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Big Data Application in Power Systems

Big Data Application in Power Systems Book
Author : Reza Arghandeh,Yuxun Zhou
Publisher : Elsevier
Release : 2017-11-27
ISBN : 0128119691
Language : En, Es, Fr & De

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Book Description :

Big Data Application in Power Systems brings together experts from academia, industry and regulatory agencies who share their understanding and discuss the big data analytics applications for power systems diagnostics, operation and control. Recent developments in monitoring systems and sensor networks dramatically increase the variety, volume and velocity of measurement data in electricity transmission and distribution level. The book focuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous and spatiotemporal data. The book chapters discuss challenges, opportunities, success stories and pathways for utilizing big data value in smart grids. Provides expert analysis of the latest developments by global authorities Contains detailed references for further reading and extended research Provides additional cross-disciplinary lessons learned from broad disciplines such as statistics, computer science and bioinformatics Focuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous and spatiotemporal data

Big Data Analytics in Future Power Systems

Big Data Analytics in Future Power Systems Book
Author : Ahmed F. Zobaa,Trevor J. Bihl
Publisher : CRC Press
Release : 2018-08-14
ISBN : 1351601296
Language : En, Es, Fr & De

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Book Description :

Power systems are increasingly collecting large amounts of data due to the expansion of the Internet of Things into power grids. In a smart grids scenario, a huge number of intelligent devices will be connected with almost no human intervention characterizing a machine-to-machine scenario, which is one of the pillars of the Internet of Things. The book characterizes and evaluates how the emerging growth of data in communications networks applied to smart grids will impact the grid efficiency and reliability. Additionally, this book discusses the various security concerns that become manifest with Big Data and expanded communications in power grids. Provide a general description and definition of big data, which has been gaining significant attention in the research community. Introduces a comprehensive overview of big data optimization methods in power system. Reviews the communication devices used in critical infrastructure, especially power systems; security methods available to vet the identity of devices; and general security threats in CI networks. Presents applications in power systems, such as power flow and protection. Reviews electricity theft concerns and the wide variety of data-driven techniques and applications developed for electricity theft detection.

Big Data in Engineering Applications

Big Data in Engineering Applications Book
Author : Sanjiban Sekhar Roy,Pijush Samui,Ravinesh Deo,Stavros Ntalampiras
Publisher : Springer
Release : 2018-05-02
ISBN : 9811084769
Language : En, Es, Fr & De

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Book Description :

This book presents the current trends, technologies, and challenges in Big Data in the diversified field of engineering and sciences. It covers the applications of Big Data ranging from conventional fields of mechanical engineering, civil engineering to electronics, electrical, and computer science to areas in pharmaceutical and biological sciences. This book consists of contributions from various authors from all sectors of academia and industries, demonstrating the imperative application of Big Data for the decision-making process in sectors where the volume, variety, and velocity of information keep increasing. The book is a useful reference for graduate students, researchers and scientists interested in exploring the potential of Big Data in the application of engineering areas.

Data Mining Application to Power Grid PMU Data

Data Mining Application to Power Grid PMU Data Book
Author : Tianzhixi Yin
Publisher : Unknown
Release : 2018
ISBN : 9780438020108
Language : En, Es, Fr & De

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Book Description :

