Advances in Electric Power and Energy E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: IEEE Press Series on Power Engineering
• Sprache: Englisch

Seitenzahl: 816

Veröffentlichungsjahr: 2020

Beschreibung

A guide to the role of static state estimation in the mitigation of potential system failures With contributions from a noted panel of experts on the topic, Advances in Electric Power and Energy: Static State Estimation addresses the wide-range of issues concerning static state estimation as a main energy control function and major tool for evaluating prevailing operating conditions in electric power systems worldwide. This book is an essential guide for system operators who must be fully aware of potential threats to the integrity of their own and neighboring systems. The contributors provide an overview of the topic and review common threats such as cascading black-outs to model-based anomaly detection to the operation of micro-grids and much more. The book also includes a discussion of an effective mathematical programming approach to state estimation in power systems. Advances in Electric Power and Energy reviews the most recent developments in the field and: * Offers an introduction to the topic to help non-experts (and professionals) get up-to-date on static state estimation * Covers the essential information needed to understand power system state estimation written by experts on the subject * Discusses a mathematical programming approach Written for electric power system planners, operators, consultants, power system software developers, and academics, Advances in Electric Power and Energy is the authoritative guide to the topic with contributions from experts who review the most recent developments.

Leseprobe

Table of Contents

COVER

TITLE PAGE

COPYRIGHT PAGE

DEDICATION PAGE

ABOUT THE EDITOR

ABOUT THE CONTRIBUTORS

CHAPTER 1: GENERAL CONSIDERATIONS

1.1 PRELUDE

1.2 DEFINING SSE

1.3 THE NEED FOR STATE ESTIMATION

1.4 STATIC STATE ESTIMATION IN PRACTICE

1.5 APPLICATIONS THAT USE SE SOLUTION

1.6 OVERVIEW OF CHAPTERS

REFERENCES

CHAPTER 2: STATE ESTIMATION IN POWER SYSTEMS BASED ON A MATHEMATICAL PROGRAMMING APPROACH

2.1 INTRODUCTION

2.2 FORMULATION

2.3 CLASSICAL STATE ESTIMATION PROCEDURE

2.4 MATHEMATICAL PROGRAMMING SOLUTION

2.5 ALTERNATIVE STATE ESTIMATORS

REFERENCES

PART I: SYSTEM FAILURE MITIGATION

CHAPTER 3: SYSTEM STRESS AND CASCADING BLACKOUTS

3.1 INTRODUCTION

3.2 CASCADING BLACKOUTS AND PREVIOUS WORK

3.3 PROBLEM STATEMENT AND APPROACH

3.4 DFAXes, VULNERABILITY, AND CRITICALITY METRICS

3.5 VALIDITY OF METRICS

3.6 STUDIES WITH METRICS

3.7 SUMMARY

3.8 APPLICATION OF STRESS METRICS

3.9 CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

CHAPTER 4: MODEL‐BASED ANOMALY DETECTION FOR POWER SYSTEM STATE ESTIMATION

4.1 INTRODUCTION

4.2 CYBERATTACKS ON STATE ESTIMATION

4.3 ATTACK‐RESILIENT STATE ESTIMATION

4.4 MODEL‐BASED ANOMALY DETECTION

4.5 CONCLUSIONS

REFERENCES

CHAPTER 5: PROTECTION, CONTROL, AND OPERATION OF MICROGRIDS

5.1 PRELUDE

5.2 INTRODUCTION

5.3 STATE OF THE ART IN MICROGRID PROTECTION AND CONTROL

5.4 EMERGING TECHNOLOGIES

5.5 TEST CASE FOR DDSE

5.6 TEST RESULTS

5.7 TEST CASE FOR ADAPTIVE SETTING‐LESS PROTECTION

5.8 CONCLUSIONS

REFERENCES

PART II: ROBUST STATE ESTIMATION

CHAPTER 6: PSSE REDUX: CONVEX RELAXATION, DECENTRALIZED, ROBUST, AND DYNAMIC SOLVERS

6.1 INTRODUCTION

6.2 POWER GRID MODELING

6.3 PROBLEM STATEMENT

6.4 DISTRIBUTED SOLVERS

6.5 ROBUST ESTIMATORS AND CYBERATTACKS

6.6 POWER SYSTEM STATE TRACKING

6.7 DISCUSSION

ACKNOWLEDGMENTS

6.A APPENDIX

REFERENCES

CHAPTER 7: ROBUST WIDE‐AREA FAULT VISIBILITY AND STRUCTURAL OBSERVABILITY IN POWER SYSTEMS WITH SYNCHRONIZED MEASUREMENT UNITS

