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Dynamic Vulnerability

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Dynamic Vulnerability Assessment andIntelligent Control for Sustainable Power Systems

Edited byProfessor José Luis Rueda-TorresDelft University of TechnologyThe Netherlands

Professor Francisco González-LongattLoughborough UniversityLeicestershire, United Kingdom

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This edition first published 2018© 2018 John Wiley & Sons Ltd

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, ortransmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise,except as permitted by law. Advice on how to obtain permission to reuse material from this title is availableat http://www.wiley.com/go/permissions.

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Library of Congress Cataloging-in-Publication Data:

Names: Rueda-Torres, José Luis, 1980- author. | González-Longatt, Francisco,1972- author.

Title: Dynamic vulnerability assessment and intelligent control forsustainable power systems / edited by Professor José Luis Rueda-Torres,Professor Francisco González-Longatt.

Description: First edition. | Hoboken, NJ : John Wiley & Sons, 2018. |Includes bibliographical references and index. |

Identifiers: LCCN 2017042787 (print) | LCCN 2017050856 (ebook) | ISBN9781119214977 (pdf) | ISBN 9781119214960 (epub) | ISBN 9781119214953(cloth)

Subjects: LCSH: Electric power distribution–Testing. | Smart power grids.Classification: LCC TK3081 (ebook) | LCC TK3081 .D96 2018 (print) | DDC

621.31/7–dc23LC record available at https://lccn.loc.gov/2017042787

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Contents

List of Contributors xvForeword xixPreface xxi

1 Introduction: The Role of Wide Area Monitoring Systems in DynamicVulnerability Assessment 1Jaime C. Cepeda and José Luis Rueda-Torres

1.1 Introduction 11.2 Power System Vulnerability 21.2.1 Vulnerability Assessment 21.2.2 Timescale of Power System Actions and Operations 41.3 Power System Vulnerability Symptoms 51.3.1 Rotor Angle Stability 61.3.1.1 Transient Stability 61.3.1.2 Oscillatory Stability 61.3.2 Short-Term Voltage Stability 71.3.3 Short-Term Frequency Stability 71.3.4 Post-Contingency Overloads 71.4 Synchronized Phasor Measurement Technology 81.4.1 Phasor Representation of Sinusoids 81.4.2 Synchronized Phasors 91.4.3 Phasor Measurement Units (PMUs) 91.4.4 Discrete Fourier Transform and Phasor Calculation 101.4.5 Wide Area Monitoring Systems 101.4.6 WAMPAC Communication Time Delay 121.5 The Fundamental Role of WAMS in Dynamic Vulnerability Assessment 131.6 Concluding Remarks 16

References 17

2 Steady-state Security 21Evelyn Heylen, Steven De Boeck, Marten Ovaere, Hakan Ergun, and Dirk Van Hertem

2.1 Power System Reliability Management: A Combination of ReliabilityAssessment and Reliability Control 22

2.1.1 Reliability Assessment 232.1.2 Reliability Control 24

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2.1.2.1 Credible and Non-Credible Contingencies 252.1.2.2 Operating State of the Power System 252.1.2.3 System State Space Representation 282.2 Reliability Under Various Timeframes 312.3 Reliability Criteria 332.4 Reliability and Its Cost as a Function of Uncertainty 342.4.1 Reliability Costs 342.4.2 Interruption Costs 352.4.3 Minimizing the Sum of Reliability and Interruption Costs 362.5 Conclusion 37

References 38

3 Probabilistic Indicators for the Assessment of Reliability and Securityof Future Power Systems 41Bart W. Tuinema, Nikoleta Kandalepa, and José Luis Rueda-Torres

3.1 Introduction 413.2 Time Horizons in the Planning and Operation of Power Systems 423.2.1 Time Horizons 423.2.2 Overlapping and Interaction 423.2.3 Remedial Actions 423.3 Reliability Indicators 453.3.1 Security-of-Supply Related Indicators 453.3.2 Additional Indicators 473.4 Reliability Analysis 493.4.1 Input Information 493.4.2 Pre-calculations 503.4.3 Reliability Analysis 503.4.4 Output: Reliability Indicators 533.5 Application Example: EHV Underground Cables 533.5.1 Input Parameters 543.5.2 Results of Analysis 563.6 Conclusions 58

References 60

4 An Enhanced WAMS-based Power System Oscillation AnalysisApproach 63Qing Liu, Hassan Bevrani, and Yasunori Mitani

4.1 Introduction 634.2 HHT Method 654.2.1 EMD 654.2.2 Hilbert Transform 654.2.3 Hilbert Spectrum and Hilbert Marginal Spectrum 664.2.4 HHT Issues 674.2.4.1 The Boundary End Effect 694.2.4.2 Mode Mixing and Pseudo-IMF Component 704.2.4.3 Parameter Identification 71

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4.3 The Enhanced HHT Method 714.3.1 Data Pre-treatment Processing 714.3.1.1 DC Removal Processing 724.3.1.2 Digital Band-Pass Filter Algorithm 724.3.2 Inhibiting the Boundary End Effect 754.3.2.1 The Boundary End Effect Caused by the EMD Algorithm 754.3.2.2 Inhibiting the Boundary End Effects Caused by the EMD 764.3.2.3 The Boundary End Effect Caused by the Hilbert Transform 764.3.2.4 Inhibiting the Boundary End Effect Caused by the HT 794.3.3 Parameter Identification 804.4 Enhanced HHT Method Evaluation 814.4.1 Case I 814.4.2 Case II 844.4.3 Case III 854.5 Application to Real Wide Area Measurements 88

