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2019 | OriginalPaper | Buchkapitel

1. Detailed Modeling of CSC-STATCOM with Optimized PSO Based Controller

verfasst von : Sandeep Gupta

Erschienen in: Natural Computing for Unsupervised Learning

Verlag: Springer International Publishing

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Abstract

Today, different types of FACTS devices have been used in the complex transmission power network with many applications to solve the system stability problems. Among all FACTS devices, static synchronous compensators (STATCOMs) are the most important shunt FACTS devices for the vital voltage support and efficient reactive power compensation because of its attractive quick response and wide operating characteristics range. Therefore, in this chapter, first the system description and modeling of three-phase current source converter (CSC)-based STATCOM is presented. Nonlinearity of this modeling is removed with the help of power balance equation and dq-transformation. After that pole-shifting and LQR-based controllers are designed for the CSC-STATCOM. But the best state-feedback gain matrices for different controllers are obtained laboriously through trial and error method, although time-consuming. So this problem is solved with the help of particle swarm optimization (PSO)-based AI method to search the best values of state-feedback gain matrices in a very short time. In this chapter, comparisons among different controller-based CSC-STATCOM devices are also presented with simulation results. The feasibility of the proposed optimized controller-based CSC-STATCOM scheme is demonstrated through simulation in MATLAB. Finally, this chapter shows the detailed modeling of optimized novel controller-based CSC-STATCOM with better simulation outcomes. Design of DC-link reactor for CSC-STATCOM is also discussed in this chapter.

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Nächstes Kapitel A Brief Review and Comparative Study of Nature-Inspired Optimization Algorithms Applied to Power System Control
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Metadaten
Titel
Detailed Modeling of CSC-STATCOM with Optimized PSO Based Controller
verfasst von
Sandeep Gupta
Copyright-Jahr
2019
Buch
Natural Computing for Unsupervised Learning

Print ISBN: 978-3-319-98565-7

Electronic ISBN: 978-3-319-98566-4

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-319-98566-4_1

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