7.1 INTRODUCTION

7.2 ROBUST FAULT VISIBILITY USING STRATEGICALLY DEPLOYED SYNCHRONIZED MEASUREMENTS

7.3 OPTIMAL PMU DEPLOYMENT FOR SYSTEM‐WIDE STRUCTURAL OBSERVABILITY

7.4 CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

CHAPTER 8: A ROBUST HYBRID POWER SYSTEM STATE ESTIMATOR WITH UNKNOWN MEASUREMENT NOISE

8.1 INTRODUCTION

8.2 PROBLEM STATEMENT

8.3 PROPOSED FRAMEWORK FOR ROBUST HYBRID STATE ESTIMATION

8.4 NUMERICAL RESULTS

8.5 CONCLUSIONS

REFERENCES

CHAPTER 9: LEAST‐TRIMMED‐ABSOLUTE‐VALUE STATE ESTIMATOR

9.1 BAD DATA DETECTION AND ROBUST ESTIMATORS

9.2 RESULTS AND DISCUSSION

9.3 CONCLUSIONS

9.A.1 6‐Bus DC System

9.A.2 6‐Bus AC System

9.B 14‐Bus AC System

9.C 30‐Bus AC System

REFERENCES

PART III: STATE ESTIMATION FOR DISTRIBUTION SYSTEMS

CHAPTER 10: PROBABILISTIC STATE ESTIMATION IN DISTRIBUTION NETWORKS

10.1 INTRODUCTION

10.2 STATE ESTIMATION IN DISTRIBUTION NETWORKS

10.3 IMPROVING OBSERVABILITY IN DISTRIBUTION NETWORKS

10.4 CONCLUSION

REFERENCES

CHAPTER 11: ADVANCED DISTRIBUTION SYSTEM STATE ESTIMATION IN MULTI‐AREA ARCHITECTURES

11.1 ISSUES AND CHALLENGES OF DISTRIBUTION SYSTEM STATE ESTIMATION

11.2 DISTRIBUTION SYSTEM MULTI‐AREA STATE ESTIMATION (DS‐MASE) APPROACH

11.3 APPLICATION OF THE DS‐MASE APPROACH

11.4 VALIDITY AND APPLICABILITY OF DS‐MASE APPROACH

REFERENCES

PART IV: PARALLEL/DISTRIBUTED PROCESSING

CHAPTER 12: HIERARCHICAL MULTI‐AREA STATE ESTIMATION

12.1 INTRODUCTION

12.2 PRELIMINARIES

12.3 MODELING AND PROBLEM FORMULATION

12.4 A BRIEF SURVEY OF SOLUTION TECHNIQUES

12.5 HIERARCHICAL STATE ESTIMATOR VIA SENSITIVITY FUNCTION EXCHANGES

12.6 ADD‐ON FUNCTIONS IN MULTI‐AREA STATE ESTIMATION

12.7 PROPERTIES

12.8 SIMULATIONS

12.9 CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

CHAPTER 13: PARALLEL DOMAIN‐DECOMPOSITION‐BASED DISTRIBUTED STATE ESTIMATION FOR LARGE‐SCALE POWER SYSTEMS

13.1 INTRODUCTION

13.2 FUNDAMENTAL THEORY AND FORMULATION

13.3 EXPERIMENTAL RESULTS

13.4 CONCLUSION

REFERENCES

CHAPTER 14: DISHONEST GAUSS–NEWTON METHOD‐BASED POWER SYSTEM STATE ESTIMATION ON A GPU

14.1 INTRODUCTION

14.2 BACKGROUND

14.3 PERFORMANCE OF DISHONEST GAUSS–NEWTON METHOD

14.4 GPU IMPLEMENTATION

14.5 SIMULATION RESULTS

14.6 DISCUSSIONS ON SCALABILITY

14.7 DISTRIBUTED METHOD OF PARALLELIZATION

14.8 CONCLUSIONS

REFERENCES

INDEX

IEEE PRESS SERIES ON POWER ENGINEERING

END USER LICENSE AGREEMENT

List of Tables

Chapter 2

TABLE 2.1 Line parameters.

TABLE 2.2 Voltages: measured, true and estimated values.

TABLE 2.3 Power injections: measured, true and estimated values.

TABLE 2.4 Power flows: measured, true and estimated values.

TABLE 2.5 State‐variable updates and convergency summary.

TABLE 2.6 Residuals and normalized residuals.

TABLE 2.7 Characterization of different state‐estimation formulations.

TABLE 2.8 Example of alternative estimators: line characteristics.

TABLE 2.9 Example of alternative estimators: operating point.

TABLE 2.10 Example of alternative estimators: measurements.

TABLE 2.11 Example of alternative estimators: characterization.

TABLE 2.12 Example of alternative estimators: optimal values for the binary varia...

TABLE 2.13 Example of alternative estimators: true and estimated state vectors.

TABLE 2.14 Case study: estimation accuracy results.

TABLE 2.15 Case study: computational performance results.

TABLE 2.16 Case study: estimation accuracy results with bad measurements.

TABLE 2.17 Case study: computational performance results with bad measurements.

Chapter 3

TABLE 3.1 Ybus structure for the nine‐branch subsystem.

TABLE 3.2 DFAX matrix and pre‐contingency flows for nine‐branch subsystem....

TABLE 3.3 N − 1 flows, rank and degree of vulnerability and criticality, for nine...

TABLE 3.4 Number of large DFAX compared with number of monitored branches.

TABLE 3.5 Load growth and system expansion, congestion, and stress [19].

Chapter 4

TABLE 4.1 Synthesis of related work.

TABLE 4.2 Experimental parameters for case study.

TABLE 4.3 Power flow solution for the example.

TABLE 4.4 SE outputs and predicted states for the example.

TABLE 4.5 Comparison of differences with detection thresholds.

Chapter 5

TABLE 5.1 Example branch data for the topology in Figure 5.1 (A > B means B is th...

TABLE 5.2 Example coordination data for the topology in Figure 5.1.

TABLE 5.3 Parameters for line impedances per unit length.

TABLE 5.4 Line length of distribution lines.

TABLE 5.5 Power injection by loads.

TABLE 5.6 Standard deviation of measurements.

TABLE 5.7 Parameters for the wind turbine generator.

TABLE 5.8 Test scenarios.

Chapter 6

TABLE 6.1 Mean‐square estimation error in the presence of bad data.

Chapter 7

TABLE 7.1 Synchronized sensor locations versus wave arrival times for the fault o...

TABLE 7.2 Values of ℓ, , ψ(ℓ) (in ms), (in ms), and Dℓ (in ms) for the fault occ...

TABLE 7.3 Synchronized sensor locations versus wave arrival times (after correcti...

TABLE 7.4 Optimal PMU deployment results for three IEEE test systems.

Chapter 9

TABLE 9.1 Simulated measurements for 6‐bus DC system.

TABLE 9.2 Estimation results of case 0, 6‐bus DC system.

TABLE 9.3 Estimation errors of case 0, 6‐bus DC system.

TABLE 9.4 Estimation results of case 1, 6‐bus DC system.

TABLE 9.5 Estimation errors of case 1, 6‐bus DC system.

TABLE 9.6 Estimation results of case 2, 6‐bus DC system.

TABLE 9.7 Estimation errors of case 2, 6‐bus DC system.

TABLE 9.8 Estimation results of case 3, 6‐bus DC system.

TABLE 9.9 Estimation errors of case 3, 6‐bus DC system.

TABLE 9.10 Comparison of 6‐bus indicators.

TABLE 9.11 Simulated measurements for 6‐bus AC system.

TABLE 9.12 Estimation errors of case 0, 6‐bus AC system.

TABLE 9.13 Estimation errors of case 1, 6‐bus AC system.

TABLE 9.14 Estimation errors of case 2, 6‐bus AC system.

TABLE 9.15 Estimation errors of case 3, 6‐bus AC system.

TABLE 9.16 Comparison of indicators for 6‐bus AC system.

TABLE 9.17 Actual values for 14‐bus AC system.

TABLE 9.18 Measured meters for 14‐bus AC system, full redundancy.

TABLE 9.19 Selected measured meters for 14‐bus AC system, median redundancy....

TABLE 9.20 Full redundancy of 14‐bus AC system.

TABLE 9.21 Median redundancy of 14‐bus AC system.

TABLE 9.22 Actual and measured values for 30‐bus AC system.

TABLE 9.23 Median redundancy of 30‐bus AC system.

TABLE 9.A.1 Line data of 6‐bus DC system.

TABLE 9.A.2 Bus data of 6‐bus DC system.

TABLE 9.A.3 Line data of 6‐bus AC system.

TABLE 9.A.4 Bus data of 6‐bus AC system.

TABLE 9.B.1 Line data of 14‐bus AC system.

TABLE 9.B.2 Bus data of 14‐bus AC system.

TABLE 9.C.1 Line data of 30‐bus AC system.

TABLE 9.C.2 Bus data of 30‐bus AC system.

Chapter 10

TABLE 10.1 Example simulation results with confidence values.

TABLE 10.2 Example simulation results (voltages and standard deviations).

TABLE 10.3 Example simulation results (accuracy and minimum accuracy values).

TABLE 10.4 Compliance ratios.

TABLE 10.5 Compliance ratios (case study 2).

Chapter 11

TABLE 11.1 Correlation factors between voltage estimates with a shared voltage me...

TABLE 11.2 Measurement location for Scenario 1.

TABLE 11.3 Measurement location for Scenario 2.

TABLE 11.4 Measurement location for Scenario 3.

Chapter 12

TABLE 12.1 Attributes of selected works on multi‐area state estimation.

TABLE 12.2 Initialization for boundary state variables.

