Dynamic Vulnerability Assessment and Intelligent Control

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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. Key features: Introduces behavioural recognition in wide-area monitoring and security constrained optimal power flow for intelligent control and protection and optimal grid management. Provides in-depth understanding of risk-based reliability and security assessment, dynamic vulnerability assessment methods, supported by the underpinning mathematics. Develops expertise in mitigation techniques using intelligent protection and control, controlled islanding, model predictive control, multi-agent and distributed control systems 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. Dynamic Vulnerability Assessment and Intelligent Control for Power Systems 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.


Edited by

José Luis Rueda-Torres 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.

Francisco González-Longatt 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.


List of Contributors xv

Foreword xix

Preface xxi

1 Introduction: The Role of Wide Area Monitoring Systems in Dynamic Vulnerability Assessment 1

Jaime C. Cepeda and José Luis Rueda-Torres

1.1 Introduction 1

1.2 Power System Vulnerability 2

1.2.1 Vulnerability Assessment 2

1.2.2 Timescale of Power System Actions and Operations 4

1.3 Power System Vulnerability Symptoms 5

1.3.1 Rotor Angle Stability 6

1.3.2 Short-Term Voltage Stability 7

1.3.3 Short-Term Frequency Stability 7

1.3.4 Post-Contingency Overloads 7

1.4 Synchronized Phasor Measurement Technology 8

1.4.1 Phasor Representation of Sinusoids 8

1.4.2 Synchronized Phasors 9

1.4.3 Phasor Measurement Units (PMUs) 9

1.4.4 Discrete Fourier Transform and Phasor Calculation 10

1.4.5 Wide Area Monitoring Systems 10

1.4.6 WAMPAC Communication Time Delay 12

1.5 The Fundamental Role of WAMS in Dynamic Vulnerability Assessment 13

1.6 Concluding Remarks 16

2 Steady-state Security 21

Evelyn Heylen, Steven De Boeck, Marten Ovaere, Hakan Ergun, and Dirk Van Hertem

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

2.1.1 Reliability Assessment 23

2.1.2 Reliability Control 24

2.2 Reliability Under Various Timeframes 31

2.3 Reliability Criteria 33

2.4 Reliability and Its Cost as a Function of Uncertainty 34

2.4.1 Reliability Costs 34

2.4.2 Interruption Costs 35

2.4.3 Minimizing the Sum of Reliability and Interruption Costs 36

3 Probabilistic Indicators for the Assessment of Reliability and Security of Future Power Systems 41

Bart W. Tuinema, Nikoleta Kandalepa, and José Luis Rueda-Torres

3.1 Introduction 41

3.2 Time Horizons in the Planning and Operation of Power Systems 42

3.2.1 Time Horizons 42

3.2.2 Overlapping and Interaction 42

3.2.3 Remedial Actions 42

3.3 Reliability Indicators 45

3.3.1 Security-of-Supply Related Indicators 45

3.3.2 Additional Indicators 47

3.4 Reliability Analysis 49

3.4.1 Input Information 49

3.4.2 Pre-calculations 50

3.4.3 Reliability Analysis 50

3.4.4 Output: Reliability Indicators 53

3.5 Application Example: EHV Underground Cables 53

3.5.1 Input Parameters 54

3.5.2 Results of Analysis 56

4 An Enhanced WAMS-based Power System Oscillation Analysis Approach 63

Qing Liu, Hassan Bevrani, and Yasunori Mitani

4.1 Introduction 63

4.2 HHT Method 65

4.2.1 EMD 65

4.2.2 Hilbert Transform 65

4.2.3 Hilbert Spectrum and Hilbert Marginal Spectrum 66

4.2.4 HHT Issues 67

4.3 The Enhanced HHT Method 71

4.3.1 Data Pre-treatment Processing 71

4.3.2 Inhibiting the Boundary End Effect 75

4.3.3 Parameter Identification 80

4.4 Enhanced HHT Method Evaluation 81

4.4.1 Case I 81

4.4.2 Case II 84

4.4.3 Case III 85

4.5 Application to RealWide Area Measurements 88

5 Pattern Recognition-Based Approach for Dynamic Vulnerability Status Prediction 95

Jaime C. Cepeda, José Luis Rueda-Torres, Delia G. Colomé, and István Erlich

5.1 Introduction 95

5.2 Post-contingency Dynamic Vulnerability Regions 96

5.3 Recognition of Post-contingency DVRs 97

5.3.1 N-1 Contingency Monte Carlo Simulation 98

5.3.2 Post-contingency Pattern Recognition Method 100

5.3.3 Definition of Data-TimeWindows 103

5.3.4 Identification of Post-contingency DVRsCase Study 104

5.4 Real-Time Vulnerability Status Prediction 109

5.4.1 Support Vector Classifier (SVC) Training 112

5.4.2 SVC Real-Time Implementation 113

5.5 Concluding Remarks 115

6 Performance Indicator-Based Real-Time Vulnerability Assessment 119


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