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<p>List of Contributors xv</p> <p>Foreword xix</p> <p>Preface xxi</p> <p>1 Introduction: The Role of Wide Area Monitoring Systems in Dynamic Vulnerability Assessment 1</p> <p>Jaime C. Cepeda and José Luis Rueda-Torres</p> <p>1.1 Introduction 1</p> <p>1.2 Power System Vulnerability 2</p> <p>1.2.1 Vulnerability Assessment 2</p> <p>1.2.2 Timescale of Power System Actions and Operations 4</p> <p>1.3 Power System Vulnerability Symptoms 5</p> <p>1.3.1 Rotor Angle Stability 6</p> <p>1.3.2 Short-Term Voltage Stability 7</p> <p>1.3.3 Short-Term Frequency Stability 7</p> <p>1.3.4 Post-Contingency Overloads 7</p> <p>1.4 Synchronized Phasor Measurement Technology 8</p> <p>1.4.1 Phasor Representation of Sinusoids 8</p> <p>1.4.2 Synchronized Phasors 9</p> <p>1.4.3 Phasor Measurement Units (PMUs) 9</p> <p>1.4.4 Discrete Fourier Transform and Phasor Calculation 10</p> <p>1.4.5 Wide Area Monitoring Systems 10</p> <p>1.4.6 WAMPAC Communication Time Delay 12</p> <p>1.5 The Fundamental Role of WAMS in Dynamic Vulnerability Assessment 13</p> <p>1.6 Concluding Remarks 16</p> <p>2 Steady-state Security 21</p> <p>Evelyn Heylen, Steven De Boeck, Marten Ovaere, Hakan Ergun, and Dirk Van Hertem</p> <p>2.1 Power System Reliability Management: A Combination of Reliability Assessment and Reliability Control 22</p> <p>2.1.1 Reliability Assessment 23</p> <p>2.1.2 Reliability Control 24</p> <p>2.2 Reliability Under Various Timeframes 31</p> <p>2.3 Reliability Criteria 33</p> <p>2.4 Reliability and Its Cost as a Function of Uncertainty 34</p> <p>2.4.1 Reliability Costs 34</p> <p>2.4.2 Interruption Costs 35</p> <p>2.4.3 Minimizing the Sum of Reliability and Interruption Costs 36</p> <p>3 Probabilistic Indicators for the Assessment of Reliability and Security of Future Power Systems 41</p> <p>Bart W. Tuinema, Nikoleta Kandalepa, and José Luis Rueda-Torres</p> <p>3.1 Introduction 41</p> <p>3.2 Time Horizons in the Planning and Operation of Power Systems 42</p> <p>3.2.1 Time Horizons 42</p> <p>3.2.2 Overlapping and Interaction 42</p> <p>3.2.3 Remedial Actions 42</p> <p>3.3 Reliability Indicators 45</p> <p>3.3.1 Security-of-Supply Related Indicators 45</p> <p>3.3.2 Additional Indicators 47</p> <p>3.4 Reliability Analysis 49</p> <p>3.4.1 Input Information 49</p> <p>3.4.2 Pre-calculations 50</p> <p>3.4.3 Reliability Analysis 50</p> <p>3.4.4 Output: Reliability Indicators 53</p> <p>3.5 Application Example: EHV Underground Cables 53</p> <p>3.5.1 Input Parameters 54</p> <p>3.5.2 Results of Analysis 56</p> <p>4 An Enhanced WAMS-based Power System Oscillation Analysis Approach 63</p> <p>Qing Liu, Hassan Bevrani, and Yasunori Mitani</p> <p>4.1 Introduction 63</p> <p>4.2 HHT Method 65</p> <p>4.2.1 EMD 65</p> <p>4.2.2 Hilbert Transform 65</p> <p>4.2.3 Hilbert Spectrum and Hilbert Marginal Spectrum 66</p> <p>4.2.4 HHT Issues 67</p> <p>4.3 The Enhanced HHT Method 71</p> <p>4.3.1 Data Pre-treatment Processing 71</p> <p>4.3.2 Inhibiting the Boundary End Effect 75</p> <p>4.3.3 Parameter Identification 80</p> <p>4.4 Enhanced HHT Method Evaluation 81</p> <p>4.4.1 Case I 81</p> <p>4.4.2 Case II 84</p> <p>4.4.3 Case III 85</p> <p>4.5 Application to RealWide Area Measurements 88</p> <p>5 Pattern Recognition-Based Approach for Dynamic Vulnerability Status Prediction 95</p> <p>Jaime C. Cepeda, José Luis Rueda-Torres, Delia G. Colomé, and István Erlich</p> <p>5.1 Introduction 95</p> <p>5.2 Post-contingency Dynamic Vulnerability Regions 96</p> <p>5.3 Recognition of Post-contingency DVRs 97</p> <p>5.3.1 N-1 Contingency Monte Carlo Simulation 98</p> <p>5.3.2 Post-contingency Pattern Recognition Method 100</p> <p>5.3.3 