Summary 92References 93

5 Pattern Recognition-Based Approach for Dynamic VulnerabilityStatus Prediction 95Jaime C. Cepeda, José Luis Rueda-Torres, Delia G. Colomé, and István Erlich

5.1 Introduction 955.2 Post-contingency Dynamic Vulnerability Regions 965.3 Recognition of Post-contingency DVRs 975.3.1 N-1 Contingency Monte Carlo Simulation 985.3.2 Post-contingency Pattern Recognition Method 1005.3.3 Definition of Data-Time Windows 1035.3.4 Identification of Post-contingency DVRs—Case Study 1045.4 Real-Time Vulnerability Status Prediction 1095.4.1 Support Vector Classifier (SVC) Training 1125.4.2 SVC Real-Time Implementation 1135.5 Concluding Remarks 115

References 115

6 Performance Indicator-Based Real-Time VulnerabilityAssessment 119Jaime C. Cepeda, José Luis Rueda-Torres, Delia G. Colomé, and István Erlich

6.1 Introduction 1196.2 Overview of the Proposed Vulnerability Assessment Methodology 1206.3 Real-Time Area Coherency Identification 1226.3.1 Associated PMU Coherent Areas 1226.4 TVFS Vulnerability Performance Indicators 1256.4.1 Transient Stability Index (TSI) 1256.4.2 Voltage Deviation Index (VDI) 1286.4.3 Frequency Deviation Index (FDI) 1316.4.4 Assessment of TVFS Security Level for the Illustrative Examples 1316.4.5 Complete TVFS Real-Time Vulnerability Assessment 133

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6.5 Slower Phenomena Vulnerability Performance Indicators 1376.5.1 Oscillatory Index (OSI) 1376.5.2 Overload Index (OVI) 1416.6 Concluding Remarks 145

References 145

7 Challenges Ahead Risk-Based AC Optimal Power Flow UnderUncertainty for Smart Sustainable Power Systems 149Florin Capitanescu

7.1 Chapter Overview 1497.2 Conventional (Deterministic) AC Optimal Power Flow (OPF) 1507.2.1 Introduction 1507.2.2 Abstract Mathematical Formulation of the OPF Problem 1507.2.3 OPF Solution via Interior-Point Method 1517.2.3.1 Obtaining the Optimality Conditions In IPM 1517.2.3.2 The Basic Primal Dual Algorithm 1527.2.4 Illustrative Example 1547.2.4.1 Description of the Test System 1547.2.4.2 Detailed Formulation of the OPF Problem 1557.2.4.3 Analysis of Various Operating Modes 1567.2.4.4 Iterative OPF Methodology 1577.3 Risk-Based OPF 1587.3.1 Motivation and Principle 1587.3.2 Risk-Based OPF Problem Formulation 1597.3.3 Illustrative Example 1607.3.3.1 Detailed Formulation of the RB-OPF Problem 1607.3.3.2 Numerical Results 1617.4 OPF Under Uncertainty 1627.4.1 Motivation and Potential Approaches 1627.4.2 Robust Optimization Framework 1627.4.3 Methodology for Solving the R-OPF Problem 1637.4.4 Illustrative Example 1647.4.4.1 Detailed Formulation of the Worst Uncertainty Pattern Computation With

Respect to a Contingency 1647.4.4.2 Detailed Formulation of the OPF to Check Feasibility in the Presence of

Corrective Actions 1667.4.4.3 Detailed Formulation of the R-OPF Relaxation 1667.4.4.4 Numerical Results 1687.5 Advanced Issues and Outlook 1697.5.1 Conventional OPF 1697.5.1.1 Overall OPF Solution Methodology 1697.5.1.2 Core Optimizers: Classical Methods Versus Convex Relaxations 1717.5.2 Beyond the Scope of Conventional OPF: Risk, Uncertainty, Smarter

Sustainable Grid 172References 173

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8 Modeling Preventive and Corrective Actions Using LinearFormulation 177Tom Van Acker and Dirk Van Hertem

8.1 Introduction 1778.2 Security Constrained OPF 1788.3 Available Control Actions in AC Power Systems 1788.3.1 Generator Redispatch 1798.3.2 Load Shedding and Demand Side Management 1798.3.3 Phase Shifting Transformer 1798.3.4 Switching Actions 1808.3.5 Reactive Power Management 1808.3.6 Special Protection Schemes 1808.4 Linear Implementation of Control Actions in a SCOPF Environment 1808.4.1 Generator Redispatch 1818.4.2 Load Shedding and Demand Side Management 1828.4.3 Phase Shifting Transformer 1838.4.4 Switching 1848.5 Case Study of Preventive and Corrective Actions 1858.5.1 Case Study 1: Generator Redispatch and Load Shedding (CS1) 1868.5.2 Case Study 2: Generator Redispatch, Load Shedding and PST (CS2) 1878.5.3 Case Study 3: Generator Redispatch, Load Shedding and Switching

(CS3) 1908.6 Conclusions 191

References 191

9 Model-based Predictive Control for Damping ElectromechanicalOscillations in Power Systems 193Da Wang

9.1 Introduction 1939.2 MPC Basic Theory & Damping Controller Models 1949.2.1 What is MPC? 1949.2.2 Damping Controller Models 1969.3 MPC for Damping Oscillations 1989.3.1 Outline of Idea 1989.3.2 Mathematical Formulation 1999.3.3 Proposed Control Schemes 2009.3.3.1 Centralized MPC 2009.3.3.2 Decentralized MPC 2009.3.3.3 Hierarchical MPC 2029.4 Test System & Simulation Setting 2049.5 Performance Analysis of MPC Schemes 2049.5.1 Centralized MPC 2049.5.1.1 Basic Results in Ideal Conditions 2049.5.1.2 Results Considering State Estimation Errors 2069.5.1.3 Consideration of Control Delays 208