TABLE 12.3 Performance of SFHSE and the comparison with the centralized state est...

TABLE 12.4 Comparison between SFHSE, the CSE, and another MASE method for IEEE 11...

TABLE 12.5 Distributed bad data identification in the IEEE 118‐bus system test....

TABLE 12.6 Comparison between SFHSE and the CSE for real system test.

Chapter 13

TABLE 13.1 Results for comparison of parallel ASM WLS with centralized WLS.

TABLE 13.2 Data sets for simulation.

TABLE 13.3 Estimation error for different percentage of PMU installation.

TABLE 13.4 Execution time for CPU‐based and GPU‐based DSE.

Chapter 14

TABLE 14.1 Reported required time [11].

List of Illustrations

Chapter 2

Figure 2.1 One‐line diagram and measurement configuration of a 4‐bus power s...

Figure 2.2 Objective function for the WLS estimator as a function of the err...

Figure 2.3 Objective function for the LAV estimator as a function of the err...

Figure 2.4 Objective function for the QC estimator as a function of the erro...

Figure 2.5 Objective function for the QL estimator as a function of the erro...

Figure 2.6 Graphical representation of the LMS estimator.

Figure 2.7 Problem size comparison for different estimators.

Figure 2.8 Example of alternative estimators: four‐bus system.

Figure 2.9 Example of alternative estimators: residuals of the WLS solution....

Figure 2.10 Example of alternative estimators: residuals of the LAV solution...

Figure 2.11 Example of alternative estimators: residuals of the QC solution....

Figure 2.12 Example of alternative estimators: residuals of the QL solution....

Figure 2.13 Histogram of voltage magnitude estimation accuracy for each meth...

Figure 2.14 Histogram of voltage angle estimation accuracy for each method....

Figure 2.15 Histogram of the computation time for each estimator.

Chapter 3

Figure 3.1 A generic network [24].

Figure 3.2 Nine‐branch subsystem of 118‐bus IEEE test system.

Figure 3.3 DFAX distribution functions for large and small systems [19].

Figure 3.4 DFAX

density

functions resemble power law functions.

Figure 3.5 Degree of vulnerability is affected by interregional transfers.

Figure 3.6 Rank of vulnerability for peak, shoulder, and minimum loads.

Figure 3.7 Degree of criticality for peak, shoulder, and minimum loads.

Figure 3.8 RankV and tipping points as a function of demand, SW WI.

Chapter 4

Figure 4.1 Intuitive understanding of stealthy false data injection attacks....

Figure 4.2 Intuitive understanding of stealthy topology attacks.

Figure 4.3 Overview of model‐based anomaly detection approach.

Figure 4.4 Detailed workflow of model‐based anomaly detection.

Figure 4.5 IEEE 14‐bus system with a measurement configuration.

Figure 4.6 Plotting deviation between predicted states and SE outputs over a...

Figure 4.7 Variation of FPR with respect to detection thresholds for

θ

2

Figure 4.8 Variation of TPR with respect to attack magnitude for various det...

Chapter 5

Figure 5.1 Example microgrid circuit, legacy distance protection.

Figure 5.2 Distance relay characteristics.

Figure 5.3 Performance of the distance protection, relay I, with a phase A t...

Figure 5.4 Example microgrid circuit, legacy line differential protection.

Figure 5.5 Line differential relay characteristics (alpha plane method).

Figure 5.6 Performance of the line differential protection, relay I, with a ...

Figure 5.7 Performance of the line differential protection, relay I, with a ...

Figure 5.8 Example topology of a microgrid, adaptive protection scheme.

Figure 5.9 An example microgrid system, differential energy‐based protection...

Figure 5.10 Phase A current measurement at bus B‐1 of the line DL‐1, grid‐co...

Figure 5.11 Phase A current measurement at bus B‐2 of the line DL‐1, grid‐co...

Figure 5.12 Phase A current spectral energy at bus B‐1 of the line DL‐1, gri...

Figure 5.13 Phase A current spectral energy at bus B‐2 of the line DL‐1, gri...

Figure 5.14 Phase A differential energy of the line DL‐1, grid‐connected mod...

Figure 5.15 Phase A differential energy of the line DL‐1, islanded mode.

Figure 5.16 Control architecture of an example microgrid.

Figure 5.17 Two voltage sources and the inductive circuit connecting them.

Figure 5.18 Active power/frequency droop and reactive power/voltage droop.

Figure 5.19 Active power/frequency droop diagram, method 1.

Figure 5.20 Reactive power/voltage droop diagram, method 1.

Figure 5.21 Droop control diagram, method 2 [15].

Figure 5.22 Secondary control, active power/frequency droop.

Figure 5.23 Secondary control, reactive power/voltage droop.

Figure 5.24 Two voltage sources and the resistive circuit connecting them.

Figure 5.25 Two voltage sources (the inverter and the grid) and the resistiv...

Figure 5.26 π‐Equivalent microgrid circuit model.

Figure 5.27 Example microgrid system, dynamic state estimation‐based protect...

Figure 5.28 Dynamic state estimation‐based protection results: low impedance...

Figure 5.29 Dynamic state estimation‐based protection results: high impedanc...

Figure 5.30 Adaptive setting‐less protection scheme.

Figure 5.31 UMPCU installation and its functions.

Figure 5.32 Test case for a distribution feeder. The acronyms WG and L repre...

Figure 5.33 Expected errors of states (test case 1).

Figure 5.34 Actual absolute errors of states (test case 1).

Figure 5.35 Computation time (test case 1).

Figure 5.36 Expected errors of states (test case 2).

Figure 5.37 Actual absolute errors of states (test case 2).

Figure 5.38 Computation time (test case 2).

Figure 5.39 Equivalent circuit for a three‐phase induction machine.

Figure 5.40 Confidence levels for test cases 3–7.

Figure 5.41 Terminal currents for test case 6 (high impedance fault).

Figure 5.42 Top: terminal currents for test case 4 (three‐phase‐to‐ground fa...

Chapter 6

Figure 6.1 Left: Voltage magnitude and angle estimation errors per bus for t...

Figure 6.2 Left: The IEEE 14‐bus system partitioned into four areas [25, 31]...

Figure 6.3 Average error curves

(bottom) and

(top) for the LSE and its r...

Figure 6.4 (Left) Per area state matrix error and (right) state vector estim...

Chapter 7

Figure 7.1 Shortest propagation paths from Sensor “

k

” to the fault on Line “...

Figure 7.2 Virtual nodes generated at the points “

β

k

,ℓ

D

ℓ

” ...

Figure 7.3 (a) Location of a fault occurring at 99 miles away from Bus 63 th...

Figure 7.4 Phasor measurements provided by a PMU.

Figure 7.5 7‐Bus system for illustration.

Figure 7.6 Placement of 3 two‐channel PMUs in the 7‐bus system.

Figure 7.7 Optimal PMU deployment on a 4‐bus system (a) ignoring zero‐inject...

Figure 7.8 Configuration of 3 single‐channel PMUs in the 7‐bus system (the d...

Figure 7.9 Placement of 5 two‐channel PMUs in the 7‐bus system.

Chapter 8

Figure 8.1 Schematic of the proposed robust hybrid state estimation framewor...

