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Cloud Computing and IoT Based Intelligent Monitoring System for Photovoltaic Plants Using Machine Learning Techniques

Author

Listed:
  • Masoud Emamian

    (Department of Electrical Engineering, Amirkabir University of Technology, Tehran 15119-43943, Iran)

  • Aref Eskandari

    (Department of Electrical Engineering, Amirkabir University of Technology, Tehran 15119-43943, Iran)

  • Mohammadreza Aghaei

    (Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), 6009 Ålesund, Norway
    Department of Sustainable Systems Engineering (INATECH), University of Freiburg, 79110 Freiburg, Germany)

  • Amir Nedaei

    (Department of Electrical Engineering, Amirkabir University of Technology, Tehran 15119-43943, Iran)

  • Amirmohammad Moradi Sizkouhi

    (Department of Electrical and Computer Engineering, Concordia University, Montréal, QC H3G 1M8, Canada)

  • Jafar Milimonfared

    (Department of Electrical Engineering, Amirkabir University of Technology, Tehran 15119-43943, Iran)

Abstract

This paper proposes an Intelligent Monitoring System (IMS) for Photovoltaic (PV) systems using affordable and cost-efficient hardware and also lightweight software that is capable of being easily implemented in different locations and having the capability to be installed in different types of PV power plants. IMS uses the Internet of Things (IoT) platform for handling data as well as Interoperability and Communication among the devices and components in the IMS. Moreover, IMS includes a personal cloud server for computing and storing the acquired data of PV systems. The IMS also consists of a web monitor system via some open-source and lightweight software that displays the information to multiple users. The IMS uses deep ensemble models for fault detection and power prediction in PV systems. A remarkable ability of the IMS is the prediction of the output power of the PV system to increase energy yield and identify malfunctions in PV plants. To this end, a long short-term memory (LSTM) ensemble neural network is developed to predict the output power of PV systems under different environmental conditions. On the other hand, the IMS uses machine learning-based models to detect numerous faults in PV systems. The fault diagnostic of IMS is based on the following stages. Firstly, major features are elicited through an analysis of Current–Voltage (I–V) characteristic curve under different faulty and normal events. Second, an ensemble learning model including Naive Bayes (NB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) is used for detecting and classifying fault events. To enhance the performance in the process of fault detection, a feature selection algorithm is also applied. A PV system has been designed and implemented for testing and validating the IMS under real conditions. IMS is an interoperable, scalable, and replicable solution for holistic monitoring of PV plant from data acquisition, storing, pre-and post-processing to malfunction and failure diagnosis, performance and energy yield assessment, and output power prediction.

Suggested Citation

  • Masoud Emamian & Aref Eskandari & Mohammadreza Aghaei & Amir Nedaei & Amirmohammad Moradi Sizkouhi & Jafar Milimonfared, 2022. "Cloud Computing and IoT Based Intelligent Monitoring System for Photovoltaic Plants Using Machine Learning Techniques," Energies, MDPI, vol. 15(9), pages 1-25, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3014-:d:798089
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    References listed on IDEAS

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    Cited by:

    1. Jing Yu & Jicheng Liu & Jiakang Sun & Mengyu Shi, 2023. "Evolutionary Game of Digital-Driven Photovoltaic–Storage–Use Value Chain Collaboration: A Value Intelligence Creation Perspective," Sustainability, MDPI, vol. 15(4), pages 1-30, February.
    2. Amor Hamied & Adel Mellit & Mohamed Benghanem & Sahbi Boubaker, 2023. "IoT-Based Low-Cost Photovoltaic Monitoring for a Greenhouse Farm in an Arid Region," Energies, MDPI, vol. 16(9), pages 1-21, April.

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