Definition of Data-TimeWindows 103</p> <p>5.3.4 Identification of Post-contingency DVRs—Case Study 104</p> <p>5.4 Real-Time Vulnerability Status Prediction 109</p> <p>5.4.1 Support Vector Classifier (SVC) Training 112</p> <p>5.4.2 SVC Real-Time Implementation 113</p> <p>5.5 Concluding Remarks 115</p> <p>6 Performance Indicator-Based Real-Time Vulnerability Assessment 119</p> <p>Jaime C. Cepeda, José Luis Rueda-Torres, Delia G. Colomé, and István Erlich</p> <p>6.1 Introduction 119</p> <p>6.2 Overview of the Proposed Vulnerability Assessment Methodology 120</p> <p>6.3 Real-Time Area Coherency Identification 122</p> <p>6.3.1 Associated PMU Coherent Areas 122</p> <p>6.4 TVFS Vulnerability Performance Indicators 125</p> <p>6.4.1 Transient Stability Index (TSI) 125</p> <p>6.4.2 Voltage Deviation Index (VDI) 128</p> <p>6.4.3 Frequency Deviation Index (FDI) 131</p> <p>6.4.4 Assessment of TVFS Security Level for the Illustrative Examples 131</p> <p>6.4.5 Complete TVFS Real-Time Vulnerability Assessment 133</p> <p>6.5 Slower Phenomena Vulnerability Performance Indicators 137</p> <p>6.5.1 Oscillatory Index (OSI) 137</p> <p>6.5.2 Overload Index (OVI) 141</p> <p>6.6 Concluding Remarks 145</p> <p>7 Challenges Ahead Risk-Based AC Optimal Power Flow Under Uncertainty for Smart Sustainable Power Systems 149</p> <p>Florin Capitanescu</p> <p>7.1 Chapter Overview 149</p> <p>7.2 Conventional (Deterministic) AC Optimal Power Flow (OPF) 150</p> <p>7.2.1 Introduction 150</p> <p>7.2.2 Abstract Mathematical Formulation of the OPF Problem 150</p> <p>7.2.3 OPF Solution via Interior-Point Method 151</p> <p>7.2.4 Illustrative Example 154</p> <p>7.3 Risk-Based OPF 158</p> <p>7.3.1 Motivation and Principle 158</p> <p>7.3.2 Risk-Based OPF Problem Formulation 159</p> <p>7.3.3 Illustrative Example 160</p> <p>7.4 OPF Under Uncertainty 162</p> <p>7.4.1 Motivation and Potential Approaches 162</p> <p>7.4.2 Robust Optimization Framework 162</p> <p>7.4.3 Methodology for Solving the R-OPF Problem 163</p> <p>7.4.4 Illustrative Example 164</p> <p>7.5 Advanced Issues and Outlook 169</p> <p>7.5.1 Conventional OPF 169</p> <p>7.5.2 Beyond the Scope of Conventional OPF: Risk, Uncertainty, Smarter Sustainable Grid 172</p> <p>8 Modeling Preventive and Corrective Actions Using Linear Formulation 177</p> <p>Tom Van Acker and Dirk Van Hertem</p> <p>8.1 Introduction 177</p> <p>8.2 Security Constrained OPF 178</p> <p>8.3 Available Control Actions in AC Power Systems 178</p> <p>8.3.1 Generator Redispatch 179</p> <p>8.3.2 Load Shedding and Demand Side Management 179</p> <p>8.3.3 Phase Shifting Transformer 179</p> <p>8.3.4 Switching Actions 180</p> <p>8.3.5 Reactive Power Management 180</p> <p>8.3.6 Special Protection Schemes 180</p> <p>8.4 Linear Implementation of Control Actions in a SCOPF Environment 180</p> <p>8.4.1 Generator Redispatch 181</p> <p>8.4.2 Load Shedding and Demand Side Management 182</p> <p>8.4.3 Phase Shifting Transformer 183</p> <p>8.4.4 Switching 184</p> <p>8.5 Case Study of Preventive and Corrective Actions 185</p> <p>8.5.1 Case Study 1: Generator Redispatch and Load Shedding (CS1) 186</p> <p>8.5.2 Case Study 2: Generator Redispatch, Load Shedding and PST (CS2) 187</p> <p>8.5.3 Case Study 3: Generator Redispatch, Load Shedding and Switching (CS3) 190</p> <p>9 Model-based Predictive Control for Damping Electromechanical Oscillations in Power Systems 193</p> <p>DaWang</p> <p>9.1 Introduction 193</p> <p>9.2 MPC BasicTheory & Damping Controller Models 194</p> <p>9.2.1 What is MPC? 194</p> <p>9.2.2 Damping Controller Models 196</p> <p>9.3 MPC for Damping Oscillations 198</p> <p>9.3.1 Outline of Idea 198</p> <p>9.3.2 Mathematical Formulation 