The reliability of the power system is of vital importance to daily life. Power outages, either small or big, cause economic loss and inconvenience. The effort to better understand the behaviors of the power grid has a long history. Several years ago, engineers and researchers started to modernize the national power grid in the United States by moving it into the next generation called SmartGrid. The massive installation of Phasor Measurement Units (PMUs) is the highlight of this movement. Compared to the sensors from the last generation in the power grid, PMUs can produce more accurate and rapid information regarding the state of the grid. Data from PMUs are usually 30 samples to 60 samples per second and contain both voltage magnitude and angle measurements. The data is high-dimensional with hundreds or thousands of signals and is highly correlated across signals. One advantage of PMU data is that it is time aligned throught GPS. The biggest challenge in the use of PMU data is the massive amount of data, which causes difficulties for storage, pre-processing, analysis, and visualization. A whole year of PMU data can be several terabytes depending on the number of signals in the data. In fact, the information provided by PMUs is now so big that it is difficult for scientists to handle or easily comprehend. Meanwhile, many exciting accomplishments have been seen in various fields using data mining. Data mining has become increasingly important with the appearance of various kinds of big data. The power grid data analytics is a good example of such a big data problem. There has been an increase of data mining applications in the power systems research field in recent years, partly due to advancements in data mining. There has been much work on this topic in the last 10 years. This research work, which has been done at the University of Wyoming and Pacific Northwest National Laboratory, consists of data analytics on simulated PMU data from the MinniWECC system and real PMU data from the Western Electricity Coordinating Council (WECC) system for the 2008 to 2009 and 2016 to 2017 operating seasons. This work contains measurement-based power system offline studies involving event detection, event classification, abnormal operating conditions, and potential online applications. The simulated study discusses the implementation and performance of various machine learning algorithms for classifying power system event types and event locations. A simple feature extraction method is applied. The contribution of this study is to demonstrate how data mining techniques can be used to incorporate information from PMU data to assess the system condition. Moreover, data mining techniques are applied to historical data consisting of PMU measurements from WECC from June 2008 to June 2009. The main objective is to classify abnormal and normal power grid modal behavior of the WECC interconnect at the daily scale. The data is transformed to the frequency domain to represent operating conditions of each day. A closer investigation of misclassified days is also conducted to look at abnormal system behaviors at the hourly scale. The research contribution of this study is the application of data mining techniques to power grid data in the frequency domain to identify various power system events, especially large scale events both in size and in duration. The third part of this dissertation extends the findings in the simulated study and applies updated methodologies to PMU data from October 2016 to May 2017. This work involves training machine learning algorithms to detect and classify power system events in the time domain. Different machine learning algorithms are applied and a new algorithm is developed to enhance the final algorithm. The results show that the proposed algorithm can successfully detect and classify power system events at high accuracy in under one second. This research demonstrates the potential for an on-line application of achieving near real-time power system situational awareness.

Predictive Modelling for Energy Management and Power Systems Engineering

Predictive Modelling for Energy Management and Power Systems Engineering Book
Author : Ravinesh Deo,Pijush Samui,Sanjiban Sekhar Roy
Publisher : Elsevier
Release : 2020-09-30
ISBN : 012817773X
Language : En, Es, Fr & De

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Book Description :

Predictive Modeling for Energy Management and Power Systems Engineering introduces readers to the cutting-edge use of big data and large computational infrastructures in energy demand estimation and power management systems. The book supports engineers and scientists who seek to become familiar with advanced optimization techniques for power systems designs, optimization techniques and algorithms for consumer power management, and potential applications of machine learning and artificial intelligence in this field. The book provides modeling theory in an easy-to-read format, verified with on-site models and case studies for specific geographic regions and complex consumer markets. Presents advanced optimization techniques to improve existing energy demand system Provides data-analytic models and their practical relevance in proven case studies Explores novel developments in machine-learning and artificial intelligence applied in energy management Provides modeling theory in an easy-to-read format

IBM Power System S822LC for Big Data Technical Overview and Introduction

IBM Power System S822LC for Big Data  Technical Overview and Introduction Book
Author : Scott Vetter,David Barron,Alexandre Bicas Caldeira,Volker Haug,IBM Redbooks
Publisher : IBM Redbooks
Release : 2017-08-29
ISBN : 0738455776
Language : En, Es, Fr & De

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Book Description :

This IBM® RedpaperTM publication is a comprehensive guide that covers the IBM Power System S822LC for Big Data (8001-22C) server that uses the latest IBM POWER8® processor technology and supports Linux operating systems (OSs). The objective of this paper is to introduce the Power S822LC for Big Data offerings and their relevant functions as related to targeted application workloads. The new Linux scale-out systems provide differentiated performance, scalability, and low acquisition cost, including: Consolidated server footprint with up to 66% more virtual machines (VMs) per server than competitive x86 servers Superior data throughput and performance for high-value Linux workloads, such as big data, analytic, and industry applications Up to 12 LFF drives that are installed within the chassis to meet storage-rich application requirements Superior application performance due to a 2x per core performance advantage over x86-based systems Leadership data through put enabled by POWER8 multithreading with up to 4x more threads than x86 designs Acceleration of bid data workloads with up to two GPUs and superior I/O bandwidth with Coherent Accelerator Processor Interface (CAPI) This publication is for professionals who want to acquire a better understanding of IBM Power SystemsTM products. The intended audience includes: Clients Sales and marketing professionals Technical support professionals IBM Business Partners Independent software vendors

Deep Learning Techniques and Optimization Strategies in Big Data Analytics

Deep Learning Techniques and Optimization Strategies in Big Data Analytics Book
Author : Thomas, J. Joshua,Karagoz, Pinar,Ahamed, B. Bazeer,Vasant, Pandian
Publisher : IGI Global
Release : 2019-11-29
ISBN : 1799811948
Language : En, Es, Fr & De

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Book Description :

Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there’s a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.