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9.5.2 Distributed MPC 2099.5.3 Hierarchical MPC 2099.6 Conclusions and Discussions 213

References 214

10 Voltage Stability Enhancement by Computational IntelligenceMethods 217Worawat Nakawiro

10.1 Introduction 21710.2 Theoretical Background 21810.2.1 Voltage Stability Assessment 21810.2.2 Sensitivity Analysis 21910.2.3 Optimal Power Flow 22010.2.4 Artificial Neural Network 22010.2.5 Ant Colony Optimisation 22110.3 Test Power System 22310.4 Example 1: Preventive Measure 22410.4.1 Problem Statement 22410.4.2 Simulation Results 22510.5 Example 2: Corrective Measure 22610.5.1 Problem Statement 22610.5.2 Simulation Results 22710.6 Conclusions 229

References 230

11 Knowledge-Based Primary and Optimization-Based SecondaryControl of Multi-terminal HVDC Grids 233Adedotun J. Agbemuko, Mario Ndreko, Marjan Popov, José Luis Rueda-Torres, andMart A.M.M van der Meijden

11.1 Introduction 23411.2 Conventional Control Schemes in HV-MTDC Grids 23411.3 Principles of Fuzzy-Based Control 23611.4 Implementation of the Knowledge-Based Power-Voltage Droop Control

Strategy 23611.4.1 Control Scheme for Primary and Secondary Power-Voltage Control 23711.4.2 Input/Output Variables 23811.4.2.1 Membership Functions and Linguistic Terms 23911.4.3 Knowledge Base and Inference Engine 24111.4.4 Defuzzification and Output 24111.5 Optimization-Based Secondary Control Strategy 24211.5.1 Fitness Function 24211.5.2 Constraints 24411.6 Simulation Results 24511.6.1 Set Point Change 24511.6.2 Constantly Changing Reference Set Points 24611.6.3 Sudden Disconnection of Wind Farm for Undefined Period 24611.6.4 Permanent Outage of VSC 3 247

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11.7 Conclusion 247References 248

12 Model Based Voltage/Reactive Control in Sustainable DistributionSystems 251Hoan Van Pham and Sultan Nasiruddin Ahmed

12.1 Introduction 25112.2 Background Theory 25212.2.1 Voltage Control 25212.2.2 Model Predictive Control 25312.2.3 Model Analysis 25512.2.3.1 Definition of Sensitivity 25512.2.3.2 Computation of Sensitivity 25512.2.4 Implementation 25712.3 MPC Based Voltage/Reactive Controller – an Example 25812.3.1 Control Scheme 25812.3.2 Overall Objective Function of the MPC Based Controller 25912.3.3 Implementation of the MPC Based Controller 26112.4 Test Results 26212.4.1 Test System and Measurement Deployment 26212.4.2 Parameter Setup and Algorithm Selection for the Controller 26312.4.3 Results and Discussion 26312.4.3.1 Loss Minimization Performance of the Controller 26312.4.3.2 Voltage Correction Performance of the Controller 26412.5 Conclusions 266

References 267

13 Multi-Agent based Approach for Intelligent Control of Reactive PowerInjection in Transmission Systems 269Hoan Van Pham and Sultan Nasiruddin Ahmed

13.1 Introduction 26913.2 System Model and Problem Formulation 27013.2.1 Power System Model 27013.2.2 Optimal Reactive Control Problem Formulation 27113.2.3 Multi-Agent Sensitivity Model 27213.2.3.1 Calculation of the First Layer 27313.2.3.2 Calculation of the Second Layer 27313.3 Multi-Agent Based Approach 27513.3.1 Augmented Lagrange Formulation 27513.3.2 Implementation Algorithm 27513.4 Case Studies and Simulation Results 27713.4.1 Case Studies 27713.4.2 Simulation Results 27713.4.2.1 Performance Comparison Between Multi-Agent Based and Single-Agent

Based System 27813.4.2.2 Impacts of General Parameters on the Proposed Control Scheme’s

Performance 279

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13.4.2.3 Impacts of Multi-Agent Parameters on the Proposed Control Scheme’sPerformance 279

13.5 Conclusions 280References 281

14 Operation of Distribution Systems Within Secure Limits UsingReal-Time Model Predictive Control 283Hamid Soleimani Bidgoli, Gustavo Valverde, Petros Aristidou, Mevludin Glavic, andThierry Van Cutsem

14.1 Introduction 28314.2 Basic MPC Principles 28514.3 Control Problem Formulation 28514.4 Voltage Correction With Minimum Control Effort 28814.4.1 Inclusion of LTC Actions as Known Disturbances 28914.4.2 Problem Formulation 29014.5 Correction of Voltages and Congestion Management with Minimum

Deviation from References 29114.5.1 Mode 1 29214.5.2 Mode 2 29214.5.3 Mode 3 29414.5.4 Problem Formulation 29514.6 Test System 29614.7 Simulation Results: Voltage Correction with Minimal Control Effort 29814.7.1 Scenario A 29914.7.2 Scenario B 30014.8 Simulation Results: Voltage and/or Congestion Corrections with Minimum

Deviation from Reference 30214.8.1 Scenario C: Mode 1 30214.8.2 Scenario D: Modes 1 and 2 Combined 30414.8.3 Scenario E: Modes 1 and 3 Combined 30514.9 Conclusion 306

