Robert C. Qiu, Paul Antonik

Smart Grid using Big Data Analytics (eBook, ePUB)

A Random Matrix Theory Approach

• Format: ePub

Produktbeschreibung

This book is aimed at students in communications and signal processing who want to extend their skills in the energy area. It describes power systems and why these backgrounds are so useful to smart grid, wireless communications being very different to traditional wireline communications.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 632
• Erscheinungstermin: 8. Februar 2017
• Englisch
• ISBN-13: 9781118716793
• Artikelnr.: 47879132

Autorenporträt

Robert Caiming Qiu, Professor, Dept. of ECE, Tennessee Technological University, Cookeville, TN, USA. Professor Qiu was Founder-CEO and President of Wiscom Technologies, Inc., manufacturing and marketing WCDMA chipsets. Wiscom was acquired by Intel in 2003. Prior to Wiscom, he worked for GTE Labs, Inc. (now Verizon), Waltham, MA, and Bell Labs, Lucent, Whippany, NJ. He holds 5 U.S. patents (another two pending) in WCDMA. Professor Qiu has contributed to 3GPP and IEEE standards bodies, and delivered invited seminars to institutions including Princeton University and the U.S. Army Research Lab. Dr. Qiu was made an IEEE Fellow in 2014. Dr. Paul Antonik, Chief Scientist, Information Directorate, Air Force Research Laboratory, Rome, N.Y., USA. Dr. Antonik serves as the directorate's principal scientific and technical adviser and primary authority for the technical content of the science and technology portfolio, providing principal technical oversight of a broad spectrum of information technologies.