Figure 8.2 The absolute estimation error of the bus voltage magnitude with G...

Figure 8.3 The absolute estimation error of the bus voltage angles with Gaus...

Figure 8.4 The absolute estimation error of the bus voltage magnitude with G...

Figure 8.5 The absolute estimation error of the bus voltage angles with non‐...

Figure 8.6 The absolute estimation error of the bus voltage magnitude with L...

Figure 8.7 The absolute estimation error of the bus voltage angles with Lapl...

Figure 8.8 The estimated states of the proposed method under different bad d...

Chapter 9

Figure 9.1

χ

2

probability density function.

Figure 9.2 Least square objective function.

Figure 9.3 Least‐absolute‐value objective function.

Figure 9.4 Least trimmed square objective function.

Figure 9.5 Least‐trimmed‐absolute‐value objective function.

Chapter 10

Figure 10.1 The 68, 95, and 99.7% confidence interval of a Gaussian‐distribu...

Figure 10.2 The 13‐bus feeder.

Figure 10.3 The voltage profile of the 13‐bus feeder (case 1).

Figure 10.4 PDF of the voltage estimate at bus 6 of the 13‐bus feeder (case ...

Figure 10.5 The voltage profile of the 13‐bus feeder including confidence va...

Figure 10.6 Voltage profile of the 13‐bus feeder confidence values (case 2)....

Figure 10.7 Voltage profile of the 13‐bus feeder confidence values (case 3)....

Figure 10.8 The 145‐bus test feeder.

Figure 10.9 The voltage profile of the 145‐bus feeder.

Figure 10.10 The voltage of the 145‐bus feeder with a heat map overlay.

Figure 10.11 Flowchart of the probabilistic observability assessment.

Figure 10.12 Modified IEEE test feeder.

Figure 10.13 Voltage profile for worst‐case scenario one (maximum load and n...

Figure 10.14 Voltage profile for worst‐case scenario two (minimum load and m...

Figure 10.15 The convergence behavior of the compliance ratio at bus 33.

Figure 10.16 Modified IEEE test network with the voltage control devices.

Figure 10.17 Voltage profile for worst‐case one (maximum load and no distrib...

Figure 10.18 Voltage profile for worst‐case two (minimum load and maximum di...

Chapter 11

Figure 11.1 Multi‐area partition strategies.

Figure 11.2 Multi‐area state estimation: in‐series and in‐parallel execution...

Figure 11.3 MASE computing architecture.

Figure 11.4 Example of equivalent power injection creation at a shared bus....

Figure 11.5 Flowchart of the MASE first step.

Figure 11.6 Sub‐areas without measurement points at the shared node.

Figure 11.7 Sub‐areas with measurement point installed at the shared node.

Figure 11.8 Flowchart of the MASE second step.

Figure 11.9 95‐bus network.

Figure 11.10 Current magnitude estimation in Scenario 1.

Figure 11.11 Voltage magnitude estimation in Scenario 1.

Figure 11.12 Current magnitude estimation in Scenario 2.

Figure 11.13 Voltage magnitude estimation in Scenario 2.

Figure 11.14 Voltage magnitude estimation in Scenario 3.

Figure 11.15 Voltage magnitude estimation in Scenario 4.

Figure 11.16 Voltage phase angle estimation in Scenario 4.

Chapter 12

Figure 12.1 The interconnection of the power system in North America [1].

Figure 12.2 Architectures of multi‐area state estimators: hierarchical versu...

Figure 12.3 Illustration of topological methods for observability analysis. ...

Figure 12.4 A three‐area power system.

Figure 12.5 Architecture of the method in [10].

Figure 12.6 Illustration of the phase angle rotation method.

Figure 12.7 Complete scheme of SFHSE.

Figure 12.8 IEEE 118‐bus three area system [46].

Figure 12.9 Four‐area real power system.

Chapter 13

Figure 13.1 State estimation process block diagram.

Figure 13.2 State estimation flowchart.

Figure 13.3 Standard transmission line π model.

Figure 13.4 Steps of parallel algorithm generation.

Figure 13.5 CPU, GPU, CUDATM, and OpenMP resources.

Figure 13.6 Gauss–Jacobi iterative method for two subsystems.

Figure 13.7 Flowchart of ASM method with time stem

τ

.

i

, current subsys...

Figure 13.8 The ASM‐based Jacobi WLS algorithm with BDD.

k

, time step;

i

, th...

Figure 13.9 Domain decomposition: (a) interconnection of two subsystems and ...

Figure 13.10 Original power system decomposed into

J

subsystems for RJDSE im...

Figure 13.11 IEEE 39‐bus power system used to build large‐scale test cases....

Figure 13.12 Fermi GPU architecture.

Figure 13.13 Voltage magnitudes for Case 1 with respect to system size.

Figure 13.14 Phase angles for Case 1 with respect to system size.

Figure 13.15 Decomposing a Case 1 into four subsystems to apply the ASM algo...

Figure 13.16 Percentage of execution time breakdown with respect to system s...

Figure 13.17 Hierarchy of parallelism.

τ

, integration time step;

t

, sim...

Figure 13.18 Estimation errors in GPU‐based ASM for Case 1 compared with PSS...

Figure 13.19 Snapshot of estimation error for Case 1 at bus numbers 10, 11, ...

Figure 13.20 Percentage of time used for various steps in GPU‐based ASM.

Figure 13.21 Execution time (

T

Ex

) and speedup (

S

p

) comparisons of multithrea...

Chapter 14

Figure 14.1 The working principle of Gauss–Newton method: (a) honest and (b)...

Figure 14.2 Two major ways of convergence of the dishonest method on a linea...

Figure 14.3 Simplified structure of a GPU.

Figure 14.4 Accuracy of the dishonest Gauss–Newton method compared with the ...

Figure 14.5 Accuracy of the dishonest Gauss–Newton method compared with the ...

Figure 14.6 The accuracy of the estimator under different level of noise [11...

Figure 14.7 The norm of the residue of the estimated values under different ...

Figure 14.8 Parallel multiplication of a matrix and a vector [11].

Figure 14.9 Parallel addition of 16 numbers [12].

Figure 14.10 Required time for different number of iterations. Though it gro...

Figure 14.11 The process of exchange and update of the CCN.

Figure 14.12 The cellular dishonest method.

Figure 14.13 The actual and the estimated value of the cellular dishonest me...

Guide

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IEEE Press445 Hoes LanePiscataway, NJ 08854

IEEE Press Editorial BoardEkram Hossain, Editor in Chief

Jón Atli Benediktsson

David Alan Grier

Elya B. Joffe

Xiaoou Li

Peter Lian

Andreas Molisch

Saeid Nahavandi

Jeffrey Reed

Diomidis Spinellis

Sarah Spurgeon

Ahmet Murat Tekalp

ADVANCES IN ELECTRIC POWER AND ENERGY

Static State Estimation

Edited by

Mohamed E. El‐Hawary

Dalhousie University

© 2021 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

Published simultaneously in Canada.

