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Applications of Reinforcement Learning and Bayesian Networks Algorithms to the Load-Frequency Control Problem

Applications of Reinforcement Learning and Bayesian Networks Algorithms to the Load-Frequency Control Problem
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Author(s): Fatemeh Daneshfar (University of Kurdistan, Iran)
Copyright: 2014
Pages: 34
Source title: Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications
Source Author(s)/Editor(s): Pandian M. Vasant (Petronas University of Technology, Malaysia)
DOI: 10.4018/978-1-4666-4450-2.ch023

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Abstract

Load-Frequency Control (LFC) is an essential auxiliary service to keep the electrical system reliability at a suitable level. In addition to the regulating area frequency, the LFC system should control the net interchange power with neighboring areas at scheduled values. Therefore, a desirable LFC performance is achieved by effective adjusting of generation to minimize frequency deviation and regulate tie-line power flows. Nowadays such an LFC design is becoming much more complicated and significant due to the complexity of interconnected power systems. However, most of the LFC designs are based on conventional Proportional-Integral (PI) controllers that are tuned online by trial-and-error approaches. These conventional LFC designs are usually suitable for working at specific operating points and are not more efficient for modern and distributed power systems. These problems apply to design of intelligent LFC schemes that are more adaptive and flexible than conventional ones. The present chapter addresses the frequency regulation using Reinforcement Learning (RL) and Bayesian Networks (BNs) approaches for interconnected power systems. RL and BNs are computational learning based solutions which can adapt with environment conditions. They are a kind of Machine Learning (ML) techniques which have many applications in power system engineering. The main advantages of these intelligent-based solutions for the LFC design can be simplicity and intuitive model building that is closely based on the physical power system topology, easy incorporation of uncertainty, and dependent to the frequency response model and also to the power system parameter values.

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