199</p> <p>9.3.3 Proposed Control Schemes 200</p> <p>9.4 Test System & Simulation Setting 204</p> <p>9.5 Performance Analysis of MPC Schemes 204</p> <p>9.5.1 Centralized MPC 204</p> <p>9.5.2 Distributed MPC 209</p> <p>9.5.3 Hierarchical MPC 209</p> <p>9.6 Conclusions and Discussions 213</p> <p>10 Voltage Stability Enhancement by Computational Intelligence Methods 217</p> <p>Worawat Nakawiro</p> <p>10.1 Introduction 217</p> <p>10.2 Theoretical Background 218</p> <p>10.2.1 Voltage Stability Assessment 218</p> <p>10.2.2 Sensitivity Analysis 219</p> <p>10.2.3 Optimal Power Flow 220</p> <p>10.2.4 Artificial Neural Network 220</p> <p>10.2.5 Ant Colony Optimisation 221</p> <p>10.3 Test Power System 223</p> <p>10.4 Example 1: Preventive Measure 224</p> <p>10.4.1 Problem Statement 224</p> <p>10.4.2 Simulation Results 225</p> <p>10.5 Example 2: Corrective Measure 226</p> <p>10.5.1 Problem Statement 226</p> <p>10.5.2 Simulation Results 227</p> <p>11 Knowledge-Based Primary and Optimization-Based Secondary Control of Multi-terminal HVDCGrids 233</p> <p>Adedotun J. Agbemuko, Mario Ndreko, Marjan Popov, José Luis Rueda-Torres, and Mart A.M.M van der Meijden</p> <p>11.1 Introduction 234</p> <p>11.2 Conventional Control Schemes in HV-MTDC Grids 234</p> <p>11.3 Principles of Fuzzy-Based Control 236</p> <p>11.4 Implementation of the Knowledge-Based Power-Voltage Droop Control Strategy 236</p> <p>11.4.1 Control Scheme for Primary and Secondary Power-Voltage Control 237</p> <p>11.4.2 Input/Output Variables 238</p> <p>11.4.3 Knowledge Base and Inference Engine 241</p> <p>11.4.4 Defuzzification and Output 241</p> <p>11.5 Optimization-Based Secondary Control Strategy 242</p> <p>11.5.1 Fitness Function 242</p> <p>11.5.2 Constraints 244</p> <p>11.6 Simulation Results 245</p> <p>11.6.1 Set Point Change 245</p> <p>11.6.2 Constantly Changing Reference Set Points 246</p> <p>11.6.3 Sudden Disconnection ofWind Farm for Undefined Period 246</p> <p>11.6.4 Permanent Outage of VSC 3 247</p> <p>12 Model Based Voltage/Reactive Control in Sustainable Distribution Systems 251</p> <p>Hoan Van Pham and Sultan Nasiruddin Ahmed</p> <p>12.1 Introduction 251</p> <p>12.2 BackgroundTheory 252</p> <p>12.2.1 Voltage Control 252</p> <p>12.2.2 Model Predictive Control 253</p> <p>12.2.3 Model Analysis 255</p> <p>12.2.4 Implementation 257</p> <p>12.3 MPC Based Voltage/Reactive Controller – an Example 258</p> <p>12.3.1 Control Scheme 258</p> <p>12.3.2 Overall Objective Function of the MPC Based Controller 259</p> <p>12.3.3 Implementation of the MPC Based Controller 261</p> <p>12.4 Test Results 262</p> <p>12.4.1 Test System and Measurement Deployment 262</p> <p>12.4.2 Parameter Setup and Algorithm Selection for the Controller 263</p> <p>12.4.3 Results and Discussion 263</p> <p>12.5 Conclusions 266</p> <p>13 Multi-Agent based Approach for Intelligent Control of Reactive Power Injection in Transmission Systems 269</p> <p>Hoan Van Pham and Sultan Nasiruddin Ahmed</p> <p>13.1 Introduction 269</p> <p>13.2 System Model and Problem Formulation 270</p> <p>13.3 Multi-Agent Based Approach 275</p> <p>13.3.1 Augmented Lagrange Formulation 275</p> <p>13.3.2 Implementation Algorithm 275</p> <p>13.4 Case Studies and Simulation Results 277</p> <p>13.4.1 Case Studies 277</p> <p>13.4.2 Simulation Results 277</p> <p>14 Operation of Distribution SystemsWithin Secure Limits Using Real-Time Model Predictive Control 283</p> <p>Hamid Soleimani Bidgoli, Gustavo Valverde, Petros Aristidou, Mevludin Glavic, and Thierry Van Cutsem</p> <p>14.1 Introduction 283</p> <p>14.2 Basic MPC Principles 285</p> <p>14.3 Control Problem Formulation 285</p> <p>14.4 Voltage CorrectionWith Minimum