Application of Artificial Neural Network in Power System Analysis
Author: Dr.Gitanjali Saha
Publisher: Book Rivers
Published: 2022-11-22
Total Pages: 169
ISBN-13: 935515609X
DOWNLOAD EBOOKPosts
Application of Signal Processing Tools and Artificial Neural Network in Diagnosis of Power System Faults
Author: Nabamita Banerjee Roy
Publisher: CRC Press
Published: 2021-07-21
Total Pages: 144
ISBN-13: 1000414906
DOWNLOAD EBOOKExplores methods of fault identification through programming and simulation in MATLAB Examines signal processing tools and their applications with examples Provides knowledge of artificial neural networks and their applications with illustrations Uses PNN and BPNN to identify the different types of faults and obtain their corresponding locations Discusses the programming of signal processing using Wavelet Transform and S-Transform
Application of Machine Learning and Deep Learning Methods to Power System Problems
Author: Morteza Nazari-Heris
Publisher: Springer Nature
Published: 2021-11-21
Total Pages: 391
ISBN-13: 3030776964
DOWNLOAD EBOOKThis book evaluates the role of innovative machine learning and deep learning methods in dealing with power system issues, concentrating on recent developments and advances that improve planning, operation, and control of power systems. Cutting-edge case studies from around the world consider prediction, classification, clustering, and fault/event detection in power systems, providing effective and promising solutions for many novel challenges faced by power system operators. Written by leading experts, the book will be an ideal resource for researchers and engineers working in the electrical power engineering and power system planning communities, as well as students in advanced graduate-level courses.
Research Anthology on Artificial Neural Network Applications
Author: Management Association, Information Resources
Publisher: IGI Global
Published: 2021-07-16
Total Pages: 1575
ISBN-13: 1668424096
DOWNLOAD EBOOKArtificial neural networks (ANNs) present many benefits in analyzing complex data in a proficient manner. As an effective and efficient problem-solving method, ANNs are incredibly useful in many different fields. From education to medicine and banking to engineering, artificial neural networks are a growing phenomenon as more realize the plethora of uses and benefits they provide. Due to their complexity, it is vital for researchers to understand ANN capabilities in various fields. The Research Anthology on Artificial Neural Network Applications covers critical topics related to artificial neural networks and their multitude of applications in a number of diverse areas including medicine, finance, operations research, business, social media, security, and more. Covering everything from the applications and uses of artificial neural networks to deep learning and non-linear problems, this book is ideal for computer scientists, IT specialists, data scientists, technologists, business owners, engineers, government agencies, researchers, academicians, and students, as well as anyone who is interested in learning more about how artificial neural networks can be used across a wide range of fields.
Artificial Intelligence Techniques in Power Systems
Author: Kevin Warwick
Publisher: IET
Published: 1997
Total Pages: 324
ISBN-13: 9780852968970
DOWNLOAD EBOOKThe intention of this book is to give an introduction to, and an overview of, the field of artificial intelligence techniques in power systems, with a look at various application studies.
Applications of Artificial Neural Networks for Nonlinear Data
Author: Patel, Hiral Ashil
Publisher: IGI Global
Published: 2020-09-25
Total Pages: 315
ISBN-13: 1799840433
DOWNLOAD EBOOKProcessing information and analyzing data efficiently and effectively is crucial for any company that wishes to stay competitive in its respective market. Nonlinear data presents new challenges to organizations, however, due to its complexity and unpredictability. The only technology that can properly handle this form of data is artificial neural networks. These modeling systems present a high level of benefits in analyzing complex data in a proficient manner, yet considerable research on the specific applications of these intelligent components is significantly deficient. Applications of Artificial Neural Networks for Nonlinear Data is a collection of innovative research on the contemporary nature of artificial neural networks and their specific implementations within data analysis. While highlighting topics including propagation functions, optimization techniques, and learning methodologies, this book is ideally designed for researchers, statisticians, academicians, developers, scientists, practitioners, students, and educators seeking current research on the use of artificial neural networks in diagnosing and solving nonparametric problems.
