & Sabri Boulouma, 2021. "An Efficient Neural Network-Based Method for Diagnosing Faults of PV Array," Sustainability, MDPI, vol. 13(11), pages 1-27, May.
    2. Li, Ding & Zhang, Yufei & Yang, Zheng & Jin, Yaohui & Xu, Yanyan, 2024. "Sensing anomaly of photovoltaic systems with sequential conditional variational autoencoder," Applied Energy, Elsevier, vol. 353(PA).
    3. Tomasz Śmiałkowski & Andrzej Czyżewski, 2022. "Detection of Anomalies in the Operation of a Road Lighting System Based on Data from Smart Electricity Meters," Energies, MDPI, vol. 15(24), pages 1-23, December.
    4. Yuping Zou & Rui Wu & Xuesong Tian & Hua Li, 2023. "Realizing the Improvement of the Reliability and Efficiency of Intelligent Electricity Inspection: IAOA-BP Algorithm for Anomaly Detection," Energies, MDPI, vol. 16(7), pages 1-15, March.
    5. Mariam Ibrahim & Ahmad Alsheikh & Feras M. Awaysheh & Mohammad Dahman Alshehri, 2022. "Machine Learning Schemes for Anomaly Detection in Solar Power Plants," Energies, MDPI, vol. 15(3), pages 1-17, February.

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