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Intelligent and Soft Computing Systems for Green Energy

E-BookEPUB2 - DRM Adobe / EPUBE-Book
384 Seiten
Englisch
John Wiley & Sonserschienen am15.05.20231. Auflage
INTELLIGENT AND SOFT COMPUTING SYSTEMS FOR GREEN ENERGY
Written and edited by some of the world's top experts in the field, this exciting new volume provides state-of-the-art research and the latest technological breakthroughs in next-generation computing systems for the energy sector, striving to bring the science toward sustainability.
Real-world problems need intelligent solutions. Across many industries and fields, intelligent and soft computing systems, using such developing technologies as artificial intelligence and Internet of Things, are quickly becoming important tools for scientists, engineers, and other professionals for solving everyday problems in practical situations.
This book aims to bring together the research that has been carried out in the field of intelligent and soft computing systems. Intelligent and soft computing systems involves expertise from various domains of research, such as electrical engineering, computer engineering, and mechanical engineering. This book will serve as a point of convergence wherein all these domains come together.
The various chapters are configured to address the challenges faced in intelligent and soft computing systems from various fields and possible solutions. The outcome of this book can serve as a potential resource for industry professionals and researchers working in the domain of intelligent and soft computing systems.
To list a few soft computing techniques, neural-based load forecasting, IoT-enabled smart grids, and blockchain technology for energy trading. Whether for the veteran engineer or the student learning the latest breakthroughs, this exciting new volume is a must-have for any library.


A. Chitra is an associate professor in the School of Electrical Engineering, at Vellore Institute of Technology, Vellore, India. She has published many papers in reputed journals and conferences and is a Board of Studies member at Pondicherry Engineering College, where she received a Gold Medal while studying for her MTech and also where she earned her PhD.
V. Indragandhi, PhD, is an associate professor in the School of Electrical Engineering, VIT, Vellore, Tamilnadu. She received her PhD from Anna University in Chennai, India. She has over 12 years of experience in the area of power electronics and renewable energy systems and has authored over 100 research articles in leading peer-reviewed international journals. She has filed three patents and has one book to her credit. She has also received the best researcher award from NFED, Coimbatore and from VIT.
W. Razia Sultana, PhD, is an associate professor in the School of Electrical Engineering, at the Vellore Institute of Technology University, Vellore, Tamil Nadu, India, where she also received her PhD.mehr
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Produkt

KlappentextINTELLIGENT AND SOFT COMPUTING SYSTEMS FOR GREEN ENERGY
Written and edited by some of the world's top experts in the field, this exciting new volume provides state-of-the-art research and the latest technological breakthroughs in next-generation computing systems for the energy sector, striving to bring the science toward sustainability.
Real-world problems need intelligent solutions. Across many industries and fields, intelligent and soft computing systems, using such developing technologies as artificial intelligence and Internet of Things, are quickly becoming important tools for scientists, engineers, and other professionals for solving everyday problems in practical situations.
This book aims to bring together the research that has been carried out in the field of intelligent and soft computing systems. Intelligent and soft computing systems involves expertise from various domains of research, such as electrical engineering, computer engineering, and mechanical engineering. This book will serve as a point of convergence wherein all these domains come together.
The various chapters are configured to address the challenges faced in intelligent and soft computing systems from various fields and possible solutions. The outcome of this book can serve as a potential resource for industry professionals and researchers working in the domain of intelligent and soft computing systems.
To list a few soft computing techniques, neural-based load forecasting, IoT-enabled smart grids, and blockchain technology for energy trading. Whether for the veteran engineer or the student learning the latest breakthroughs, this exciting new volume is a must-have for any library.