Artificial Neural Networks in Hydrology
Author: R.S. Govindaraju
Publisher: Springer Science & Business Media
Published: 2013-03-09
Total Pages: 338
ISBN-13: 9401593418
DOWNLOAD EBOOKR. S. GOVINDARAJU and ARAMACHANDRA RAO School of Civil Engineering Purdue University West Lafayette, IN. , USA Background and Motivation The basic notion of artificial neural networks (ANNs), as we understand them today, was perhaps first formalized by McCulloch and Pitts (1943) in their model of an artificial neuron. Research in this field remained somewhat dormant in the early years, perhaps because of the limited capabilities of this method and because there was no clear indication of its potential uses. However, interest in this area picked up momentum in a dramatic fashion with the works of Hopfield (1982) and Rumelhart et al. (1986). Not only did these studies place artificial neural networks on a firmer mathematical footing, but also opened the dOOf to a host of potential applications for this computational tool. Consequently, neural network computing has progressed rapidly along all fronts: theoretical development of different learning algorithms, computing capabilities, and applications to diverse areas from neurophysiology to the stock market. . Initial studies on artificial neural networks were prompted by adesire to have computers mimic human learning. As a result, the jargon associated with the technical literature on this subject is replete with expressions such as excitation and inhibition of neurons, strength of synaptic connections, learning rates, training, and network experience. ANNs have also been referred to as neurocomputers by people who want to preserve this analogy.
Automatic Learning Techniques in Power Systems
Author: Louis A. Wehenkel
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 297
ISBN-13: 1461554519
DOWNLOAD EBOOKAutomatic learning is a complex, multidisciplinary field of research and development, involving theoretical and applied methods from statistics, computer science, artificial intelligence, biology and psychology. Its applications to engineering problems, such as those encountered in electrical power systems, are therefore challenging, while extremely promising. More and more data have become available, collected from the field by systematic archiving, or generated through computer-based simulation. To handle this explosion of data, automatic learning can be used to provide systematic approaches, without which the increasing data amounts and computer power would be of little use. Automatic Learning Techniques in Power Systems is dedicated to the practical application of automatic learning to power systems. Power systems to which automatic learning can be applied are screened and the complementary aspects of automatic learning, with respect to analytical methods and numerical simulation, are investigated. This book presents a representative subset of automatic learning methods - basic and more sophisticated ones - available from statistics (both classical and modern), and from artificial intelligence (both hard and soft computing). The text also discusses appropriate methodologies for combining these methods to make the best use of available data in the context of real-life problems. Automatic Learning Techniques in Power Systems is a useful reference source for professionals and researchers developing automatic learning systems in the electrical power field.
Applied Artificial Neural Network Methods For Engineers And Scientists: Solving Algebraic Equations
Author: Snehashish Chakraverty
Publisher: World Scientific
Published: 2021-01-26
Total Pages: 192
ISBN-13: 9811230226
DOWNLOAD EBOOKThe aim of this book is to handle different application problems of science and engineering using expert Artificial Neural Network (ANN). As such, the book starts with basics of ANN along with different mathematical preliminaries with respect to algebraic equations. Then it addresses ANN based methods for solving different algebraic equations viz. polynomial equations, diophantine equations, transcendental equations, system of linear and nonlinear equations, eigenvalue problems etc. which are the basic equations to handle the application problems mentioned in the content of the book. Although there exist various methods to handle these problems, but sometimes those may be problem dependent and may fail to give a converge solution with particular discretization. Accordingly, ANN based methods have been addressed here to solve these problems. Detail ANN architecture with step by step procedure and algorithm have been included. Different example problems are solved with respect to various application and mathematical problems. Convergence plots and/or convergence tables of the solutions are depicted to show the efficacy of these methods. It is worth mentioning that various application problems viz. Bakery problem, Power electronics applications, Pole placement, Electrical Network Analysis, Structural engineering problem etc. have been solved using the ANN based methods.
Process Neural Networks
Author: Xingui He
Publisher: Springer Science & Business Media
Published: 2010-07-05
Total Pages: 240
ISBN-13: 3540737626
DOWNLOAD EBOOKFor the first time, this book sets forth the concept and model for a process neural network. You’ll discover how a process neural network expands the mapping relationship between the input and output of traditional neural networks and greatly enhances the expression capability of artificial neural networks. Detailed illustrations help you visualize information processing flow and the mapping relationship between inputs and outputs.
