Cooperative load frequency control of generator and battery using a recurrent neural network

Takato Otani, Ryu Tanabe, Yui Koyanagi, Shinichi Iwamoto

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    A frequency issue is one of the concerns caused by renewable energies. There is a possibility of losing a balance of the demand and the supply followed by a frequency disturbance when large amounts of the renewable energies are introduced. As a countermeasure against the problem, storage battery systems for the load frequency control are beginning to be introduced. Batteries have faster response, because it is a power electronics equipment and does not have mechanical components. However, a state of the charge of the battery needs to be maintained in order to make efficient use of the battery. In this paper, in order to use the introduced storage battery effectively, a control by a recurrent neural network is proposed. Recently, neural networks are attracting attention as an innovative control method. By training the neural network with particle swarm optimization, the frequency of the power system and the state of the charge of the storage battery are maintained simultaneously.

    Original languageEnglish
    Title of host publicationTENCON 2017 - 2017 IEEE Region 10 Conference
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages918-923
    Number of pages6
    Volume2017-December
    ISBN (Electronic)9781509011339
    DOIs
    Publication statusPublished - 2017 Dec 19
    Event2017 IEEE Region 10 Conference, TENCON 2017 - Penang, Malaysia
    Duration: 2017 Nov 52017 Nov 8

    Other

    Other2017 IEEE Region 10 Conference, TENCON 2017
    CountryMalaysia
    CityPenang
    Period17/11/517/11/8

    Fingerprint

    Recurrent neural networks
    Neural networks
    Power electronics
    Particle swarm optimization (PSO)
    Electronic equipment

    Keywords

    • battery
    • load frequency control
    • machine learning
    • power system
    • renewable energy
    • state of charge

    ASJC Scopus subject areas

    • Computer Science Applications
    • Electrical and Electronic Engineering

    Cite this

    Otani, T., Tanabe, R., Koyanagi, Y., & Iwamoto, S. (2017). Cooperative load frequency control of generator and battery using a recurrent neural network. In TENCON 2017 - 2017 IEEE Region 10 Conference (Vol. 2017-December, pp. 918-923). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TENCON.2017.8227989

    Cooperative load frequency control of generator and battery using a recurrent neural network. / Otani, Takato; Tanabe, Ryu; Koyanagi, Yui; Iwamoto, Shinichi.

    TENCON 2017 - 2017 IEEE Region 10 Conference. Vol. 2017-December Institute of Electrical and Electronics Engineers Inc., 2017. p. 918-923.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Otani, T, Tanabe, R, Koyanagi, Y & Iwamoto, S 2017, Cooperative load frequency control of generator and battery using a recurrent neural network. in TENCON 2017 - 2017 IEEE Region 10 Conference. vol. 2017-December, Institute of Electrical and Electronics Engineers Inc., pp. 918-923, 2017 IEEE Region 10 Conference, TENCON 2017, Penang, Malaysia, 17/11/5. https://doi.org/10.1109/TENCON.2017.8227989
    Otani T, Tanabe R, Koyanagi Y, Iwamoto S. Cooperative load frequency control of generator and battery using a recurrent neural network. In TENCON 2017 - 2017 IEEE Region 10 Conference. Vol. 2017-December. Institute of Electrical and Electronics Engineers Inc. 2017. p. 918-923 https://doi.org/10.1109/TENCON.2017.8227989
    Otani, Takato ; Tanabe, Ryu ; Koyanagi, Yui ; Iwamoto, Shinichi. / Cooperative load frequency control of generator and battery using a recurrent neural network. TENCON 2017 - 2017 IEEE Region 10 Conference. Vol. 2017-December Institute of Electrical and Electronics Engineers Inc., 2017. pp. 918-923
    @inproceedings{6f43f9cc3f1448058b69c6505e846203,
    title = "Cooperative load frequency control of generator and battery using a recurrent neural network",
    abstract = "A frequency issue is one of the concerns caused by renewable energies. There is a possibility of losing a balance of the demand and the supply followed by a frequency disturbance when large amounts of the renewable energies are introduced. As a countermeasure against the problem, storage battery systems for the load frequency control are beginning to be introduced. Batteries have faster response, because it is a power electronics equipment and does not have mechanical components. However, a state of the charge of the battery needs to be maintained in order to make efficient use of the battery. In this paper, in order to use the introduced storage battery effectively, a control by a recurrent neural network is proposed. Recently, neural networks are attracting attention as an innovative control method. By training the neural network with particle swarm optimization, the frequency of the power system and the state of the charge of the storage battery are maintained simultaneously.",
    keywords = "battery, load frequency control, machine learning, power system, renewable energy, state of charge",
    author = "Takato Otani and Ryu Tanabe and Yui Koyanagi and Shinichi Iwamoto",
    year = "2017",
    month = "12",
    day = "19",
    doi = "10.1109/TENCON.2017.8227989",
    language = "English",
    volume = "2017-December",
    pages = "918--923",
    booktitle = "TENCON 2017 - 2017 IEEE Region 10 Conference",
    publisher = "Institute of Electrical and Electronics Engineers Inc.",

    }

    TY - GEN

    T1 - Cooperative load frequency control of generator and battery using a recurrent neural network

    AU - Otani, Takato

    AU - Tanabe, Ryu

    AU - Koyanagi, Yui

    AU - Iwamoto, Shinichi

    PY - 2017/12/19

    Y1 - 2017/12/19

    N2 - A frequency issue is one of the concerns caused by renewable energies. There is a possibility of losing a balance of the demand and the supply followed by a frequency disturbance when large amounts of the renewable energies are introduced. As a countermeasure against the problem, storage battery systems for the load frequency control are beginning to be introduced. Batteries have faster response, because it is a power electronics equipment and does not have mechanical components. However, a state of the charge of the battery needs to be maintained in order to make efficient use of the battery. In this paper, in order to use the introduced storage battery effectively, a control by a recurrent neural network is proposed. Recently, neural networks are attracting attention as an innovative control method. By training the neural network with particle swarm optimization, the frequency of the power system and the state of the charge of the storage battery are maintained simultaneously.

    AB - A frequency issue is one of the concerns caused by renewable energies. There is a possibility of losing a balance of the demand and the supply followed by a frequency disturbance when large amounts of the renewable energies are introduced. As a countermeasure against the problem, storage battery systems for the load frequency control are beginning to be introduced. Batteries have faster response, because it is a power electronics equipment and does not have mechanical components. However, a state of the charge of the battery needs to be maintained in order to make efficient use of the battery. In this paper, in order to use the introduced storage battery effectively, a control by a recurrent neural network is proposed. Recently, neural networks are attracting attention as an innovative control method. By training the neural network with particle swarm optimization, the frequency of the power system and the state of the charge of the storage battery are maintained simultaneously.

    KW - battery

    KW - load frequency control

    KW - machine learning

    KW - power system

    KW - renewable energy

    KW - state of charge

    UR - http://www.scopus.com/inward/record.url?scp=85044174567&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=85044174567&partnerID=8YFLogxK

    U2 - 10.1109/TENCON.2017.8227989

    DO - 10.1109/TENCON.2017.8227989

    M3 - Conference contribution

    AN - SCOPUS:85044174567

    VL - 2017-December

    SP - 918

    EP - 923

    BT - TENCON 2017 - 2017 IEEE Region 10 Conference

    PB - Institute of Electrical and Electronics Engineers Inc.

    ER -