Control Effort 288</p> <p>14.4.1 Inclusion of LTC Actions as Known Disturbances 289</p> <p>14.4.2 Problem Formulation 290</p> <p>14.5 Correction of Voltages and Congestion Management with Minimum Deviation from References 291</p> <p>14.5.4 Problem Formulation 295</p> <p>14.6 Test System 296</p> <p>14.7 Simulation Results: Voltage Correction with Minimal Control Effort 298</p> <p>14.8 Simulation Results: Voltage and/or Congestion Corrections with Minimum Deviation from Reference 302</p> <p>15 Enhancement of Transmission System Voltage Stability through Local Control of Distribution Networks 311</p> <p>Gustavo Valverde, Petros Aristidou, and Thierry Van Cutsem</p> <p>15.1 Introduction 311</p> <p>15.2 Long-Term Voltage Stability 313</p> <p>15.2.1 Countermeasures 314</p> <p>15.3 Impact of Volt-VAR Control on Long-Term Voltage Stability 316</p> <p>15.3.1 Countermeasures 318</p> <p>15.4 Test System Description 319</p> <p>15.4.1 Test System 319</p> <p>15.4.2 VVC Algorithm 321</p> <p>15.4.3 Emergency Detection 322</p> <p>15.5 Case Studies and Simulation Results 323</p> <p>15.5.1 Results in Stable Scenarios 323</p> <p>15.5.2 Results in Unstable Scenarios 326</p> <p>15.5.3 Results with Emergency Support From Distribution 328</p> <p>16 Electric Power Network Splitting Considering Frequency Dynamics and Transmission Overloading Constraints 337</p> <p>Nelson Granda and Delia G. Colomé</p> <p>16.1 Introduction 337</p> <p>16.1.1 Stage One: Vulnerability Assessment 337</p> <p>16.1.2 Stage Two: Islanding Process 338</p> <p>16.2 Network Splitting Mechanism 340</p> <p>16.2.1 Graph Modeling, Update, and Reduction 341</p> <p>16.2.2 Graph Partitioning Procedure 342</p> <p>16.2.3 Load Shedding/Generation Tripping Schemes 343</p> <p>16.2.4 Tie-Lines Determination 344</p> <p>16.3 Power Imbalance Constraint Limits 344</p> <p>16.3.1 Reduced Frequency ResponseModel 345</p> <p>16.3.2 Power Imbalance Constraint Limits Determination 347</p> <p>16.4 Overload Assessment and Control 348</p> <p>16.5 Test Results 349</p> <p>16.5.1 Power System Collapse 349</p> <p>16.5.2 Application of Proposed Methodology 351</p> <p>16.5.3 Performance of Proposed ACIS 354</p> <p>16.6 Conclusions and Recommendations 356</p> <p>17 High-Speed Transmission Line Protection Based on Empirical Orthogonal Functions 361</p> <p>Rommel P. Aguilar and Fabián E. Pérez-Yauli</p> <p>17.1 Introduction 361</p> <p>17.2 Empirical Orthogonal Functions 363</p> <p>17.2.1 Formulation 363</p> <p>17.3 Applications of EOFs for Transmission Line Protection 365</p> <p>17.3.1 Fault Direction 366</p> <p>17.3.2 Fault Classification 367</p> <p>17.3.3 Fault Location 369</p> <p>17.4 Study Case 369</p> <p>17.4.1 Transmission Line Model and Simulation 369</p> <p>17.4.2 The Power System and Transmission Line 370</p> <p>17.4.3 Training Data 370</p> <p>17.4.4 Training Data Matrix 370</p> <p>17.4.5 Signal Conditioning 373</p> <p>17.4.6 Energy Patterns 373</p> <p>17.4.7 EOF Analysis 376</p> <p>17.4.8 Evaluation of the Protection Scheme 379</p> <p>17.4.9 Fault Classification 380</p> <p>17.4.10 Fault Location 382</p> <p>17.5 Conclusions 383</p> <p>Study Cases:WECC 9-bus, ATPDrawModels and Parameters 384</p> <p>18 Implementation of a Real Phasor Based Vulnerability Assessment and Control Scheme: The Ecuadorian WAMPAC System 389</p> <p>Pablo X. Verdugo, Jaime C. Cepeda, Aharon B. De La Torre, and Diego E. Echeverría</p> <p>18.1 Introduction 389</p> <p>18.2 PMU Location in the Ecuadorian SNI 390</p> <p>18.3 Steady-State Angle Stability 391</p> <p>18.4 Steady-State Voltage Stability 395</p> <p>18.5 Oscillatory Stability 398</p> <p>18.5.1 Power System Stabilizer Tuning 402</p> <p>18.6 Ecuadorian Special Protection Scheme (SPS) 407</p> <p>18.6.1 SPS Operation Analysis 409</p> <p>18.7 Concluding Remarks 410</p> <p>Index 413</p>