References 308

15 Enhancement of Transmission System Voltage Stability through LocalControl of Distribution Networks 311Gustavo Valverde, Petros Aristidou, and Thierry Van Cutsem

15.1 Introduction 31115.2 Long-Term Voltage Stability 31315.2.1 Countermeasures 31415.3 Impact of Volt-VAR Control on Long-Term Voltage Stability 31615.3.1 Countermeasures 31815.4 Test System Description 31915.4.1 Test System 31915.4.2 VVC Algorithm 32115.4.3 Emergency Detection 32215.5 Case Studies and Simulation Results 32315.5.1 Results in Stable Scenarios 323

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15.5.1.1 Case A1 32315.5.1.2 Case A2 32415.5.2 Results in Unstable Scenarios 32615.5.2.1 Case B1 32615.5.2.2 Case B2 32615.5.3 Results with Emergency Support From Distribution 32815.5.3.1 Case C1 32815.5.3.2 Case C2 32915.5.3.3 Case C3 33315.6 Conclusion 334

References 334

16 Electric Power Network Splitting Considering Frequency Dynamicsand Transmission Overloading Constraints 337Nelson Granda and Delia G. Colomé

16.1 Introduction 33716.1.1 Stage One: Vulnerability Assessment 33716.1.2 Stage Two: Islanding Process 33816.2 Network Splitting Mechanism 34016.2.1 Graph Modeling, Update, and Reduction 34116.2.2 Graph Partitioning Procedure 34216.2.3 Load Shedding/Generation Tripping Schemes 34316.2.4 Tie-Lines Determination 34416.3 Power Imbalance Constraint Limits 34416.3.1 Reduced Frequency Response Model 34516.3.2 Power Imbalance Constraint Limits Determination 34716.4 Overload Assessment and Control 34816.5 Test Results 34916.5.1 Power System Collapse 34916.5.2 Application of Proposed Methodology 35116.5.3 Performance of Proposed ACIS 35416.6 Conclusions and Recommendations 356

References 357

17 High-Speed Transmission Line Protection Based on EmpiricalOrthogonal Functions 361Rommel P. Aguilar and Fabián E. Pérez-Yauli

17.1 Introduction 36117.2 Empirical Orthogonal Functions 36317.2.1 Formulation 36317.3 Applications of EOFs for Transmission Line Protection 36517.3.1 Fault Direction 36617.3.2 Fault Classification 36717.3.2.1 Required EOF 36817.3.2.2 Fault Type Surfaces 36817.3.2.3 Defining the Fault Type 36817.3.3 Fault Location 369

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17.4 Study Case 36917.4.1 Transmission Line Model and Simulation 36917.4.2 The Power System and Transmission Line 37017.4.3 Training Data 37017.4.4 Training Data Matrix 37017.4.4.1 Data Window 37217.4.4.2 Sampling Frequency 37217.4.5 Signal Conditioning 37317.4.5.1 Superimposed Component 37317.4.5.2 Centering the Variables 37317.4.5.3 Scaling 37317.4.6 Energy Patterns 37317.4.7 EOF Analysis 37617.4.7.1 Computing the EOFs 37617.4.7.2 Fault Patterns Using EOF 37817.4.8 Evaluation of the Protection Scheme 37917.4.8.1 Fault Direction 37917.4.9 Fault Classification 38017.4.9.1 Classification 38117.4.10 Fault Location 38217.5 Conclusions 383

Appendix 17.A 384Study Cases: WECC 9-bus, ATPDraw Models and Parameters 384References 386

18 Implementation of a Real Phasor Based Vulnerability Assessment andControl Scheme: The Ecuadorian WAMPAC System 389Pablo X. Verdugo, Jaime C. Cepeda, Aharon B. De La Torre, and Diego E. Echeverría

18.1 Introduction 38918.2 PMU Location in the Ecuadorian SNI 39018.3 Steady-State Angle Stability 39118.4 Steady-State Voltage Stability 39518.5 Oscillatory Stability 39818.5.1 Power System Stabilizer Tuning 40218.6 Ecuadorian Special Protection Scheme (SPS) 40718.6.1 SPS Operation Analysis 40918.7 Concluding Remarks 410

References 410

Index 413

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List of Contributors

Jaime C. CepedaOperador Nacional de Electricidad(CENACE), and Escuela PolitécnicaNacional (EPN)QuitoEcuador

Dirk Van HertemESAT – ElectaUniversity of LeuvenBelgium

Steven De BoeckESAT – ElectaUniversity of LeuvenBelgium

Hakan ErgunESAT – ElectaUniversity of LeuvenBelgium

Evelyn HeylenESAT – ElectaUniversity of LeuvenBelgium

Tom Van AckerESAT – ElectaUniversity of LeuvenBelgium

Marten OvaereDepartment of EconomicsUniversity of LeuvenBelgium

Bart W. TuinemaDelft University of TechnologyThe Netherlands

Nikoleta KandalepaTenneT TSO B.VArnhemThe Netherlands

Qing LiuKyushu Institute of TechnologyKitakyushuJapan

Hassan BevraniUniversity of KurdistanSanandajIran

Yasunori MitaniKyushu Institute of TechnologyKitakyushuJapan

Delia G. ColoméUniversidad Nacional de San JuanArgentina

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István ErlichUniversity Duisburg-EssenDuisburgGermany

Florin CapitanescuLuxembourg Institute of Science andTechnology, BelvauxLuxembourg

Da WangDelft University of TechnologyThe Netherlands

Worawat NakawiroKing Mongkut’s Institute of TechnologyLadkrabangBangkokThailand

Adedotun J. AgbemukoInstitut de Recerca en Energia deCatalunya (IREC)BarcelonaSpain

