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Load–frequency control : a GA-based multi-agent reinforcement learning

Daneshfar, Fatheme & Bevrani, Hassan (2010) Load–frequency control : a GA-based multi-agent reinforcement learning. IET Generation, Transmission & Distribution, 4(1), pp. 13-26.

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Abstract

The load–frequency control (LFC) problem has been one of the major subjects in a power system. In practice, LFC systems use proportional–integral (PI) controllers. However since these controllers are designed using a linear model, the non-linearities of the system are not accounted for and they are incapable of gaining good dynamical performance for a wide range of operating conditions in a multi-area power system. A strategy for solving this problem because of the distributed nature of a multi-area power system is presented by using a multi-agent reinforcement learning (MARL) approach. It consists of two agents in each power area; the estimator agent provides the area control error (ACE) signal based on the frequency bias estimation and the controller agent uses reinforcement learning to control the power system in which genetic algorithm optimisation is used to tune its parameters. This method does not depend on any knowledge of the system and it admits considerable flexibility in defining the control objective. Also, by finding the ACE signal based on the frequency bias estimation the LFC performance is improved and by using the MARL parallel, computation is realised, leading to a high degree of scalability. Here, to illustrate the accuracy of the proposed approach, a three-area power system example is given with two scenarios.

Impact and interest:

23 citations in Scopus
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13 citations in Web of Science®

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ID Code: 31466
Item Type: Journal Article
Keywords: Load-frequency control, reinforcement learning, Multi-agent system
DOI: 10.1049/iet-gtd.2009.0168
ISSN: 1751-8687
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Power and Energy Systems Engineering (excl. Renewable Power) (090607)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
Deposited On: 26 Mar 2010 15:23
Last Modified: 10 Aug 2011 23:47

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