Dynamic Vulnerability Assessment and Intelligent Control
Description
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.
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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.
Content
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 DVRs-Case 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
Jaime C. Cepeda, José Luis Rueda-Torres, Delia G. Colomé, and István Erlich
6.1 Introduction 119
6.2 Overview of the Proposed Vulnerability Assessment Methodology 120
6.3 Real-Time Area Coherency Identification 122
6.3.1 Associated PMU Coherent Areas 122
6.4 TVFS Vulnerability Performance Indicators 125
6.4.1 Transient Stability Index (TSI) 125
6.4.2 Voltage Deviation Index (VDI) 128
6.4.3 Frequency Deviation Index (FDI) 131
6.4.4 Assessment of TVFS Security Level for the Illustrative Examples 131
6.4.5 Complete TVFS Real-Time Vulnerability Assessment 133
6.5 Slower Phenomena Vulnerability Performance Indicators 137
6.5.1 Oscillatory Index (OSI) 137
6.5.2 Overload Index (OVI) 141
6.6 Concluding Remarks 145
7 Challenges Ahead Risk-Based AC Optimal Power Flow Under Uncertainty for Smart Sustainable Power Systems 149
Florin Capitanescu
7.1 Chapter Overview 149
7.2 Conventional (Deterministic) AC Optimal Power Flow (OPF) 150
7.2.1 Introduction 150
7.2.2 Abstract Mathematical Formulation of the OPF Problem 150
7.2.3 OPF Solution via Interior-Point Method 151
7.2.4 Illustrative Example 154
7.3 Risk-Based OPF 158
7.3.1 Motivation and Principle 158
7.3.2 Risk-Based OPF Problem Formulation 159
7.3.3 Illustrative Example 160
7.4 OPF Under Uncertainty 162
7.4.1 Motivation and Potential Approaches 162
7.4.2 Robust Optimization Framework 162
7.4.3 Methodology for Solving the R-OPF Problem 163
7.4.4 Illustrative Example 164
7.5 Advanced Issues and Outlook 169
7.5.1 Conventional OPF 169
7.5.2 Beyond the Scope of Conventional OPF: Risk, Uncertainty, Smarter Sustainable Grid 172
8 Modeling Preventive and Corrective Actions Using Linear Formulation 177
Tom Van Acker and Dirk Van Hertem
8.1 Introduction 177
8.2 Security Constrained OPF 178
8.3 Available Control Actions in AC Power Systems 178
8.3.1 Generator Redispatch 179
8.3.2 Load Shedding and Demand Side Management 179
8.3.3 Phase Shifting Transformer 179
8.3.4 Switching Actions 180
8.3.5 Reactive Power Management 180
8.3.6 Special Protection Schemes 180
8.4 Linear Implementation of Control Actions in a SCOPF Environment 180
8.4.1 Generator Redispatch 181
8.4.2 Load Shedding and Demand Side Management 182
8.4.3 Phase Shifting Transformer 183
8.4.4 Switching 184
8.5 Case Study of Preventive and Corrective Actions 185
8.5.1 Case Study 1: Generator Redispatch and Load Shedding (CS1) 186
8.5.2 Case Study 2: Generator Redispatch, Load Shedding and PST (CS2) 187
8.5.3 Case Study 3: Generator Redispatch, Load Shedding and Switching (CS3) 190
9 Model-based Predictive Control for Damping Electromechanical Oscillations in Power Systems 193
DaWang
9.1 Introduction 193
9.2 MPC BasicTheory & Damping Controller Models 194
9.2.1 What is MPC? 194
9.2.2 Damping Controller Models 196
9.3 MPC for Damping Oscillations 198
9.3.1 Outline of Idea 198
9.3.2 Mathematical Formulation 199
9.3.3 Proposed Control Schemes 200
9.4 Test System & Simulation Setting 204
9.5 Performance Analysis of MPC Schemes 204
9.5.1 Centralized MPC 204
9.5.2 Distributed MPC 209
9.5.3 Hierarchical MPC 209
9.6 Conclusions and Discussions 213
10 Voltage Stability Enhancement by Computational Intelligence Methods 217
Worawat Nakawiro
10.1 Introduction 217
10.2 Theoretical Background 218
10.2.1 Voltage Stability Assessment 218
10.2.2 Sensitivity Analysis 219
10.2.3 Optimal Power Flow 220
10.2.4 Artificial Neural Network 220
10.2.5 Ant Colony Optimisation 221
10.3 Test Power System 223
10.4 Example 1: Preventive Measure 224
10.4.1 Problem Statement 224
10.4.2 Simulation Results 225
10.5 Example 2: Corrective Measure 226
10.5.1 Problem Statement 226
10.5.2 Simulation Results 227
11 Knowledge-Based Primary and Optimization-Based Secondary Control of Multi-terminal HVDCGrids 233