Mario NdrekoTenneT TSO GmbHBayreuthGermany

Marjan PopovDelft University of TechnologyThe Netherlands

Mart A.M.M. van der MeijdenTenneT TSO B.VArnhemThe Netherlands and Delft University ofTechnologyThe Netherlands

Hoan Van PhamPower Generation Corporation 2Vietnam Electricity and School ofEngineering and TechnologyTra Vinh UniversityVietnam

Sultan Nasiruddin AhmedFGH GmbHAachenGermany

Gustavo ValverdeUniversity of Costa RicaSan JoseCosta Rica

Hamid Soleimani BidgoliUniversité de LiègeBelgium

Petros AristidouUniversity of LeedsUnited Kingdom

Mevludin GlavicUniversité de LiègeBelgium

Thierry Van CutsemUniversité de LiègeBelgium

Nelson GrandaEscuela Politécnica NacionalQuitoEcuador

Rommel P. AguilarUniversidad Nacional de San JuanArgentina

Fabián E. Pérez-YauliEscuela Politécnica NacionalQuitoEcuador

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Pablo X. VerdugoOperador Nacional de Electricidad(CENACE)QuitoEcuador

Aharon B. De La TorreOperador Nacional de Electricidad(CENACE)QuitoEcuador

Diego E. EcheverríaOperador Nacional de Electricidad(CENACE)QuitoEcuador

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Foreword

Over the last decades, the electrical power system has gone through a fundamentaltransformation never seen before. The liberalisation of the power industry that setthe whole process in motion has opened up the possibility of electricity trading acrossutility and even national boundaries. The distance between where power is generatedand where the final consumption takes place and with it the power transit throughthe high voltage transmission lines has increased immensely. A further developmentcompounding the competitive electricity market and power transmission over longdistances has been the large-scale installation of renewables-based power generationunits. In addition to the volatility and stochasticity of the power outputs of theseunits, utilities now also have to contend with possible bi-directional power flows in thedistribution networks.

Due to the different dynamic characteristics of renewable generation units comparedwith conventional power plants, the increasing share of renewables-based generationcapacity in the system can give rise to new dynamic phenomena that can reduce theexisting security of the whole system. Additionally, restrictions regarding expansion orreinforcement of the existing network mean that lines have to be loaded up to or neartheir maximum current carrying capabilities. It can thus be safely concluded that theincreasing uncertainty regarding load flows and the use of power plants in a heavilyloaded network, together with the new power generation technologies such as windand solar as well as transmission technologies such as VSC-HVDC, would necessarilylead to the reduction of existing security levels unless appropriate countermeasures areimplemented.

This book takes up this most up-to-date topic and provides valuable contributions inthe areas of both vulnerability assessment and intelligent control. The use of many ofthe methods under discussion has been made possible by the powerful computers andcommunication technologies that are now available. Also, in the last decade, significantadvances in the area of computational intelligence have been made. These results arenow mature enough for use in the planning and operation of power systems. During acontingency, for example, the operator is often overwhelmed by the rapidly changing sit-uation and the associated flood of information, on the basis of which appropriate stepshave to be taken. Clearly, the dispatcher cannot be expected to form an objective judg-ment on the unfolding situation based on his/her observation and experiences alone.The uncertainties must be assessed by suitable analytical tools in order to make the bestpossible decision within the shortest time possible, and computer-based decision sup-port systems come in handy here. Other promising techniques in this context are the

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model based predictive control approaches. If a contingency or unfavourable operatingcondition is predicted some time ahead of its occurrence, a suitable countermeasurecan be devised over the intervening period taking prior experience into account. Also,since the available time for decision and control actions is typically very short, real-timeapplications are required.

The current challenges, and particularly those ahead in the upcoming years, urgentlyrequire the introduction of new methods and approaches to ensure the preservationof the existing level of system security, which is taken for granted and assumed sofar to be self-evident. The approaches described in this book grew out of the work oftalented and committed young scientists working in the area. On the one hand, thecontributions serve as a thought-provoking impulse for practising engineers who arelooking for new ways to cope with the challenges of today and the future. However,many of these forward-looking ideas are already ready for implementation. On theother hand, this book also allows graduate students to get an overview of modernmathematical and computational methods. Certainly, the book presupposes a thoroughknowledge of power system analysis, dynamics and control. Building on this, however,it introduces the reader to an exciting world of new approaches. The combination ofpractice-orientation and introduction of modern methods for vulnerability assessmentand control applications make this book particularly valuable, and recommendedreading for a wide audience in the area of power engineering.

January 2017 Prof. István ErlichChair Professor of Department of ElectricalEngineering and Information TechnologiesHead of the Institute of Electrical Power SystemsUniversity Duisburg-Essen

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Preface

Traditionally, electrical power systems worldwide have been planned and operated ina relatively conservative manner, in which power system security, in terms of stability(i.e. dynamic performance under disturbances), has not been considered a major issue.Most of the tools developed and applied for these tasks were conceived to deal withreduced levels of uncertainty and have proven to be helpful to identify optimal develop-mental and operational strategies that ensure maximum net techno-economic benefits,in which only the fulfilment of steady-state performance constraints has been tackled.

The societal ambition of a cleaner, sustainable and affordable electrical energy supplyis motivating a dramatic change in the infrastructure of transmission and distributionsystems in order to catch up with the rapid and massive addition of evolving technologiesfor power generation based on renewable energy sources, particularly wind and solarphotovoltaics. In addition to this, the emergence of the prosumer figure and new inter-active business schemes entail operations within a heterogeneous and rapidly evolvingmarket environment.

