Load frequency regulation for multi-area power system using integral reinforcement learning

Active load variations in uncertain dynamical power system environments affect the energy exchange and efficiency in multi-area power systems, which could compromise the stability of power grids. Hence, model-free load frequency control mechanisms are needed in order to sustain proper performances u...

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Bibliographic Details
Published in:IET generation, transmission & distribution transmission & distribution, 2019-10, Vol.13 (19), p.4311-4323
Main Authors: Abouheaf, Mohammed, Gueaieb, Wail, Sharaf, Adel
Format: Article
Language:English
Subjects:
active load variations
adaptive control
frequency control
integral reinforcement learning
iterative methods
learning (artificial intelligence)
load frequency deviations
load frequency regulation
load regulation
model‐free control strategy
model‐free load frequency control mechanisms
multiarea power system
neurocontrollers
online model‐free
optimal control
power generation control
power generation units
power grids
power system control
power system interconnection
Research Article
uncertain dynamical power system environments
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Summary:Active load variations in uncertain dynamical power system environments affect the energy exchange and efficiency in multi-area power systems, which could compromise the stability of power grids. Hence, model-free load frequency control mechanisms are needed in order to sustain proper performances under such conditions. An online model-free adaptive control scheme based on integral reinforcement learning is proposed to regulate load frequency deviations in multi-area power systems. This scheme takes into account the generation rate constraints of the power generation units and the optimal control decisions do not employ any knowledge about the dynamical model of the power system. This approach reformulates Bellman equation and approximates the associated solving value functions and model-free control strategies using neural networks. The adaption mechanism uses value iteration processes to evaluate the underlying modified-Bellman equation and model-free control strategy in real time. The performance of the adaptive learning scheme is compared with other control methodologies using challenging validation scenarios.
ISSN:1751-8687
1751-8695
1751-8695
DOI:10.1049/iet-gtd.2019.0218