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Library of Congress Cataloging‐in‐Publication Data:

Names: El-Hawary, M. E., editor. | John Wiley & Sons, Inc., publisher.Title: Advances in electric power and energy : static state estimation / edited by Mohamed E El-Hawary, Dalhousie University.Description: Hoboken, New Jersey : John Wiley & Sons, Inc., [2021] | Series: IEEE Press | Includes bibliographical references and index.Identifiers: LCCN 2020028681 (print) | LCCN 2020028682 (ebook) | ISBN 9781119480464 (hardback) | ISBN 9781119584506 (hardback) | ISBN 9781119480365 (adobe pdf) | ISBN 9781119480440 (epub)Subjects: LCSH: Electric power systems–State estimation.Classification: LCC TK1005 .A35 2020 (print) | LCC TK1005 (ebook) | DDC  621.319/1–dc23LC record available at https://lccn.loc.gov/2020028681LC ebook record available at https://lccn.loc.gov/2020028682

Cover Design: WileyCover Images: (top) © Sam Robinson/Getty Images, (middle) © Steve Ramplin/EyeEm/Getty Images

TO FRED C. SCHWEPPE, 1933–1988

He brought state estimation to electric power, and later led the development of the theoretical bases of competitive electric power markets. A teacher and innovator of great understanding and creativity, “solving the problem” was secondary to bringing out the best in those he worked with.

ABOUT THE EDITOR

Mohamed “Mo” El‐Aref El‐Hawary, age 76, of Halifax passed away on Friday, 26 July 2019. Born in Sohag, Egypt, he was predeceased by his parents Mahmood and Amina El‐Hawary of Alexandria, Egypt. He is survived by his wife, Ferial (El‐Bibany) El‐Hawary, Halifax; daughter, Elizabeth “Bette” El‐Hawary, Halifax; sons, Robert “Bob” El‐Hawary, London, UK, and Rany “Ron” (Tricia Lane) El‐Hawary, Halifax; sisters, Mervat El‐Hawary, Mona El‐Hawary, and Mawaheb (Heba) El‐Hawary (all located in Alexandria, Egypt); and grandchildren, Alexa, Ben, Grace, Ryan, Eoin, Kegan, Duncan, and Liam.

Dr. El‐Hawary was a Professor of Electrical and Computer Engineering at Dalhousie University in Halifax, Nova Scotia, Canada. He had a BSc in Electrical Engineering, Distinction and First‐Class Honors, University of Alexandria, Egypt, 1965, and a PhD in Electrical Engineering, University of Alberta, Edmonton, 1972, where he was an Izaak Walton Killam Memorial Fellow from 1970 to 1972. He was Associate Professor of Electrical Engineering at the Federal University of Rio de Janeiro for two years and subsequently served for eight years on faculty at Memorial University of Newfoundland since 1974. He was appointed Chairman of Electrical Engineering Program in 1976. In 1981, he joined the Technical University of Nova Scotia (TUNS) as Professor of Electrical Engineering. In 1997, TUNS was amalgamated with Dalhousie University. Dr. El‐Hawary has been Associate Dean of Engineering at Dalhousie between 1995 and 2007, Director of International and External Relations for the Faculty of Engineering in 2008–2009, and Chair of the Senate of Dalhousie University in 2001–2007. He cherished having had the opportunity to be part of educating, mentoring, and touching the lives and careers of countless students in the field of Electrical Engineering over his long and distinguished career.

Throughout his career, Mo authored over 10 textbooks and almost 200 full journal papers. He was the Institute of Electrical and Electronics Engineering (IEEE) Press Power Engineering Series Editor and Founding Editor in Chief of the IEEE Systems, Man and Cybernetics Magazine, and Power Letters of PES. He was Associate Editor for the three major Electric Machines and Power Systems' Journals and Editor of Electrical Power Engineering, McGraw‐Hill Encyclopedia of Science and Technology. He was a Fellow of IEEE, Canadian Academy of Engineering, Engineers Canada, and the Engineering Institute of Canada. He was a Distinguished Lecturer of the IEEE Power and Energy Society.

He served as a member of the Board of Directors and Secretary of IEEE and as President of IEEE Canada. He served on the IEEE Publication Services and Products Board, Fellows Committee, IEEE Press Board Chairman, Power Engineering Society (PES): System Operations Committee Chair and, member of HKN Board, and Vice President, Development, IEEE Canada Foundation. He has been recipient of IEEE Canada, W. S. Read Service Award, 2010. In 1999 IEEE awarded him the EAB Meritorious Achievement, Power Engineering Educator of the Year, and IEEE Canada General A.G.L. McNaughton Gold Medal.

ABOUT THE CONTRIBUTORS

Venkataramana Ajjarapu currently holds the David Nicholas Professor of Electrical and Computer Engineering at Iowa State University. His area of expertise includes power system stability, reactive power control, and optimization. He is a Fellow of the IE.

Aditya Ashok is a senior research engineer in the Electricity Infrastructure and Buildings division at the Pacific Northwest National Laboratory (PNNL) and has been with PNNL since February 2016. Aditya received his doctoral degree in Electrical Engineering from Iowa State University in May 2017. Aditya’s research interests include analyzing cyber vulnerabilities in energy delivery systems, assessing potential impacts to system operations, reliability, and economics, and developing novel algorithms to mitigate cyber vulnerabilities and help enhance the overall security and resilience of energy delivery systems.

Bernd Brinkmann received his Bachelor's degree in Electrical Engineering from the University of Applied Sciences Bielefeld, Germany, in 2011. After gaining experience as a design engineer and software developer, he is currently pursuing the PhD degree in Electrical Power Engineering at the University of Tasmania, Australia. His research interests include state estimation uncertainty, distribution network observability, and optimal meter placement.

Eduardo Caro received the Electrical Engineering degree from the Technical University of Catalonia, Barcelona, Spain, 2007, and the PhD degree in the University of Castilla‐La Mancha, Spain, 2011. He is currently an Assistant Professor at the Universidad Politécnica de Madrid, Madrid, Spain. His research interests include power system estimation, optimization, and electricity load forecasting.

Sungyun Choi received the BE degree in Electrical Engineering from Korea University, Seoul, South Korea, in 2002 and the MS and PhD degrees in Electrical and Computer Engineering from Georgia Institute of Technology, Atlanta, GA, USA, in 2009 and 2013, respectively. From 2002 to 2005, he was a Network and System Engineer, and from 2007 to 2013, he was a Research Assistant with the Power System Control and Automation Laboratory, Atlanta, GA, USA. Since 2014, he has been a Senior Researcher with Smart Power Grid Research Center, Korea Electrotechnology Research Institute, Uiwang, South Korea. His research interests include smart grid technology, autonomous operation of microgrids, power system protection, distributed dynamic state estimation, and communication networks and systems in power industries.

George J. Cokkinides was born in Athens, Greece, in 1955. He received the BS, MS, and PhD degrees from the Georgia Institute of Technology, Atlanta, GA, USA, in 1978, 1980, and 1985, respectively. From 1983 to 1985, he was a Research Engineer at the Georgia Tech Research Institute. Since 1985, he has been with the University of South Carolina, Columbia, SC, USA, where he is currently an Associate Professor of Electrical Engineering. His research interests include power system modeling and simulation, power electronics applications, power system harmonics, and measurement instrumentation. Professor Cokkinides is a member of the IEEE Power and Energy Society.