In view of this, power system security, and especially the analysis of vulnerabilityand possible mitigation measures against disturbances, deserves special attention, sinceplanning and operating the electric power system of the future will involve dealing witha large volume of uncertainties that are reflected in highly variable operating conditionsand will eventually lead to unprecedented events.

This book covers the fundamentals and application of recently developed method-ologies for assessment and enhancement of power system security in short-termoperational planning (e.g. intra-day, day-ahead, a week ahead, and monthly timehorizons) and real-time operation. The methodologies are based on advanced datamining, probabilistic theory and computational intelligence algorithms, in order toprovide knowledge-based support for monitoring, control and protection tasks. Eachchapter of the book provides a thorough introduction to the intriguing mathematicsbehind each methodology as well as a sound discussion on its application to a specificcase study, which addresses different aspects of power system steady-state and dynamicsecurity.

In order to properly follow the content of the book, the reader is expected to have abasic background in power system analysis (e.g. power flow and fault calculation), powersystem stability (e.g. stability phenomena and modelling needs), and basics of controltheory (e.g. Fourier transforms, linear systems). This background is usually acquired ingraduate programs in electrical engineering and dedicated training courses and semi-nars. Therefore, the book is recommended for formal instruction, via advanced courses,

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of postgraduate students as well as for specialists working in power system operationand planning in industry. The content of the book is organised into two parts as follows:

Part I: Dynamic Vulnerability Assessment

Chapter 1 provides general definitions and rationale behind power system vulnerabilityassessment and phasor measurement technology, with special emphasis on the funda-mental relationship between these concepts as seen in modern control centres.

Chapter 2 addresses power system reliability management and provides a broaddiscussion on the challenges for reliability management due to uncertainties in differenttime frames, ranging from long-term system development to short-term systemoperation.

Chapter 3 concerns the fundamentals of probabilistic reliability analysis, with empha-sis on the study of large transmission networks. Two common approaches are presented:enumeration and Monte Carlo simulation. The chapter also provides a comprehensivestudy of the impact of underground cables on the Dutch extra-high-voltage (EHV) trans-mission network.

Chapter 4 introduces an enhanced data processing method based on theHilbert–Huang Transform technology for studying low-frequency power systemoscillations. Application to a real case study in Japan is overviewed and discussed.

Chapter 5 concerns the application of Monte Carlo simulation to recreate a statisticaldatabase of power system dynamic behaviour, followed by empirical orthogonal func-tions to approximate the dynamic vulnerability regions and a support vector classifierfor online post-contingency dynamic vulnerability status prediction. The tuning of theclassifier via a mean–variance mapping optimisation algorithm is also outlined.

Chapter 6 addresses the challenge of real-time vulnerability assessment. It introducesthe notion of real-time coherency identification and vulnerability symptoms, for bothfast and slow dynamic phenomena, and their identification from PMU data based onkey performance indicators and clustering techniques.

Chapter 7 focuses on the security constrained optimal power flow problem, discussingthe challenges and proposed solutions to leverage the computational effort in light of themore frequent use of risk-based security assessment and criteria for massive integrationof renewable generation and the associated volumes of uncertainty.

Chapter 8 presents the various reliability management actions (preventive and correc-tive) as well as their modelling and integration into a security constrained optimal powerflow problem. The different actions are represented by using a suitable linearized formu-lation, which allows keeping the computational costs low while retaining a sufficientlyaccurate approximation of the behaviour of the system.

PART II: Intelligent Control

Chapter 9 is devoted to damping control to mitigate oscillatory stability threats by usingmodel-based predictive control. This is an emerging method that is receiving increasinginterest in the control and power engineering community for the design of adaptiveand coordinated control schemes. In this chapter, a hierarchical model-based predictive

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control scheme is proposed to calculate supplementary signals that are superimposedon the inputs of the damping controllers that are usually attached to different devicessuch as synchronous generators and FACTS devices.

Chapter 10 introduces a combined approach of an artificial neural network and antcolony optimisation to provide a fast estimation of voltage stability margin and to definethe necessary adjustments of set-points of controllable reactive power sources based onvoltage stability constrained optimal power flow.

Chapter 11 presents a control scheme for voltage and power control in high-voltagemulti-terminal DC grids used for the grid connection of large offshore wind powerplants. The proposed control scheme employs a computational intelligence techniquein the form of a fuzzy controller for primary voltage control and a genetic algorithm forthe secondary control level.

Chapter 12 concerns the application of model-based predictive control for reactivepower control to adjust power system voltages during normal (i.e. quasi-steady state)conditions. This kind of control scheme has a slow response from, say, 10 to 60 sec-onds, to small operational changes and does not provide any fast reaction during largedisturbances to prevent undesirable adverse implications.

Chapter 13 proposes an optimisation approach in which the objective function isaugmented to incorporate the global optimisation of a linearized large scale multi-agentpower system using the Lagrangian decomposition algorithm. The aim is to maintaincentralised coordination among agents via a master agent leaving loss minimizationas the only distributed optimisation, which is analysed while protecting the localsensitive data.

Chapter 14 presents a basic formulation of model-based predictive control for volt-age corrective control, as well as the management of congestion and thermal overloadsin distribution networks in the presence of high penetration of distributed generationunits.

Chapter 15 addresses the interplay between transmission and distribution networksfrom the point of view of long-term voltage stability. It introduces the notion of Volt-VarControl (VVC) and the application of model-based predictive control for coordinationof reactive power support between distribution and transmission.