Harness the Power of Big Data The IBM Big Data Platform

Harness the Power of Big Data The IBM Big Data Platform Book
Author : Paul Zikopoulos,Dirk deRoos,Krishnan Parasuraman,Thomas Deutsch,James Giles,David Corrigan
Publisher : McGraw Hill Professional
Release : 2012-11-08
ISBN : 0071808183
Language : En, Es, Fr & De

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Book Description :

Boost your Big Data IQ! Gain insight into how to govern and consume IBM’s unique in-motion and at-rest Big Data analytic capabilities Big Data represents a new era of computing—an inflection point of opportunity where data in any format may be explored and utilized for breakthrough insights—whether that data is in-place, in-motion, or at-rest. IBM is uniquely positioned to help clients navigate this transformation. This book reveals how IBM is infusing open source Big Data technologies with IBM innovation that manifest in a platform capable of "changing the game." The four defining characteristics of Big Data—volume, variety, velocity, and veracity—are discussed. You’ll understand how IBM is fully committed to Hadoop and integrating it into the enterprise. Hear about how organizations are taking inventories of their existing Big Data assets, with search capabilities that help organizations discover what they could already know, and extend their reach into new data territories for unprecedented model accuracy and discovery. In this book you will also learn not just about the technologies that make up the IBM Big Data platform, but when to leverage its purpose-built engines for analytics on data in-motion and data at-rest. And you’ll gain an understanding of how and when to govern Big Data, and how IBM’s industry-leading InfoSphere integration and governance portfolio helps you understand, govern, and effectively utilize Big Data. Industry use cases are also included in this practical guide.

Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making

Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making Book
Author : Cengiz Kahraman,Selcuk Cebi,Sezi Cevik Onar,Basar Oztaysi,A. Cagri Tolga,Irem Ucal Sari
Publisher : Springer
Release : 2019-07-05
ISBN : 3030237567
Language : En, Es, Fr & De

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Book Description :

This book includes the proceedings of the Intelligent and Fuzzy Techniques INFUS 2019 Conference, held in Istanbul, Turkey, on July 23–25, 2019. Big data analytics refers to the strategy of analyzing large volumes of data, or big data, gathered from a wide variety of sources, including social networks, videos, digital images, sensors, and sales transaction records. Big data analytics allows data scientists and various other users to evaluate large volumes of transaction data and other data sources that traditional business systems would be unable to tackle. Data-driven and knowledge-driven approaches and techniques have been widely used in intelligent decision-making, and they are increasingly attracting attention due to their importance and effectiveness in addressing uncertainty and incompleteness. INFUS 2019 focused on intelligent and fuzzy systems with applications in big data analytics and decision-making, providing an international forum that brought together those actively involved in areas of interest to data science and knowledge engineering. These proceeding feature about 150 peer-reviewed papers from countries such as China, Iran, Turkey, Malaysia, India, USA, Spain, France, Poland, Mexico, Bulgaria, Algeria, Pakistan, Australia, Lebanon, and Czech Republic.

The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy

The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy Book
Author : John MacIntyre
Publisher : Springer Nature
Release : 2021-05-13
ISBN : 3030627438
Language : En, Es, Fr & De

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Book Description :

Download The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy book written by John MacIntyre, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Big Data Analytics Systems Algorithms Applications

Big Data Analytics  Systems  Algorithms  Applications Book
Author : C.S.R. Prabhu,Aneesh Sreevallabh Chivukula,Aditya Mogadala,Rohit Ghosh,L.M. Jenila Livingston
Publisher : Springer Nature
Release : 2019-10-14
ISBN : 9811500940
Language : En, Es, Fr & De

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Book Description :