Venkata Dinavahi received the BEng. degree in electrical engineering from the Visveswaraya National Institute of Technology (VNIT), Nagpur, India, in 1993, the MTech. degree in Electrical Engineering from the Indian Institute of Technology (IIT) Kanpur, India, in 1996, and the PhD degree in Electrical and Computer engineering from the University of Toronto, Ontario, Canada, in 2000. Presently he is a Professor with the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada. His research interests include real-time simulation of power systems and power electronic systems, electromagnetic transients, device-level modeling, large-scale systems, and parallel and distributed computing. He is a Fellow of IEEE.

James W. Feltes received his BS degree with honors in Electrical Engineering from Iowa State University and his MS degree in Electrical Engineering from Union College.

He joined Power Technologies, Inc. (PTI), now part of Siemens Power Transmission and Distribution Inc., in 1979 and is currently a senior manager. At PTI, he has participated in many studies involving planning, analysis, and design of transmission and distribution systems. He has also been involved in many projects involving the development of models for studies of power system dynamics, testing to record equipment response, and model parameter derivation.

He is a registered professional engineer in the state of New York and a Fellow of the IEEE. He is a member of the IEEE Power Engineering Society and Industry Applications Society and is active on several IEEE committees and task forces.

Georgios B. Giannakis (Fellow’97) received his Diploma in Electrical Engineering from the National Technical University of Athens, Greece, 1981. From 1982 to 1986 he was with the University of Southern California (USC), where he received his MSc in Electrical Engineering, 1983, MSc in Mathematics, 1986, and PhD in Electrical Engineering, 1986. He was a faculty member with the University of Virginia from 1987 to 1998, and since 1999 he has been a professor with the University of Minnesota, where he holds an ADC Endowed Chair, a University of Minnesota McKnight Presidential Chair in ECE, and serves as director of the Digital Technology Center.

His general interests span the areas of statistical learning, communications, and networking – subjects on which he has published more than 470 journal papers, 770 conference papers, 25 book chapters, two edited books, and two research monographs. His current research focuses on Data Science, and Network Science with applications to the Internet of Things, and power networks with renewables. He is the (co-) inventor of 34 issued patents, and the (co-) recipient of 9 best journal paper awards from the IEEE Signal Processing (SP) and Communications Societies, including the G. Marconi Prize Paper Award in Wireless Communications. He also received the IEEE-SPS Norbert Wiener Society Award (2019); EURASIP’s A. Papoulis Society Award (2020); Technical Achievement Awards from the IEEE-SPS (2000) and from EURASIP (2005); the IEEE ComSoc Education Award (2019); the G. W. Taylor Award for Distinguished Research from the University of Minnesota, and the IEEE Fourier Technical Field Award (2015). He is a foreign member of the Academia Europaea, and Fellow of the National Academy of Inventors, the European Academy of Sciences, IEEE and EURASIP. He has served the IEEE in a number of posts, including that of a Distinguished Lecturer for the IEEE-SPS.

Manimaran Govindarasu currently holds the Mehl Professor of Computer Engineering at Iowa State University. His area of expertise includes CPS for the smart grid, cyber security, and real‐time systems and networks. He is a Fellow of the IEEE.

Ye Guo is an Assistant Professor at Tsinghua-Berkeley Shenzhen Institute, Tsinghua University. He received his bachelor degree in 2008 and doctoral degree in 2013, both from the Department of Electrical Engineering, Tsinghua University. He was a Postdoctoral Associate at Cornell University between 2014 and 2018. His research interests include distributed optimization, game and market theory, state estimation, and their applications in power and energy systems. He has received the Best-of-the-Best paper award and another Best Paper Award at IEEE PES General Meeting, 2019, and another Best Paper Award at IEEE PES General Meeting 2020. He also received the Best Poster Award at PSERC IAB Meeting 2018.

Ibrahim Omar Habiballah is an Associate Professor of EE Department at King Fahd University of Petroleum and Minerals, Saudi Arabia. In his area he taught several undergraduate and graduate courses in electrical, power systems, power transmission, and electrical machines. His research interests include power systems in general, power system state estimation, power system optimization, HV insulators, and energy conservation.

Araceli Hernández received the PhD degree in Electrical Engineering from the Universidad Politécnica de Madrid (UPM), Madrid, Spain, in 2000. Currently, she works at the Department of Control, Electrical and Electronic Engineering and Computing at UPM, where she is an Associate Professor. Her fields of interest include power system analysis and power quality estimation and measurement.

Hadis Karimipour received the PhD degree from the University of Alberta in 2016. She is currently a Postdoctoral Fellow at the Department of Electrical and Computer Engineering at the University of Calgary. Her research interests include large‐scale power system state estimation, cyber‐physical modeling, cybersecurity of the smart grids, and parallel and distributed computing.

Vassilis Kekatos is an Assistant Professor at the Bradley Department of ECE at Virginia Tech. He obtained his Diploma, MSc, and PhD in Computer Science and Engineering from the University of Patras, Greece, in 2001, 2003, and 2007, respectively. He was a recipient of a Marie Curie Fellowship during 2009–2012 and a research associate with the ECE Department at the University of Minnesota, where he received the postdoctoral career development award (honorable mention). During 2014, he stayed with the University of Texas at Austin and the Ohio State University as a visiting researcher. His research focus is on optimization and learning for future energy systems. He is currently serving in the editorial board of the IEEE Trans. on Smart Grid.

Mert Korkali received his MS and PhD degrees in Electrical Engineering from Northeastern University, Boston, MA, USA, in 2010 and 2013, respectively. He is currently a Research Staff Member at the Computational Engineering Division at Lawrence Livermore National Laboratory, Livermore, CA, USA. Previously, he was a Postdoctoral Research Associate at the University of Vermont, Burlington, VT, USA. His current research interests lie at the broad interface of robust state estimation and fault location in power systems, extreme event modeling, cascading failures, uncertainty quantification, and probabilistic grid planning. He is the Co-chair of the IEEE Task Force on Standard Test Cases for Power System State Estimation. He is currently serving as an Editor of the IEEE Open Access Journal of Power and Energy and of the IEEE Power Engineering Letters, and an Associate Editor of Journal of Modern Power Systems and Clean Energy. Dr. Korkali is a Senior Member of IEEE.

Massimo La Scala is Professor of Electrical Energy Systems at Politecnico di Bari and IEEE Fellow. He has been Principal Investigator of numerous research projects in smart grids and smart cities and scientific consultant of the Ministry of the Economic Development in Italy and of AEEGSI the Italian Regulatory Authority of Electricity, Gas and Water. He is the director of the “Laboratory for the development of renewables and energy efficiency: Lab ZERO” at Politecnico di Bari.

Yu Liu was born in Hefei, China, in 1990. He received the BS and MS degrees in Electric Power Engineering from Shanghai Jiao Tong University, Shanghai, China, in 2011 and 2013, respectively, and the MS degree in electrical and computer engineering in 2013 from Georgia Institute of Technology, Atlanta, GA, USA, where he is currently working toward the PhD degree in electrical and computer engineering. His research interests include power system protection, parameter estimation, and circuit fault locating.