Chapter 16 overviews an approach for power system controlled islanding. Theapproach is based on the development and integration of novel algorithms andprocedures for graph partitioning and frequency behaviour estimation. It helps inavoiding a system collapse by splitting the system into electrical islands with adequategeneration-load balance.

Chapter 17 provides insight into the application and value of empirical orthogonalfunctions as a promising alternative for signal processing applied to fault diagnosis. Acomprehensive case study evidences that fault signals decomposed in terms of theseorthogonal basis functions exhibit well-defined patterns, which can be used for recog-nising the main features of fault events such as inception angle, fault type and faultlocation.

Chapter 18 presents the main developmental aspects and lessons learnt so far con-cerning the implementation of a real phasor based vulnerability assessment and controlscheme in the Ecuadorian National Interconnected System.

The book has intentionally been designed to allow some overlap between thechapters; it is desired to illustrate how some of the presented approaches could share

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some common elements, implementations or even developments and applications,despite being conceived for different purposes and uses.

We hope that the book proves to be a useful source of information on the understatingof dynamic vulnerability assessment and intelligent control, but at the same time pro-vides the basis for discussion among readers with diverse expertise and backgrounds.Given the great variety of topics covered in the book, which could not be completelycovered in a single edition, it is expected that a second edition of the book will be madeavailable soon.

José Luis Rueda-Torres, Delft University of Technology, The Netherlands.Francisco González-Longatt, Loughborough University, UK.

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Introduction: The Role of Wide Area Monitoring Systemsin Dynamic Vulnerability AssessmentJaime C. Cepeda1 and José Luis Rueda-Torres2

1 Head of Research and Development and University Professor, Technical Development Department and Electrical EnergyDepartment, Operador Nacional de Electricidad CENACE, and Escuela Politécnica Nacional EPN, Quito Ecuador2 Assistant professor of Intelligent Electrical Power Systems, Department of Electrical Sustainable Energy, Delft Universityof Technology, The Netherlands

1.1 Introduction

Currently, most social, political, and economic activities depend on the reliability ofseveral energy infrastructures. This fact has established the necessity of improving thesecurity and robustness of Electric Power Systems [1]. In addition, the lack of invest-ment, the use of congested transmission lines, and other technical reasons, such asenvironmental constraints, have been pushing Bulk Power Systems dangerously close totheir physical limits [2]. Under these conditions, certain sudden perturbations can causecascading events that may lead to system blackouts [1, 3]. It is crucial to ensure that theseperturbations do not affect security, so the development of protection systems that guar-antee service continuity is required. In this regard, Special Protection Schemes (SPS)are designed in order to detect abnormal conditions and carry out corrective actionsthat mitigate possible consequences and allow an acceptable system performance [4].

However, the conditions that lead the system to a blackout are not easy to identifybecause the process of system collapse depends on multiple interactions [5, 6]. Vulner-ability assessment (VA) is carried out by checking the system performance under theseverest contingencies with the purpose of detecting the conditions that might initi-ate cascading failures and may provoke system collapse [7]. A vulnerable system is asystem that operates with a “reduced level of security that renders it vulnerable to thecumulative effects of a series of moderate disturbances” [7]. The concept of vulnerabilityinvolves a system’s security level (static and dynamic security) and the tendency of itsconditions changing to a critical state [8] that is called the “Verge of Collapse State” [5].In this context, vulnerability assessment assumes the function of detecting the necessityof performing global control actions (e.g., triggering of SPSs).

In recent years, emerging technologies such as Phasor Measurement Units (PMUs),which provide voltage and current phasor measurements with updating periods of a fewmilliseconds, have allowed the development of modern approaches that come closer tothe target of real time vulnerability assessment [6, 7]. Most of these real time applicationshave been focused on identifying signals that suggest a possibly insecure steady state.

Dynamic Vulnerability Assessment and Intelligent Control for Sustainable Power Systems, First Edition.Edited by José Luis Rueda-Torres and Francisco González-Longatt.© 2018 John Wiley & Sons Ltd. Published 2018 by John Wiley & Sons Ltd.

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2 Dynamic Vulnerability Assessment

This kind of VA is capable of alerting the operator to take appropriate countermeasures,with the goal of bringing the system to a more secure operating condition (i.e., preventivecontrol) [9]. Nevertheless, the use of PMUs has great potential to allow the performanceof post-contingency Dynamic Vulnerability Assessment (DVA) that could be used totrigger SPSs in order to implement corrective control actions. In this connection, a WideArea Monitoring System (WAMS), based on synchrophasor technology, constitutes thebasic infrastructure for implementing a comprehensive scheme for carrying out realtime DVA and afterwards executing real time protection and control actions. This com-prehensive scheme is called a Wide Area Monitoring, Protection, and Control system(WAMPAC) [10]. This chapter presents a general overview of concepts related to powersystem vulnerability assessment and phasor measurement technology and subsequentlyhighlights the fundamental relationship between these in modern control centers.

1.2 Power System Vulnerability

A vulnerable system is a system that operates with a “reduced level of security thatrenders it vulnerable to the cumulative effects of a series of moderate disturbances.”Vulnerability is a measure of system weakness regarding the occurrence of cascadingevents [7].

The concept of vulnerability involves a system’s security level (i.e., static and dynamicsecurity) and its tendency to change its conditions to a critical state [8] that is called the“Verge of Collapse State” [5].

A vulnerable area is a specific section of the system where vulnerability begins todevelop. The occurrence of an abnormal contingency in vulnerable areas and a highlystressed operating condition define a system in the verge of collapse state [5].