A. Chitra is an associate professor in the School of Electrical Engineering, at Vellore Institute of Technology, Vellore, India. She has published many papers in reputed journals and conferences and is a Board of Studies member at Pondicherry Engineering College, where she received a Gold Medal while studying for her MTech and also where she earned her PhD.
V. Indragandhi, PhD, is an associate professor in the School of Electrical Engineering, VIT, Vellore, Tamilnadu. She received her PhD from Anna University in Chennai, India. She has over 12 years of experience in the area of power electronics and renewable energy systems and has authored over 100 research articles in leading peer-reviewed international journals. She has filed three patents and has one book to her credit. She has also received the best researcher award from NFED, Coimbatore and from VIT.
W. Razia Sultana, PhD, is an associate professor in the School of Electrical Engineering, at the Vellore Institute of Technology University, Vellore, Tamil Nadu, India, where she also received her PhD.
Details
Weitere ISBN/GTIN9781394167500
ProduktartE-Book
EinbandartE-Book
FormatEPUB
Format Hinweis2 - DRM Adobe / EPUB
FormatFormat mit automatischem Seitenumbruch (reflowable)
Erscheinungsjahr2023
Erscheinungsdatum15.05.2023
Auflage1. Auflage
Seiten384 Seiten
SpracheEnglisch
Dateigrösse49731 Kbytes
Artikel-Nr.11725361
Rubriken
Genre9201

Inhalt/Kritik

Leseprobe

1
Placement and Sizing of Distributed Generator and Capacitor in a Radial Distribution System Considering Load Growth

By Robert X. Perez

G. Manikanta1, N. Kirn Kumar2*, Ashish Mani1 and V. Indragandhi3

1Electrical & Electronics Engineering Department, A.S.E.T, Amity University Uttar Pradesh, Noida, UP, India

2Department of Electrical & Electronics Engineering, M S Ramaiah Institute of Technology, Bangalore, India

3School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

Abstract

Annual load growth in a distribution system is expanding consistently, which results in underprivileged voltage-regulation and increment in power losses. Independent implementation of Distributed generation (DGs) along with capacitors is chosen as alternative techniques to decrease the power loss in the network. Optimal location and capacity of Capacitors along with DGs not only maximize the percentage power loss reduction but also increase the voltage profile, if optimal location and capacity is appropriate. Inappropriate placement and competence of capacitors and DGs leads the system to an increase in power loss. The best location and competence of capacitors and DGs is a difficult nondifferentiable combinatorial optimization problem, which has been applied to solve various engineering optimization problems like improvement in reliability, loadabilty, loss minimization, etc., using various analytical and evolutionary algorithms. In this study economic load growth is modelled with a predetermined yearly load expansion for the base year and next five years. In this work the main contributions are made with placing and sizing the DGs and Capacitors to minimize the power losses for every year. Tabulated results demonstrate that simultaneous implementation of Capacitor and DGs has high reduction in power loss for every year including base year in comparison with independent implementation of DGs and Capacitors. For obtaining the best location and competence of capacitors and DGs an Adaptive Quantum inspired evolutionary Algorithm (AQiEA) is applied successfully. AQiEA uses probabilistic representation with Q-bit and does not require any additional operators. The effectiveness of AQiEA is verified on a standard test bus system, i.e., 85 bus system. Simulated results exhibit that the proposed algorithm has superior performance in comparison to the algorithms in the available literature.

Keywords: Distributed generators, capacitor, load growth, power losses, 85 bus system, AQiEA
1.1 Introduction

The power demand at distribution network keeps on increasing day to day and in some scenarios the generated power is unable to meet the required load demand. Load demand at distributed network is exponentially increasing from day to day, due to industrial, domestic, commercial, municipal, residential and irrigation needs. Load growth in distribution network is a natural phenomenon which results in increased power losses (both active and reactive) and increased voltage drop. Many methods and techniques have been executed in distributed networks in order to decrease the losses. Over the last few decades, DGs and Capacitors in the distributed network are used to reduce the losses. Implementation of DG in the network will reduce losses and also improve the system voltage. DGs are defined as small power generating sources, located nearer to the load centers and size varies from kW to few MW [1]. DGs are used in distribution system due to its ease of implementation, environmental friendly technologies and low maintenance [2]. Different types of DG are available with respect to their modular structure and size. Therefore, their impact on the distribution system varies depending on location and capacity [3]. Compensation of reactive power in the distribution network is generally provided by the capacitors. Installation of Capacitors nearer to load centres reduces the power losses and maintains the voltage profile within permissible limits [4].