Adedotun J. Agbemuko, Mario Ndreko, Marjan Popov, José Luis Rueda-Torres, and Mart A.M.M van der Meijden
11.1 Introduction 234
11.2 Conventional Control Schemes in HV-MTDC Grids 234
11.3 Principles of Fuzzy-Based Control 236
11.4 Implementation of the Knowledge-Based Power-Voltage Droop Control Strategy 236
11.4.1 Control Scheme for Primary and Secondary Power-Voltage Control 237
11.4.2 Input/Output Variables 238
11.4.3 Knowledge Base and Inference Engine 241
11.4.4 Defuzzification and Output 241
11.5 Optimization-Based Secondary Control Strategy 242
11.5.1 Fitness Function 242
11.5.2 Constraints 244
11.6 Simulation Results 245
11.6.1 Set Point Change 245
11.6.2 Constantly Changing Reference Set Points 246
11.6.3 Sudden Disconnection ofWind Farm for Undefined Period 246
11.6.4 Permanent Outage of VSC 3 247
12 Model Based Voltage/Reactive Control in Sustainable Distribution Systems 251
Hoan Van Pham and Sultan Nasiruddin Ahmed
12.1 Introduction 251
12.2 BackgroundTheory 252
12.2.1 Voltage Control 252
12.2.2 Model Predictive Control 253
12.2.3 Model Analysis 255
12.2.4 Implementation 257
12.3 MPC Based Voltage/Reactive Controller - an Example 258
12.3.1 Control Scheme 258
12.3.2 Overall Objective Function of the MPC Based Controller 259
12.3.3 Implementation of the MPC Based Controller 261
12.4 Test Results 262
12.4.1 Test System and Measurement Deployment 262
12.4.2 Parameter Setup and Algorithm Selection for the Controller 263
12.4.3 Results and Discussion 263
12.5 Conclusions 266
13 Multi-Agent based Approach for Intelligent Control of Reactive Power Injection in Transmission Systems 269
Hoan Van Pham and Sultan Nasiruddin Ahmed
13.1 Introduction 269
13.2 System Model and Problem Formulation 270
13.3 Multi-Agent Based Approach 275
13.3.1 Augmented Lagrange Formulation 275
13.3.2 Implementation Algorithm 275
13.4 Case Studies and Simulation Results 277
13.4.1 Case Studies 277
13.4.2 Simulation Results 277
14 Operation of Distribution SystemsWithin Secure Limits Using Real-Time Model Predictive Control 283
Hamid Soleimani Bidgoli, Gustavo Valverde, Petros Aristidou, Mevludin Glavic, and Thierry Van Cutsem
14.1 Introduction 283
14.2 Basic MPC Principles 285
14.3 Control Problem Formulation 285
14.4 Voltage CorrectionWith Minimum Control Effort 288
14.4.1 Inclusion of LTC Actions as Known Disturbances 289
14.4.2 Problem Formulation 290
14.5 Correction of Voltages and Congestion Management with Minimum Deviation from References 291
14.5.4 Problem Formulation 295
14.6 Test System 296
14.7 Simulation Results: Voltage Correction with Minimal Control Effort 298
14.8 Simulation Results: Voltage and/or Congestion Corrections with Minimum Deviation from Reference 302
15 Enhancement of Transmission System Voltage Stability through Local Control of Distribution Networks 311
Gustavo Valverde, Petros Aristidou, and Thierry Van Cutsem
15.1 Introduction 311
15.2 Long-Term Voltage Stability 313
15.2.1 Countermeasures 314
15.3 Impact of Volt-VAR Control on Long-Term Voltage Stability 316
15.3.1 Countermeasures 318
15.4 Test System Description 319
15.4.1 Test System 319
15.4.2 VVC Algorithm 321
15.4.3 Emergency Detection 322
15.5 Case Studies and Simulation Results 323
15.5.1 Results in Stable Scenarios 323
15.5.2 Results in Unstable Scenarios 326
15.5.3 Results with Emergency Support From Distribution 328
16 Electric Power Network Splitting Considering Frequency Dynamics and Transmission Overloading Constraints 337
Nelson Granda and Delia G. Colomé
16.1 Introduction 337
16.1.1 Stage One: Vulnerability Assessment 337
16.1.2 Stage Two: Islanding Process 338
16.2 Network Splitting Mechanism 340
16.2.1 Graph Modeling, Update, and Reduction 341
16.2.2 Graph Partitioning Procedure 342
16.2.3 Load Shedding/Generation Tripping Schemes 343
16.2.4 Tie-Lines Determination 344
16.3 Power Imbalance Constraint Limits 344
16.3.1 Reduced Frequency ResponseModel 345
16.3.2 Power Imbalance Constraint Limits Determination 347
16.4 Overload Assessment and Control 348
16.5 Test Results 349
16.5.1 Power System Collapse 349
16.5.2 Application of Proposed Methodology 351
16.5.3 Performance of Proposed ACIS 354