<p> <strong>Edited by </strong> <p><strong>José Luis Rueda-Torres</strong> received the Electrical Engineer Diploma from Escuela Politécnica Nacional, Quito, Ecuador, cum laude honors, in August 2004. In November 2009, he received a Ph.D. in electrical engineering from the National University of San Juan, obtaining the highest mark 'Sobresaliente' (Outstanding). He is currently working as an Assistant Professor for Intelligent Electrical Power Grids at the Department of Electrical Sustainable Energy, Technical University Delft, Netherlands. He is vice-chair of the Working Group on Modern Heuristic Optimization (WGMHO) under the IEEE PES Power System Analysis, Computing, and Economics Committee. Dr. Rueda-Torres is a member of CIGRE and a senior member of the IEEE. His current research interests include power system planning, power system stability and control, and probabilistic and artificial intelligence methods. <p><strong>Francisco González-Longatt </strong>received an Electrical Engineering degree from Instituto Universitario Politécnico de la Fuerza Armada Nacional (1994), Master of Business Administration from Universidad Bicentenaria de Aragua (1999), a Ph.D. in Electrical Power Engineering from the Universidad Central de Venezuela (2008), and a Postgraduate Certificate in Higher Education Professional Practice from Coventry University (2013). He is a Lecturer in Electrical Power Systems in the School of Electronic, Electrical and Systems Engineering at Loughborough University, UK, and the Vice-President of the Venezuelan Wind Energy Association. Dr González-Longatt is a member of CIGRE and a senior member of the IEEE. His current research interests include innovative (operation/control) schemes to optimize the performance of future energy systems.

<p> Identifying, assessing, and mitigating electric power grid vulnerabilities is a growing focus in short-term operational planning of power systems. Through illustrated application, this important guide surveys state-of-the-art methodologies for the assessment and enhancement of power system security in short-term operational planning and real-time operation. The methodologies employ advanced methods from probabilistic theory, data mining, artificial intelligence, and optimization, to provide knowledge-based support for monitoring, control (preventive and corrective), and decision making tasks. <p> <strong>Key features:</strong> <ul> <li>Introduces behavioural recognition in wide-area monitoring and security constrained optimal power flow for intelligent control and protection and optimal grid management.</li> <li>Provides in-depth understanding of risk-based reliability and security assessment, dynamic vulnerability assessment methods, supported by the underpinning mathematics.</li> <li>Develops expertise in mitigation techniques using intelligent protection and control, controlled islanding, model predictive control, and multi-agent and distributed control systems.</li> <li>Illustrates implementation in smart grid and self-healing applications with examples and real-world experience from the WAMPAC (Wide Area Monitoring Protection and Control) scheme.</li> </ul> <br> <p> <em>Dynamic Vulnerability Assessment and Intelligent Control for Sustainable Power Systems</em> is a valuable reference for postgraduate students and researchers in power system stability as well as practicing engineers working in power system dynamics, control, and network operation and planning.

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