Analytical-based methods are easy to implement; their major drawback is implementation of single DG or Capacitor with large size in order to reduce the losses [5-7]. In most of the works, minimization of power loss in distribution system with DG integration, population-based meta-heuristics are used as solution strategies. Symbiotic Organism Search [8], Particle Swarm Optimization [9], Simulated Annealing [10], Big-Bang Big-crunch optimization [11] and Fire Work Algorithm [12] are some of the well-known optimization techniques used to reduce the losses. However, Simulated Annealing [13], Firefly Algorithm [14], Plant Growth Simulation Algorithm [15], Teaching Learning-Based Optimization [16] and Particle Swarm Optimization [17] are some population-based meta-heuristic techniques used for optimal location and capacity of Capacitors to reduce the losses. Some methods have considered only injection of real power (DG) and some other methods considered only injecting the reactive power (Capacitor) into the system, and other methods have considered simultaneous implementation of capacitors and DG [18, 19].

In this study, AQiEA was implemented to find the optimal placement and capacity of Capacitors and DGs in distribution network without violating the limits. The objective of reducing losses was considered by implementing DGs and Capacitor placements both at the same time. The yearly load increment was calculated in advance for continuous successive five years. Effect of load increase was calculated based on with and without inclusion of Capacitors and DGs. In recent times, AQiEA was applied on DG network for optimal location of capacitor was successfully applied [20], optimal DG problem [21-23], DG operation along with the network re arrangement [24], simultaneous implementation of both DG and Capacitor [25], ceramic grinding [26] and constraint handling technique [27].

The rest of the chapter was prepared as follows. The problem formulation section describes the objective to minimize the power loss by implementation of both Capacitor and DGs with predetermined annual load growth. Placement and sizing of Capacitors and DGs is a tough task and also a non-differentiable combinational optimization problem. The problem of optimization of placing and sizing of capacitors was solved by using AQiEA, which is described in the section Algorithm. The efficiency of AQiEA was compared with some other algorithms in the Results and Discussions section. In the final section of the chapter a conclusion is provided.
1.2 Problem Formulation

A load forecast for five years was precalculated based on the past and present load growth, which was used as an objective function for minimization of power losses. This was assumed based on yearly load growth in the DG network and determined by using growth rate plus the initial load in the network [28].

(1.1)

(1.2)

Where PLk(y) & QLk(y) are the real and reactive load in y year, PLk(0) & QLk(0) are the real and reactive load at base year, g is the annual load growth which is assumed as 7.5% and represents number of year (maximum number of years considered for this study is 5).

The primary goal of this research is to reduce power loss for every year by simultaneous implementation of Capacitor and DG. The generalized objective function is given as follows:

(1.3)

Where, Pm & Qm are real and reactive power injections at mth bus. Vm indicates voltage at mth bus, Rm & Xm indicates magnitude of resistance and inductance.

Inequality Constraint:

Operation of DG and Capacitor:

(1.4)

(1.5)

The power injected by DG into the network should be within the limits. PDG(m) & QDG(m) are injected real and reactive powers.

Voltage limit:

(1.6)

The voltage produced at each individual bus in the network has to be in acceptable limits.

Equality Constraint:

Power injection:

(1.7)

The total power injected by DGs and Capacitor in addition to the substation must be equal or less than its total load demand and losses of the system. Where Psub, Ploss and Pload represents substation power, power loss and demand in the system.
1.3 Algorithm

Quantum-inspired evolutionary algorithms:

QiEA uses probabilistic representation with Q-bit and it has good characteristic representation of population diversity compared with other representations. It is defined as the smallest unit of information in a quantum computer [29]. It could be represented in two states as state a , and state b or the sum of both states. It can be shown in the following equation as

(1.8)

Here probability amplitudes Y1 and Y2 are connected to its corresponding states. Y1 2 and Y2 2 will provides probability of Q-bits which are to be found in state a or b respectively. A Q-bit individual q with m-bits is given as follows

(1.9)

Here, |Y1i|2 + |Y2i|2 = 1, i = 1,2,â¦.m

In QiEA, qubit probabilistic nature is widely used for maintaining diversity. Quantum gate operators are used...
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