Inhaltsangabe

Preface xv Acknowledgments xix Some Notation xxi 1 Introduction 1 1.1 Big
Data: Basic Concepts 1 1.2 Data Mining with Big Data 9 1.3 A Mathematical
Introduction to Big Data 13 1.4 A Mathematical Theory of Big Data 28 1.5
Smart Grid 34 1.6 Big Data and Smart Grid 36 1.7 Reading Guide 37
Bibliographical Remarks 39 Part I Fundamentals of Big Data 41 2 The
Mathematical Foundations of Big Data Systems 43 2.1 Big Data Analytics 44
2.2 Big Data: Sense, Collect, Store, and Analyze 45 2.3 Intelligent
Algorithms 48 2.4 Signal Processing for Smart Grid 48 2.5 Monitoring and
Optimization for Power Grids 48 2.6 Distributed Sensing and Measurement for
Power Grids 49 2.7 Real-time Analysis of Streaming Data 50 2.8 Salient
Features of Big Data 51 2.9 Big Data for Quantum Systems 54 2.10 Big Data
for Financial Systems 55 2.11 Big Data for Atmospheric Systems 73 2.12 Big
Data for Sensing Networks 74 2.13 Big Data forWireless Networks 75 2.14 Big
Data for Transportation 78 Bibliographical Remarks 78 3 Large Random
Matrices: An Introduction 79 3.1 Modeling of Large Dimensional Data as
Random Matrices 79 3.2 A Brief of Random MatrixTheory 81 3.3 Change Point
of Views: From Vectors to Measures 85 3.4 The Stieltjes Transform of
Measures 86 3.5 A Fundamental Result: The Marchenko-Pastur Equation 88 3.6
Linear Eigenvalue Statistics and Limit Laws 89 3.7 Central LimitTheorem for
Linear Eigenvalue Statistics 99 3.8 Central LimitTheorem for Random Matrix
S.1T 101 3.9 Independence for Random Matrices 103 3.10 Matrix-Valued
Gaussian Distribution 110 3.11 Matrix-ValuedWishart Distribution 112 3.12
Moment Method 112 3.13 Stieltjes Transform Method 113 3.14 Concentration of
the Spectral Measure for Large Random Matrices 114 3.15 Future Directions
117 Bibliographical Remarks 117 4 Linear Spectral Statistics of the Sample
Covariance Matrix 121 4.1 Linear Spectral Statistics 121 4.2 Generalized
Marchenko-Pastur Distributions 122 4.3 Estimation of Spectral Density
Functions 127 4.4 Limiting Spectral Distribution of Time Series 146
Bibliographical Remarks 154 5 Large Hermitian Random Matrices and Free
Random Variables 155 5.1 Large Economic/Financial Systems 156 5.2
Matrix-Valued Probability 157 5.3 Wishart-Levy Free Stable Random Matrices
166 5.4 Basic Concepts for Free Random Variables 168 5.5 The Analytical
Spectrum of theWishart-Levy Random Matrix 172 5.6 Basic Properties of the
Stieltjes Transform 176 5.7 Basic Theorems for the Stieltjes Transform 179
5.8 Free Probability for Hermitian Random Matrices 185 5.9 Random
Vandermonde Matrix 196 5.10 Non-Asymptotic Analysis of State Estimation 200
Bibliographical Remarks 201 6 Large Non-Hermitian Random Matrices and
Quatartenionic Free Probability Theory 203 6.1 Quatartenionic Free
ProbabilityTheory 204 6.2 R-diagonalMatrices 209 6.3 The Sum of
Non-Hermitian Random Matrices 216 6.4 The Product of Non-Hermitian Random
Matrices 220 6.5 Singular Value Equivalent Models 226 6.6 The Power of the
Non-Hermitian Random Matrix 234 6.7 Power Series of Large Non-Hermitian
Random Matrices 239 6.8 Products of Random Ginibre Matrices 246 6.9
Products of Rectangular Gaussian Random Matrices 249 6.10 Product of
ComplexWishart Matrices 252 6.11 Spectral Relations between Products and
Powers 254 6.12 Products of Finite-Size I.I.D. Gaussian Random Matrices 258
6.13 Lyapunov Exponents for Products of Complex Gaussian Random Matrices
260 6.14 Euclidean Random Matrices 264 6.15 Random Matrices with
Independent Entries and the Circular Law 273 6.16 The Circular Law and
Outliers 275 6.17 Random SVD, Single Ring Law, and Outliers 285 6.18 The
Elliptic Law and Outliers 295 Bibliographical Remarks 305 7 The
Mathematical Foundations of Data Collection 307 7.1 Architectures and
Applications for Big Data 307 7.2 Covariance Matrix Estimation 308 7.3
Spectral Estimators for Large Random Matrices 312 7.4 Asymptotic Framework
for Matrix Reconstruction 319 7.5 Optimum Shrinkage 329 7.6 A Shrinkage
Approach to Large-Scale Covariance Matrix Estimation 331 7.7 Eigenvectors
of Large Sample Covariance Matrix Ensembles 338 7.8 A General Class of
Random Matrices 351 Bibliographical Remarks 359 8 Matrix Hypothesis Testing
using Large RandomMatrices 361 8.1 Motivating Examples 362 8.2 Hypothesis
Test of Two Alternative Random Matrices 363 8.3 Eigenvalue Bounds for
Expectation and Variance 364 8.4 Concentration of Empirical Distribution
Functions 369 8.5 Random Quadratic Forms 381 8.6 Log-Determinant of Random
Matrices 382 8.7 General MANOVA Matrices 383 8.8 Finite Rank Perturbations
of Large Random Matrices 386 8.9 Hypothesis Tests for High-Dimensional
Datasets 391 8.9.1 Motivation for Likelihood Ratio Test (LRT) and
Covariance Matrix Tests 392 8.10 Roy's Largest Root Test 428 8.11 Optimal
Tests of Hypotheses for Large Random Matrices 431 8.12 Matrix Elliptically
Contoured Distributions 444 8.13 Hypothesis Testing for Matrix Elliptically
Contoured Distributions 446 Bibliographical Remarks 452 Part II Smart Grid
455 9 Applications and Requirements of Smart Grid 457 9.1 History 457 9.2
Concepts and Vision 458 9.3 Today's Electric Grid 459 9.4 Future Smart
Electrical Energy System 464 10 Technical Challenges for Smart Grid 471
Bibliographical Remarks 483 11 Big Data for Smart Grid 485 11.1 Power in
Numbers: Big Data and Grid Infrastructure 485 11.2 Energy's Internet:The
Convergence of Big Data and the Cloud 486 11.3 Edge Analytics: Consumers,
Electric Vehicles, and Distributed Generation 486 11.4 CrosscuttingThemes:
Big Data 486 11.5 Cloud Computing for Smart Grid 488 11.6 Data Storage,
Data Access and Data Analysis 488 11.7 The State-of-the-Art Processing
Techniques of Big Data 488 11.8 Big Data Meets the Smart Electrical Grid
488 11.9 4Vs of Big Data: Volume, Variety, Value and Velocity 489 11.10
Cloud Computing for Big Data 490 11.11 Big Data for Smart Grid 490 11.12
Information Platforms for Smart Grid 491 Bibliographical Remarks 491 12
Grid Monitoring and State Estimation 493 12.1 Phase Measurement Unit 493
12.2 Optimal PMU Placement 495 12.3 State Estimation 495 12.4 Basics of
State Estimation 495 12.5 Evolution of State Estimation 496 12.6 Static
State Estimation 497 12.7 Forecasting-Aided State Estimation 500 12.8
Phasor Measurement Units 501 12.9 Distributed System State Estimation 502
12.10 Event-Triggered Approaches to State Estimation 502 12.11 Bad Data
Detection 502 12.12 Improved Bad Data Detection 504 12.13 Cyber-Attacks 504
12.14 Line Outage Detection 504 Bibliographical Remarks 504 13 False Data
Injection Attacks against State Estimation 505 13.1 State Estimation 505
13.2 False Data Injection Attacks 507 13.3 MMSE State Estimation and
Generalized Likelihood Ratio Test 508 13.4 Sparse Recovery from Nonlinear
Measurements 512 13.5 Real-Time Intrusion Detection 515 Bibliographical
Remarks 515 14 Demand Response 517 14.1 Why Engage Demand? 517 14.2 Optimal
Real-time Pricing Algorithms 520 14.3 Transportation Electrification and
Vehicle-to-Grid Applications 522 14.4 Grid Storage 522 Bibliographical
Remarks 523 Part III Communications and Sensing 525 15 Big Data for
Communications 527 15.1 5G and Big Data 527 15.2 5GWireless Communication
Networks 527 15.3 Massive Multiple Input, Multiple Output 528 15.4 Free
Probability for the Capacity of the Massive MIMO Channel 537 15.5 Spectral
Sensing for Cognitive Radio 539 Bibliographical Remarks 539 16 Big Data for
Sensing 541 16.1 Distributed Detection and Estimation 541 16.2 Euclidean
Random Matrix 547 16.3 Decentralized Computing 548 Appendix A: Some Basic
Results on Free Probability 551 Appendix B: Matrix-Valued Random Variables
557 References 567 Index 601