16.6 Conclusions and Recommendations 356
17 High-Speed Transmission Line Protection Based on Empirical Orthogonal Functions 361
Rommel P. Aguilar and Fabián E. Pérez-Yauli
17.1 Introduction 361
17.2 Empirical Orthogonal Functions 363
17.2.1 Formulation 363
17.3 Applications of EOFs for Transmission Line Protection 365
17.3.1 Fault Direction 366
17.3.2 Fault Classification 367
17.3.3 Fault Location 369
17.4 Study Case 369
17.4.1 Transmission Line Model and Simulation 369
17.4.2 The Power System and Transmission Line 370
17.4.3 Training Data 370
17.4.4 Training Data Matrix 370
17.4.5 Signal Conditioning 373
17.4.6 Energy Patterns 373
17.4.7 EOF Analysis 376
17.4.8 Evaluation of the Protection Scheme 379
17.4.9 Fault Classification 380
17.4.10 Fault Location 382
17.5 Conclusions 383
Study Cases:WECC 9-bus, ATPDrawModels and Parameters 384
18 Implementation of a Real Phasor Based Vulnerability Assessment and Control Scheme: The Ecuadorian WAMPAC System 389
Pablo X. Verdugo, Jaime C. Cepeda, Aharon B. De La Torre, and Diego E. Echeverría
18.1 Introduction 389
18.2 PMU Location in the Ecuadorian SNI 390
18.3 Steady-State Angle Stability 391
18.4 Steady-State Voltage Stability 395
18.5 Oscillatory Stability 398
18.5.1 Power System Stabilizer Tuning 402
18.6 Ecuadorian Special Protection Scheme (SPS) 407
18.6.1 SPS Operation Analysis 409
18.7 Concluding Remarks 410
Index 413
Preface
Traditionally, electrical power systems worldwide have been planned and operated in a 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 with reduced levels of uncertainty and have proven to be helpful to identify optimal developmental 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 supply is motivating a dramatic change in the infrastructure of transmission and distribution systems in order to catch up with the rapid and massive addition of evolving technologies for power generation based on renewable energy sources, particularly wind and solar photovoltaics. In addition to this, the emergence of the prosumer figure and new interactive business schemes entail operations within a heterogeneous and rapidly evolving market environment.
In view of this, power system security, and especially the analysis of vulnerability and possible mitigation measures against disturbances, deserves special attention, since planning and operating the electric power system of the future will involve dealing with a large volume of uncertainties that are reflected in highly variable operating conditions and will eventually lead to unprecedented events.
This book covers the fundamentals and application of recently developed methodologies for assessment and enhancement of power system security in short-term operational planning (e.g. intra-day, day-ahead, a week ahead, and monthly time horizons) and real-time operation. The methodologies are based on advanced data mining, probabilistic theory and computational intelligence algorithms, in order to provide knowledge-based support for monitoring, control and protection tasks. Each chapter of the book provides a thorough introduction to the intriguing mathematics behind each methodology as well as a sound discussion on its application to a specific case study, which addresses different aspects of power system steady-state and dynamic security.
In order to properly follow the content of the book, the reader is expected to have a basic background in power system analysis (e.g. power flow and fault calculation), power system stability (e.g. stability phenomena and modelling needs), and basics of control theory (e.g. Fourier transforms, linear systems). This background is usually acquired in graduate programs in electrical engineering and dedicated training courses and seminars. Therefore, the book is recommended for formal instruction, via advanced courses, of postgraduate students as well as for specialists working in power system operation and 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 vulnerability assessment and phasor measurement technology, with special emphasis on the fundamental relationship between these concepts as seen in modern control centres.