A.P. Sakis Meliopoulos was born in Katerini, Greece, in 1949. He received the ME and EE Diploma in Electrical Engineering from the National Technical University of Athens, Athens, Greece, in 1972 and the MSEE and PhD degrees in electrical engineering from the Georgia Institute of Technology, Atlanta, GA, USA, in 1974 and 1976, respectively. He is presently a Georgia Power Distinguished Professor. He has published three books, holds three patents, and has published more than 300 technical papers. Professor Meliopoulos received the IEEE Richard Kaufman Award in 2005, and in 2010, he received the George Montefiore Award from the Montefiore Institute, Belgium. He is the Chairman of the Georgia Tech Protective Relaying Conference and a member of Sigma Xi.

Hyde M. Merrill received the BA degree in Mathematics and MS degree in Electrical Engineering from the University of Utah and the PhD degree in Electrical Engineering from the Massachusetts Institute of Technology. He is a registered professional engineer in New York and a Fellow of the IEEE.

He has worked for the American Electric Power Service Corp, the MIT Energy Lab, Power Technologies, Inc., the Rensselaer Polytechnic Institute, and Merrill Energy LLC. In 2015, he joined the University of Utah as Adjunct Professor. He teaches power systems and leads research on blackouts.

Lamine Mili is a Professor of Electrical and Computer Engineering at Virginia Tech. He is an IEEE Fellow and a member of Institute of Mathematical Statistics and the American Statistical Association. His research interests include power system analysis and control, power system dynamics and stability, and robust statistics as applied to engineering problems.

Michael Negnevitsky received his BE (Hons.) and PhD degrees from the Byelorussian University of Technology, Belarus, in 1978 and 1983, respectively. Currently, he is a Professor in Power Engineering and Computational Intelligence and Director of the Centre for Renewable Energy and Power Systems, University of Tasmania, Australia. He is a Chartered Professional Engineer, Fellow of Engineers Australia, and Member of the National ITEE College Board. His research interests include power system security, renewable energy, and state estimation.

Marco Pau received the MS degree (cum laude) in Electrical Engineering and the PhD degree in Electronic Engineering and Computer Science from the University of Cagliari, Italy, in 2011 and 2015, respectively. Currently, he is research associate at the Institute for Automation of Complex Power Systems at the E.ON Energy Research Center, RWTH Aachen University, Germany, where he leads the team for Distribution Grid Monitoring and Automation. His research activities mainly concern the design of solutions for the monitoring and automation of distribution systems as well as techniques for the smart management of active distribution grids.

Paolo Attilio Pegoraro received the MS (summa cum laude) degree in Telecommunications engineering and the PhD degree in Electronic and Telecommunication Engineering from the University of Padova, Padua, Italy, in 2001 and 2005, respectively. From 2015 to 2018 he was an Assistant Professor with the Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy, where he is currently Associate Professor. He has authored or coauthored over 110 scientific papers. His current research interests include the development of new measurement techniques for modern power networks, with attention to synchronized measurements and state estimation.

Dr. Pegoraro is a Senior Member of IEEE Instrumentation and Measurement Society, member of TC 39 (Measurements in Power Systems) and of IEC TC 38/WG 47. He is an Associate Editor of the IEEE Transactions on Instrumentation and Measurement.

Ferdinanda Ponci graduated with PhD in Electrical Engineering from the Politecnico di Milano, in 2002. She joined the Department of Electrical Engineering, University of South Carolina, as an Assistant Professor in 2003 and became Associate Professor in 2008. In 2009, she joined the Institute for Automation of Complex Power Systems, RWTH Aachen University, where she is currently Professor for “Monitoring and distributed control for power systems.”

She is Senior Member of IEEE and of the AdCom of the IEEE Instrumentation and Measurement Society.

Md. Ashfaqur Rahman is a PhD candidate in the Department of Electrical and Computer Engineering in Clemson University, Clemson, SC, USA. He received his BS from Bangladesh University of Engineering and Technology in 2009 and MS from Texas Tech University in 2012. He has a total of 7 technical papers with 98 citations with h‐index and i‐index be 3. His current research interests include the development of a distributed dynamic state estimator. He also worked on false data injection attack, parallel and distributed computation, state prediction, contingency analysis, optimal power flow, etc. He has served as a reviewer of IEEE journals and conference papers.

Sara Sulis received the MS degree in Electrical Engineering and the PhD degree in Industrial Engineering from the University of Cagliari, Cagliari, Italy, in 2002 and 2006, respectively. She is currently Associate Professor of Instrumentation and Measurements with the University of Cagliari. Dr. Sulis is a Senior Member of the IEEE, member of the Instrumentation and Measurement Society, of the IEEE TC 39 “Measurements in Power Systems,” and of the CENELEC TC 38 “Instrument Transformers.” She has authored or coauthored more than 100 scientific papers. Her current research interests include distributed measurement systems designed to perform state estimation and harmonic sources estimation of distribution networks.

Hongbin Sun is a Professor in the Department of Electrical Engineering, Tsinghua University, Beijing, China, the Changjiang Chair Professor of Education Ministry of China, and an IEEE Fellow. He received double BS degrees in 1992 and PhD in 1997, respectively, both from Electrical Engineering, Tsinghua University. His research interests include automatic voltage control (AVC), smart grid, renewable energy and electrical vehicle integration, and power system operation and control.

Lang Tong is the Irwin and Joan Jacobs Professor of Engineering at Cornell University and the Cornell site Director of Power Systems Engineering Research Center (PSERC). He received a BE degree from Tsinghua University and a PhD degree in Electrical Engineering from the University of Notre Dame. He held visiting positions at Stanford University, the University of California at Berkeley, the Delft University of Technology, and the Chalmers University of Technology in Sweden.

Lang Tong’s current research focuses on data analytics, optimization, and economic problems in energy and power systems. A Fellow of IEEE and the 2018 Fulbright Distinguished Chair in Alternative Energy, he received paper awards from the IEEE Circuit and Systems, Signal Processing, Communications, and Power and Energy Systems societies.

Ganesh Kumar Venayagamoorthy is the Duke Energy Distinguished Professor of Power Engineering and Professor of Electrical and Computer Engineering at Clemson University. Dr. Venayagamoorthy is the Founder (2004) and Director of the Real-Time Power and Intelligent Systems Laboratory (http://rtpis.org). He holds an Honorary Professor position in the School of Engineering at the University of Kwazulu-Natal, Durban, South Africa. Dr. Venayagamoorthy received his PhD and MSc (Eng.) degrees in Electrical Engineering from the University of Natal, Durban, South Africa, in February 2002 and April 1999, respectively. He received his B.Eng. (Honors) degree with a First Class from Abubakar Tafawa Balewa University, Bauchi, Nigeria, in March 1994. He holds a MBA degree in Entrepreneurship and Innovation from Clemson University, SC (2016). Dr. Venayagamoorthy’s interests are in the research, development, and innovation of smart grid technologies and operations, including computational intelligence, intelligent sensing and monitoring, intelligent systems, integration of renewable energy sources, power system optimization, stability and control, and signal processing. He is an inventor of technologies for scalable computational intelligence for complex systems and dynamic stochastic optimal power flow. He led the brain2grid project funded by US NSF. He has published over 500 refereed technical articles. His publications are cited >18,000 times with a h-index of 64. Dr. Venayagamoorthy has been involved in over 75 sponsored projects in excess of US $12 million. Dr. Venayagamoorthy has given over 500 invited keynotes, plenaries, presentations, tutorials, and lectures in over 40 countries to date. He has several international educational and research collaborations. Dr. Venayagamoorthy is a Senior Member of the IEEE, and a Fellow of the IET, UK, and the SAIEE.