In this chapter, vulnerability is defined as “the risk level presented by a power systemduring a specific static or dynamic operating condition regarding the occurrence ofcascading events.” This concept makes vulnerability an essential indicator of systemcollapse proximity.

Although there are a lot of vulnerability causes, which vary from natural disasters tohuman failures, system vulnerability is characterized by four different symptoms of sys-tem stress: angle instability, voltage instability, frequency instability, and overloads [5].So, vulnerability assessment should be performed through analyzing the system statusas regards these symptoms of system stress.

1.2.1 Vulnerability Assessment

Vulnerability assessment has the objective of preventing the occurrence of collapsesdue to catastrophic perturbations [11]. Performing VA requires specific mathematicalmodels capable of analyzing the multiple interactions taking place between the differentpower system components [11]. These models have to consider the varied phenomenainvolved in the vulnerability condition and also the diverse timeframes in which thecorresponding phenomena occur.

Many methods have been proposed for vulnerability assessment, which have beenclassified based on various criteria [6, 7]. However, in terms of their potential imple-mentation in control centers, the techniques to assess vulnerability can be classified into

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Power SystemVulnerability Assessment

Off-line Assessment

Post mortemanalysis

On-line Assessment Real-time Assessment

Pre-contingencyAI and data mining

Post-contingencybased on PMU

HPC techniques

Advancedvisualization

Steady-statesimulation

Dynamicsimulation

Probabilisticmodels

Figure 1.1 Power system vulnerability assessment methods.

off-line, on-line, and real time methods. Figure 1.1 depicts the proposed classificationof vulnerability assessment methods.

• Off-line assessment: Off-line assessment is done using conventional methods that arebased on different complex model simulations; these usually involve time-consumingtasks, which restricts on-line applications. The high complexity is provoked by thehuge number and diversity of the components that constitute an electric power sys-tem and their particular performance during dynamic phenomena. Among this classof methods are those that assess vulnerability via power flow computations. Theseapproaches are based on the hypothesis that a typical contingency provokes minorchanges in the Bulk Power Systems, so they migrate from one quasi-steady state toanother. The simulations do not include any dynamic response analysis [7]. This typeof evaluation is commonly called Static Security Assessment (SSA). As a complementof SSA, dynamic simulation is commonly used in order to consider all the static anddynamic power system components, simulating any type of contingency and any typeof instability [9, 12]. In this case, the complexity of the modeling and mathematicalcomputations demands high processing time [7, 9]. This evaluation is usually calledDynamic Security Assessment (DSA). Usually, both in SSA and DSA, several “what-if”contingencies are simulated in order to determine the most critical disturbances (i.e.,N-x contingency analysis) [9].

• On-line assessment: In an on-line assessment, the information must be available inthe SCADA/EMS (Supervisory Control and Data Acquisition/Energy ManagementSystem). The input data are updated using adequate tools and equipment (e.g., IEDs,PMUs, WAMS), but the output is not necessarily obtained as quickly as the real timeevents occur [12].

• Real time assessment: The input data reflect the most recent picture of the system con-ditions in a real time analysis, and the entire process is performed within very shorttime, typically not exceeding a couple of seconds [12]. Emerging technologies, such

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4 Dynamic Vulnerability Assessment

as Phasor Measurement Units (PMUs), have allowed the development of modernapproaches to vulnerability assessment [7, 12] capable of being updated practically inreal time. Additionally, some novel mathematical methods permit processing the dataobtained in real time through identification of indicators or patterns that show sys-tem vulnerability, using artificial intelligence (AI) or modern data mining techniques[7]. Tools based on AI allow the analysis of power system dynamic performancepatterns in real time, using the knowledge obtained from off-line learning [7]. On theother hand, data mining techniques permit uncovering valuable hidden informationimmersed in the electric signals [7], which can exhibit certain regularities (patterns)signaling a possibly vulnerable condition [10, 13]. VA methods based on AI and datamining are oriented to evaluate both pre-contingency quasi-steady state data (i.e.,real time DSA for coordinate preventive control actions) as well as post-contingencydynamic data (i.e., post-contingency Dynamic Vulnerability Assessment—DVA—fortriggering corrective control actions). In the particular case of post-contingencyDVA, it is noteworthy that PMUs are able to provide time-synchronized phasor data,which contain valuable dynamic information that could indicate system vulnerabilitystatus and potential collapses [7]. These post-contingency data offer a new frame-work for system vulnerability assessment that is known as Dynamic VulnerabilityAssessment (DVA), which is oriented to coordinating corrective control actions.In this connection, post-contingency DVA requires even a quicker response thanpre-contingency DSA, so that AI and data mining techniques are, possibly, the mostprominent mathematical tools to be applied for accomplishing this type of real timeassessment.

1.2.2 Timescale of Power System Actions and Operations

As mentioned, an important aspect to be considered in vulnerability assessmentis the duration of the events involved in the VA timeframe of interest. Due to thecomplex tasks related to power system operation, which comprise modeling, analysis,simulation, and control actions, the timescale varies from microseconds to severalhours [1]. Table 1.1 presents some power system actions and operations and theircorresponding timeframes. Most of this book tackles the Dynamic Vulnerability

Table 1.1 Actions and operations within the power system [1].

Action or operation TimeframeElectromagnetic transients μs – msSwitching overvoltage MsFault protection 100 msElectromagnetic effects in machine windings ms – sElectromechanical transients – stability ms – sElectromechanical oscillations ms – minFrequency control 1 s – 10 sOverloads 5 s – hEconomic load dispatch 10 s – 1 hThermodynamic effects s – hEnergy Management System applications Steady state; ongoing

DVA timeframe