Chapter 2 addresses power system reliability management and provides a broad discussion on the challenges for reliability management due to uncertainties in different time frames, ranging from long-term system development to short-term system operation.
Chapter 3 concerns the fundamentals of probabilistic reliability analysis, with emphasis on the study of large transmission networks. Two common approaches are presented: enumeration and Monte Carlo simulation. The chapter also provides a comprehensive study of the impact of underground cables on the Dutch extra-high-voltage (EHV) transmission network.
Chapter 4 introduces an enhanced data processing method based on the Hilbert-Huang Transform technology for studying low-frequency power system oscillations. Application to a real case study in Japan is overviewed and discussed.
Chapter 5 concerns the application of Monte Carlo simulation to recreate a statistical database of power system dynamic behaviour, followed by empirical orthogonal functions to approximate the dynamic vulnerability regions and a support vector classifier for online post-contingency dynamic vulnerability status prediction. The tuning of the classifier via a mean-variance mapping optimisation algorithm is also outlined.
Chapter 6 addresses the challenge of real-time vulnerability assessment. It introduces the notion of real-time coherency identification and vulnerability symptoms, for both fast and slow dynamic phenomena, and their identification from PMU data based on key performance indicators and clustering techniques.
Chapter 7 focuses on the security constrained optimal power flow problem, discussing the challenges and proposed solutions to leverage the computational effort in light of the more frequent use of risk-based security assessment and criteria for massive integration of renewable generation and the associated volumes of uncertainty.
Chapter 8 presents the various reliability management actions (preventive and corrective) as well as their modelling and integration into a security constrained optimal power flow problem. The different actions are represented by using a suitable linearized formulation, which allows keeping the computational costs low while retaining a sufficiently accurate approximation of the behaviour of the system.
PART II: Intelligent Control
Chapter 9 is devoted to damping control to mitigate oscillatory stability threats by using model-based predictive control. This is an emerging method that is receiving increasing interest in the control and power engineering community for the design of adaptive and coordinated control schemes. In this chapter, a hierarchical model-based predictive control scheme is proposed to calculate supplementary signals that are superimposed on the inputs of the damping controllers that are usually attached to different devices such as synchronous generators and FACTS devices.
Chapter 10 introduces a combined approach of an artificial neural network and ant colony optimisation to provide a fast estimation of voltage stability margin and to define the necessary adjustments of set-points of controllable reactive power sources based on voltage stability constrained optimal power flow.
Chapter 11 presents a control scheme for voltage and power control in high-voltage multi-terminal DC grids used for the grid connection of large offshore wind power plants. The proposed control scheme employs a computational intelligence technique in the form of a fuzzy controller for primary voltage control and a genetic algorithm for the secondary control level.
Chapter 12 concerns the application of model-based predictive control for reactive power 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 seconds, to small operational changes and does not provide any fast reaction during large disturbances to prevent undesirable adverse implications.
Chapter 13 proposes an optimisation approach in which the objective function is augmented to incorporate the global optimisation of a linearized large scale multi-agent power system using the Lagrangian decomposition algorithm. The aim is to maintain centralised coordination among agents via a master agent leaving loss minimization as the only distributed optimisation, which is analysed while protecting the local sensitive data.
Chapter 14 presents a basic formulation of model-based predictive control for voltage corrective control, as well as the management of congestion and thermal overloads in distribution networks in the presence of high penetration of distributed generation units.
Chapter 15 addresses the interplay between transmission and distribution networks from the point of view of long-term voltage stability. It introduces the notion of Volt-Var Control (VVC) and the application of model-based predictive control for coordination of reactive power support between distribution and transmission.
Chapter 16 overviews an approach for power system controlled islanding. The approach is based on the development and integration of novel algorithms and procedures for graph partitioning and frequency behaviour estimation. It helps in avoiding a system collapse by splitting the system into electrical islands with adequate generation-load balance.
Chapter 17 provides insight into the application and value of empirical orthogonal functions as a promising alternative for signal processing applied to fault diagnosis. A comprehensive case study evidences that fault signals decomposed in terms of these orthogonal basis functions exhibit well-defined patterns, which can be used for recognising the main features of fault events such as inception angle, fault type and fault location.
Chapter 18 presents the main developmental aspects and lessons learnt so far concerning the implementation of a real phasor based vulnerability assessment and control scheme in the Ecuadorian National Interconnected System.
The book has intentionally been designed to allow some overlap between the chapters; it is desired to illustrate how some of the presented approaches could share some common elements, implementations or even developments and applications, despite being conceived for different purposes and...