This book provides a comprehensive survey of techniques, technologies and applications of Big Data and its analysis. The Big Data phenomenon is increasingly impacting all sectors of business and industry, producing an emerging new information ecosystem. On the applications front, the book offers detailed descriptions of various application areas for Big Data Analytics in the important domains of Social Semantic Web Mining, Banking and Financial Services, Capital Markets, Insurance, Advertisement, Recommendation Systems, Bio-Informatics, the IoT and Fog Computing, before delving into issues of security and privacy. With regard to machine learning techniques, the book presents all the standard algorithms for learning – including supervised, semi-supervised and unsupervised techniques such as clustering and reinforcement learning techniques to perform collective Deep Learning. Multi-layered and nonlinear learning for Big Data are also covered. In turn, the book highlights real-life case studies on successful implementations of Big Data Analytics at large IT companies such as Google, Facebook, LinkedIn and Microsoft. Multi-sectorial case studies on domain-based companies such as Deutsche Bank, the power provider Opower, Delta Airlines and a Chinese City Transportation application represent a valuable addition. Given its comprehensive coverage of Big Data Analytics, the book offers a unique resource for undergraduate and graduate students, researchers, educators and IT professionals alike.

Fifth International Conference on Power System Management and Control

Fifth International Conference on Power System Management and Control Book
Author : Anonim
Publisher : Inst of Engineering & Technology
Release : 2002
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Fifth International Conference on Power System Management and Control book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

2020 IEEE International Conference on Industrial Application of Artificial Intelligence IAAI

2020 IEEE International Conference on Industrial Application of Artificial Intelligence  IAAI  Book
Author : IEEE Staff
Publisher : Unknown
Release : 2020-12-25
ISBN : 9780738142852
Language : En, Es, Fr & De

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Book Description :

This conference aims to provide an opportunity for researchers to publish their gifted technological studies of advanced method in artificial intelligence and big data The main focus of this conference will be on the state of the art advances in the emerging applications in following topics Potential topics include, but are not limited to Computer vision with applications Natural language processing with applications Artificial Intelligence in Industry Data Science Applications on Power Systems Big data analysis in industrial application Smart Grid with application Intelligent information system with application Information restore enhancement with application Classification and evaluation with application Relation mining with application Machine learning in industry

AI and Big Data on IBM Power Systems Servers

AI and Big Data on IBM Power Systems Servers Book
Author : Scott Vetter,Ivaylo B. Bozhinov,Anto A John,Rafael Freitas de Lima,Ahmed.(Mash) Mashhour,James Van Oosten,Fernando Vermelho,Allison White,IBM Redbooks
Publisher : IBM Redbooks
Release : 2019-04-10
ISBN : 0738457515
Language : En, Es, Fr & De

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Book Description :

As big data becomes more ubiquitous, businesses are wondering how they can best leverage it to gain insight into their most important business questions. Using machine learning (ML) and deep learning (DL) in big data environments can identify historical patterns and build artificial intelligence (AI) models that can help businesses to improve customer experience, add services and offerings, identify new revenue streams or lines of business (LOBs), and optimize business or manufacturing operations. The power of AI for predictive analytics is being harnessed across all industries, so it is important that businesses familiarize themselves with all of the tools and techniques that are available for integration with their data lake environments. In this IBM® Redbooks® publication, we cover the best practices for deploying and integrating some of the best AI solutions on the market, including: IBM Watson Machine Learning Accelerator (see note for product naming) IBM Watson Studio Local IBM Power SystemsTM IBM SpectrumTM Scale IBM Data Science Experience (IBM DSX) IBM Elastic StorageTM Server Hortonworks Data Platform (HDP) Hortonworks DataFlow (HDF) H2O Driverless AI We map out all the integrations that are possible with our different AI solutions and how they can integrate with your existing or new data lake. We also walk you through some of our client use cases and show you how some of the industry leaders are using Hortonworks, IBM PowerAI, and IBM Watson Studio Local to drive decision making. We also advise you on your deployment options, when to use a GPU, and why you should use the IBM Elastic Storage Server (IBM ESS) to improve storage management. Lastly, we describe how to integrate IBM Watson Machine Learning Accelerator and Hortonworks with or without IBM Watson Studio Local, how to access real-time data, and security. Note: IBM Watson Machine Learning Accelerator is the new product name for IBM PowerAI Enterprise. Note: Hortonworks merged with Cloudera in January 2019. The new company is called Cloudera. References to Hortonworks as a business entity in this publication are now referring to the merged company. Product names beginning with Hortonworks continue to be marketed and sold under their original names.

