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

Takato Otani; Ryu Tanabe; Yui Koyanagi; Shinichi Iwamoto

doi:10.1109/TENCON.2017.8227989

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 proceeding › Conference contribution

5 Citations (Scopus)

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 language	English
Title of host publication	TENCON 2017 - 2017 IEEE Region 10 Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	918-923
Number of pages	6
Volume	2017-December
ISBN (Electronic)	9781509011339
DOIs	https://doi.org/10.1109/TENCON.2017.8227989
Publication status	Published - 2017 Dec 19
Event	2017 IEEE Region 10 Conference, TENCON 2017 - Penang, Malaysia Duration: 2017 Nov 5 → 2017 Nov 8

Other

Other	2017 IEEE Region 10 Conference, TENCON 2017
Country/Territory	Malaysia
City	Penang
Period	17/11/5 → 17/11/8

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/TENCON.2017.8227989

Cite this

Cooperative load frequency control of generator and battery using a recurrent neural network. / Otani, Takato; Tanabe, Ryu; Koyanagi, Yui et al.
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 proceeding › Conference 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

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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.",

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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.

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KW - renewable energy

KW - state of charge

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

Abstract

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Keywords

ASJC Scopus subject areas

UN SDGs

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