Gang Wang received the BEng. degree in Automatic Control from the Beijing Institute of Technology, Beijing, China, in 2011, and the PhD degree in Electrical Engineering from the University of Minnesota, Minneapolis, USA, in 2018, where he stayed as a postdoctoral researcher until 2020. Since August 2020, he has been a professor with the School of Automation, Beijing Institute of Technology. His research interests focus on the areas of signal processing, deep learning, and reinforcement learning with applications to cyber-physical systems and data science. He was the recipient of the Excellent Doctoral Dissertation Award from the Chinese Association of Automation in 2019, the Best Student Paper Award from the 2017 European Signal Processing Conference, and the Best Conference Paper at the 2019 IEEE Power & Energy Society General Meeting.

Wenchuan Wu is a Professor in the Department of Electrical Engineering, Tsinghua University, Beijing, China. He received his BS in 1996, MS in 1999, and PhD degrees in 2003 all from the Electrical Engineering Department, Tsinghua University. His research interests include Energy Management System, active distribution system operation and control, and EMTP‐TSA hybrid real‐time simulation. He is an Associate Editor of IEE Proceedings – Generation, Transmission and Distribution and Journal of Electric Power Components and Systems.

Yuanhai Xia is an Electrical Engineer with China State Construction Engineering Corporation (Middle East). He has one and half years’ experience in building electric and half year in high voltage power transmission. He got his MSc from KFUPM, Saudi Arabia, in electrical and power system in 2014. He is familiar with international and domestic codes/standards, AutoCAD drawing, master excel skills with VB programming, and other programming language such as Matlab, python, and Linux shell.

Boming Zhang is a Professor in the Department of Electrical Engineering, Tsinghua University, Beijing, China. He received MEng. from Harbin Institute of Technology in 1982 and PhD from Tsinghua University in 1985, both in Electrical Engineering. He has been serving for Tsinghua University since 1985. His research area includes power system analysis, computer application in power system control center, etc. He won IEEE PES/CSEE Yu‐Hsiu Ku Electrical Engineering Award in 2015.

Junbo Zhao (SM’19) received the PhD degree from the Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA, in 2018. He was an Assistant Professor (Research) with Virginia Tech from May 2018 to August 2019. He did the summer internship at the Pacific Northwest National Laboratory from May 2017 to August 2017. He is currently an Assistant Professor with Mississippi State University, Starkville, MS, USA. He has written three book chapters and published more than 70 peer‐reviewed journal and conference papers, among which there are three ESI papers. His research interests are power system modeling, state estimation, dynamics and cybersecurity, synchrophasor applications, renewable energy integration and control, and robust statistical signal processing and machine learning.

Dr. Zhao is a co‐recipient of the best paper award of 2019 IEEE PES ISGT Asia, and the best reviewer of the IEEE TRANSACTIONS ON POWER SYSTEMS 2018 and the IEEE TRANSACTIONS ON SMART GRID 2019. He is currently the Chair of the IEEE Task Force on Power System Dynamic State and Parameter Estimation, and the Secretary of the IEEE Working Group on State Estimation Algorithms and the IEEE Task Force on Synchrophasor Applications in Power System Operation and Control. He serves as the Associate Editor of the IEEE TRANSACTIONS ON POWER SYSTEMS, the IEEE TRANSACTIONS ON SMART GRID, and International Journal of Electrical Power and Energy Systems, and the Subject Editor of IET Generation, Transmission and Distribution.

Hao Zhu is an Assistant Professor of ECE at University of Texas at Austin. She received a BE degree from Tsinghua University in 2006 and MSc and PhD degrees from the University of Minnesota in 2009 and 2012, all in Electrical Engineering. Her current research interests include power grid monitoring, distribution system operations and control, and energy data analytics. She received the NSF CAREER Award in 2017, the Siebel Energy Institute Seed Grant Award and the US AFRL Summer Faculty Fellowship in 2016.

CHAPTER 1GENERAL CONSIDERATIONS

Mohamed E. El‐Hawary

Dalhousie University in Halifax, Nova Scotia, Canada

In this introductory chapter, we introduce the concept of state estimation (SE) in electric power system and trace its evolution from a historical perspective. SE emerged as an indispensable real‐time tool that is part of a suite of applications designed to support and enable electric power operators' “situational awareness.” The term “situational awareness” in the context of power grid operation is “understanding the present environment and being able to accurately anticipate future problems to enable effective actions.”

This chapter offers a detailed discussion of the role of SE in practice. A guide to the chapters included in this volume is offered to conclude the chapter.

1.1 PRELUDE

At the IEEE Power Industry Computer Applications (PICA) conference held on 18–21 May 1969 in Denver, Colorado, Professor Fred C. Schweppe and his associates presented a three‐part paper on static state estimation and related detection and identification problems in electric power systems. The papers were subsequently published in the IEEE Transactions on Power Apparatus and Systems [1–3]. The first paper [1] introduced the overall problem statement, mathematical modeling, and general algorithms for state estimation, detection, and identification (SEDI) using weighted least squares (WLS) approximations. The second paper [2] discussed an approximate mathematical model and the resulting simplifications in SEDI. The third paper [3] dealt with implementation problems, considerations of dimensionality, execution speed and storage, and the time-varying nature of actual power systems.

A year later, Merrill and Schweppe [4] introduced a bad data suppression (BDS) estimator, which is computationally very similar to WLS approximation. The concept is no more complex, and bad data detection and identification can be performed “for free,” since BDS requires no more computer time or complexity than does WLS, and in the absence of bad data, BDS reduces to WLS.

1.2 DEFINING SSE

In 1974, Schweppe and Handschin [5] described state estimation (SE) using the following metaphor: “The life blood of the control system is a base of clean pure data defining the system state and status (voltages, network configuration). This life blood is obtained from the nourishment provided by the measurements gathered from around the system (data acquisition). A static state estimator is the digestive system which removes the impurities from the measurements and converts them into a form which the brain (man or computer) of the central control system can readily use to make ‘action’ decisions on system economy, quality, and security.”

Reference [1] formally defines the static state of an electric power system as the vector of voltage magnitudes and angles at all network buses. The static state estimator (SSE) is a data processing algorithm for converting imperfect redundant meter readings and other available information to an estimate of the static state.

Item 603‐02‐09 of the International Electrotechnical Commission (IEC) Electropedia [6] offers the following definition of “state estimation” as “the computation of the most probable currents and voltages within the network at a given instant by solving a system of mostly nonlinear equations whose parameters are obtained by means of redundant measurements.”

The North American Electric Reliability Corporation (NERC) Real‐Time Tools Best Practices Task Force (RTBPTF) 2008 final report [7