IEEE Conference Record of 1968 Industrial and Commercial Power Systems and Electric Space Heating and Air Conditioning Joint Technical Conference

IEEE Conference Record of 1968 Industrial and Commercial Power Systems and Electric Space Heating and Air Conditioning Joint Technical Conference Book
Author : Anonim
Publisher : Unknown
Release : 1968
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download IEEE Conference Record of 1968 Industrial and Commercial Power Systems and Electric Space Heating and Air Conditioning Joint Technical Conference book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Power System Monitoring and Control

Power System Monitoring and Control Book
Author : Institution of Electrical Engineers. Power Division
Publisher : Unknown
Release : 1980
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Power System Monitoring and Control book written by Institution of Electrical Engineers. Power Division, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Applications of Big Data Analytics

Applications of Big Data Analytics Book
Author : Mohammed M. Alani,Hissam Tawfik,Mohammed Saeed,Obinna Anya
Publisher : Springer
Release : 2018-07-23
ISBN : 3319764721
Language : En, Es, Fr & De

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Book Description :

This timely text/reference reviews the state of the art of big data analytics, with a particular focus on practical applications. An authoritative selection of leading international researchers present detailed analyses of existing trends for storing and analyzing big data, together with valuable insights into the challenges inherent in current approaches and systems. This is further supported by real-world examples drawn from a broad range of application areas, including healthcare, education, and disaster management. The text also covers, typically from an application-oriented perspective, advances in data science in such areas as big data collection, searching, analysis, and knowledge discovery. Topics and features: Discusses a model for data traffic aggregation in 5G cellular networks, and a novel scheme for resource allocation in 5G networks with network slicing Explores methods that use big data in the assessment of flood risks, and apply neural networks techniques to monitor the safety of nuclear power plants Describes a system which leverages big data analytics and the Internet of Things in the application of drones to aid victims in disaster scenarios Proposes a novel deep learning-based health data analytics application for sleep apnea detection, and a novel pathway for diagnostic models of headache disorders Reviews techniques for educational data mining and learning analytics, and introduces a scalable MapReduce graph partitioning approach for high degree vertices Presents a multivariate and dynamic data representation model for the visualization of healthcare data, and big data analytics methods for software reliability assessment This practically-focused volume is an invaluable resource for all researchers, academics, data scientists and business professionals involved in the planning, designing, and implementation of big data analytics projects. Dr. Mohammed M. Alani is an Associate Professor in Computer Engineering and currently is the Provost at Al Khawarizmi International College, Abu Dhabi, UAE. Dr. Hissam Tawfik is a Professor of Computer Science in the School of Computing, Creative Technologies & Engineering at Leeds Beckett University, UK. Dr. Mohammed Saeed is a Professor in Computing and currently is the Vice President for Academic Affairs and Research at the University of Modern Sciences, Dubai, UAE. Dr. Obinna Anya is a Research Staff Member at IBM Research – Almaden, San Jose, CA, USA.

IBM Software Defined Infrastructure for Big Data Analytics Workloads

IBM Software Defined Infrastructure for Big Data Analytics Workloads Book
Author : Dino Quintero,Daniel de Souza Casali,Marcelo Correia Lima,Istvan Gabor Szabo,Maciej Olejniczak,Tiago Rodrigues de Mello,Nilton Carlos dos Santos,IBM Redbooks
Publisher : IBM Redbooks
Release : 2015-06-29
ISBN : 0738440779
Language : En, Es, Fr & De

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Book Description :

This IBM® Redbooks® publication documents how IBM Platform Computing, with its IBM Platform Symphony® MapReduce framework, IBM Spectrum Scale (based Upon IBM GPFSTM), IBM Platform LSF®, the Advanced Service Controller for Platform Symphony are work together as an infrastructure to manage not just Hadoop-related offerings, but many popular industry offeringsm such as Apach Spark, Storm, MongoDB, Cassandra, and so on. It describes the different ways to run Hadoop in a big data environment, and demonstrates how IBM Platform Computing solutions, such as Platform Symphony and Platform LSF with its MapReduce Accelerator, can help performance and agility to run Hadoop on distributed workload managers offered by IBM. This information is for technical professionals (consultants, technical support staff, IT architects, and IT specialists) who are responsible for delivering cost-effective cloud services and big data solutions on IBM Power SystemsTM to help uncover insights among client's data so they can optimize product development and business results.

Modern Power Systems

Modern Power Systems Book
Author : Anonim
Publisher : Unknown
Release : 1986
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Modern Power Systems book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.