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2015 | Buch

Voltage Security—An Introduction Kapitel lesen Erstes Kapitel lesen

Soft Computing Techniques in Voltage Security Analysis

verfasst von: Kabir Chakraborty, Abhijit Chakrabarti

Verlag: Springer India

Buchreihe : Energy Systems in Electrical Engineering

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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SUCHEN

Über dieses Buch

This book focuses on soft computing techniques for enhancing voltage security in electrical power networks. Artificial neural networks (ANNs) have been chosen as a soft computing tool, since such networks are eminently suitable for the study of voltage security. The different architectures of the ANNs used in this book are selected on the basis of intelligent criteria rather than by a “brute force” method of trial and error. The fundamental aim of this book is to present a comprehensive treatise on power system security and the simulation of power system security. The core concepts are substantiated by suitable illustrations and computer methods. The book describes analytical aspects of operation and characteristics of power systems from the viewpoint of voltage security. The text is self-contained and thorough. It is intended for senior undergraduate students and postgraduate students in electrical engineering. Practicing engineers, Electrical Control Center (ECC) operators and researchers will also find the book useful.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Voltage Security—An Introduction
Abstract
In this chapter, an overview of the basic components of an electrical power network is presented. Threats to power system voltage security and necessity for voltage security analysis are also presented.
Kabir Chakraborty, Abhijit Chakrabarti
Chapter 2. Load Flow Studies
Abstract
Load flow analysis is essential to know the detailed description of a complex power network. Load flow solutions provide the basic computational techniques in order to determine the voltages at various buses and the power flowing through the elements of the system. In this chapter, some basics of load flow analysis have been discussed along with N–R method and fast decoupled method of load flow analysis. These techniques are applied to the online/offline studies of multi-bus power networks using developed soft computing techniques.
Kabir Chakraborty, Abhijit Chakrabarti
Chapter 3. Voltage Stability and Security Analysis
Abstract
This chapter presents comprehensive theoretical concepts of voltage stability and voltage security in power networks. Mechanism of voltage collapse and the basics of contingency analysis of a power system are also covered in this chapter.
Kabir Chakraborty, Abhijit Chakrabarti
Chapter 4. Voltage Security Analysis by Classical Methods
Abstract
In this chapter the voltage security of different multi-bus power networks is analyzed using some classical techniques. The basic theory of some conventional voltage stability indices and a couple of newly developed indices are discussed along with the concept of equivalent two-bus system. The concept of FC–TCR (fixed capacitor-thyristor controlled reactor) type SVC as a reactive compensator for heavily loaded and voltage stressed power networks to augment voltage stability are also proposed.
Kabir Chakraborty, Abhijit Chakrabarti
Chapter 5. Soft Computing Techniques—An Overview
Abstract
This chapter develops a working model of an artificial neuron and introduces neural network architectures, properties, and components. The similarities between biological and artificial neural networks are described along with the architecture of neuron. This chapter also introduces an artificial neural network’s learning process.
Kabir Chakraborty, Abhijit Chakrabarti
Chapter 6. Multilayer Perceptron (MLP) with Error Back-Propagation Learning in Voltage Security Analysis
Abstract
The working of artificial neurons and their architectures stand in stark contrast with their biological counterparts. The theory of feed-forward systems is put on a firm foundation to demonstrate the computational power and limitations of these systems. This chapter develops a working model of feed-forward neural network system and introduces its architectures, properties, and application domains. The concepts of supervised learning in the context of the perceptron and LMS models are described to their fullest complexity with back-propagation learning. The applications of multilayer perceptron (MLP) with error back-propagation learning to the voltage security analysis of multi-bus power networks are also detailed.
Kabir Chakraborty, Abhijit Chakrabarti
Chapter 7. Classification of Voltage Security States Using Unsupervised ANNs
Abstract
This chapter focuses on the application of self-organizing neural networks that are capable of extracting valuable data from their working surroundings. The basic role of self-organization lies in the invention of significant patterns without the intervention of a teaching input. An important aspect of the implementation of such a system is that all adaptations must be based on the data that are accessible locally to the neural connection from the pre- and postsynaptic neuron signals and activations. Self-organization must lead eventually to a state of knowledge that provides useful information concerning the environment from which patterns are drawn. As an alternative to the multilayer perceptron, Kohonen’s self-organizing neural network offers some advantages, particularly in clustering-type applications. Faster learning rate and straightforward interpretation of the classification results make self-organizing map (SOM) an ideal choice for the classification of voltage security states in multi-bus power networks. This chapter describes an artificial neural network-based approach, in which Kohonen’s self-organizing feature map technique has been applied to classify the power system operating states based on their degree of static voltage stability.
Kabir Chakraborty, Abhijit Chakrabarti
Chapter 8. Classification of Voltage Security States Using Supervised ANNs
Abstract
Kohonen’s self-organizing feature map (SOFM) may lead to a few incorrect results because of the absence of supervision in the learning stage, since it is an unsupervised learning artificial neural network. In this chapter, learning vector quantization (LVQ), radial basis function (RBF), and probabilistic neural network (PNN) have been used as the monitoring tool in the state classification task, and these three topics (LVQ, RBF, and PNN) have been given in-depth treatments. The proposed learning vector quantization- and radial basis function-based monitoring have been found to upgrade the accuracy of the electrical power network’s security state classification as compared to that by SOFM, but there are also some misclassifications, whereas PNN brings about one hundred percent classification accuracy without any misclassification.
Kabir Chakraborty, Abhijit Chakrabarti
Backmatter
Metadaten
Titel
Soft Computing Techniques in Voltage Security Analysis
verfasst von
Kabir Chakraborty
Abhijit Chakrabarti
Copyright-Jahr
2015
Verlag
Springer India
Electronic ISBN
978-81-322-2307-8
Print ISBN
978-81-322-2306-1
DOI
https://doi.org/10.1007/978-81-322-2307-8