Chapter
1.3 Power System Vulnerability Symptoms
1.3.1 Rotor Angle Stability
1.3.1.1 Transient Stability
1.3.1.2 Oscillatory Stability
1.3.2 Short‐Term Voltage Stability
1.3.3 Short‐Term Frequency Stability
1.3.4 Post‐Contingency Overloads
1.4 Synchronized Phasor Measurement Technology
1.4.1 Phasor Representation of Sinusoids
1.4.2 Synchronized Phasors
1.4.3 Phasor Measurement Units (PMUs)
1.4.4 Discrete Fourier Transform and Phasor Calculation
1.4.5 Wide Area Monitoring Systems
1.4.6 WAMPAC Communication Time Delay
1.5 The Fundamental Role of WAMS in Dynamic Vulnerability Assessment
Chapter 2 Steady‐State Security
2.1 Power System Reliability Management: A Combination of Reliability Assessment and Reliability Control
2.1.1 Reliability Assessment
2.1.2 Reliability Control
2.1.2.1 Credible and Non‐Credible Contingencies
2.1.2.2 Operating State of the Power System
2.1.2.3 System State Space Representation
2.2 Reliability Under Various Timeframes
2.4 Reliability and Its Cost as a Function of Uncertainty
2.4.3 Minimizing the Sum of Reliability and Interruption Costs
Chapter 3 Probabilistic Indicators for the Assessment of Reliability and Security of Future Power Systems
3.2 Time Horizons in the Planning and Operation of Power Systems
3.2.2 Overlapping and Interaction
3.3 Reliability Indicators
3.3.1 Security‐of‐Supply Related Indicators
3.3.2 Additional Indicators
3.4.3 Reliability Analysis
3.4.4 Output: Reliability Indicators
3.5 Application Example: EHV Underground Cables
3.5.2 Results of Analysis
Chapter 4 An Enhanced WAMS‐based Power System Oscillation Analysis Approach
4.2.3 Hilbert Spectrum and Hilbert Marginal Spectrum
4.2.4.1 The Boundary End Effect
4.2.4.2 Mode Mixing and Pseudo‐IMF Component
4.2.4.3 Parameter Identification
4.3 The Enhanced HHT Method
4.3.1 Data Pre‐treatment Processing
4.3.1.1 DC Removal Processing
4.3.1.2 Digital Band‐Pass Filter Algorithm
4.3.2 Inhibiting the Boundary End Effect
4.3.2.1 The Boundary End Effect Caused by the EMD Algorithm
4.3.2.2 Inhibiting the Boundary End Effects Caused by the EMD
4.3.2.3 The Boundary End Effect Caused by the Hilbert Transform
4.3.2.4 Inhibiting the Boundary End Effect Caused by the HT
4.3.3 Parameter Identification
4.4 Enhanced HHT Method Evaluation
4.5 Application to Real Wide Area Measurements
Chapter 5 Pattern Recognition‐Based Approach for Dynamic Vulnerability Status Prediction
5.2 Post‐contingency Dynamic Vulnerability Regions
5.3 Recognition of Post‐contingency DVRs
5.3.1 N‐1 Contingency Monte Carlo Simulation
5.3.2 Post‐contingency Pattern Recognition Method
5.3.3 Definition of Data‐Time Windows
5.3.4 Identification of Post‐contingency DVRs—Case Study
5.4 Real‐Time Vulnerability Status Prediction
5.4.1 Support Vector Classifier (SVC) Training
5.4.2 SVC Real‐Time Implementation
Chapter 6 Performance Indicator‐Based Real‐Time Vulnerability Assessment
6.2 Overview of the Proposed Vulnerability Assessment Methodology
6.3 Real‐Time Area Coherency Identification
6.3.1 Associated PMU Coherent Areas
6.4 TVFS Vulnerability Performance Indicators
6.4.1 Transient Stability Index (TSI)
6.4.2 Voltage Deviation Index (VDI)
6.4.3 Frequency Deviation Index (FDI)
6.4.4 Assessment of TVFS Security Level for the Illustrative Examples
6.4.5 Complete TVFS Real‐Time Vulnerability Assessment
6.5 Slower Phenomena Vulnerability Performance Indicators
6.5.1 Oscillatory Index (OSI)
6.5.2 Overload Index (OVI)
Chapter 7 Challenges Ahead Risk‐Based AC Optimal Power Flow Under Uncertainty for Smart Sustainable Power Systems
7.2 Conventional (Deterministic) AC Optimal Power Flow (OPF)
7.2.2 Abstract Mathematical Formulation of the OPF Problem
7.2.3 OPF Solution via Interior‐Point Method
7.2.3.1 Obtaining the Optimality Conditions In IPM
7.2.3.2 The Basic Primal Dual Algorithm
7.2.4 Illustrative Example
7.2.4.1 Description of the Test System
7.2.4.2 Detailed Formulation of the OPF Problem
7.2.4.3 Analysis of Various Operating Modes
7.2.4.4 Iterative OPF Methodology
7.3.1 Motivation and Principle
7.3.2 Risk‐Based OPF Problem Formulation
7.3.3 Illustrative Example
7.3.3.1 Detailed Formulation of the RB‐OPF Problem
7.3.3.2 Numerical Results
7.4 OPF Under Uncertainty
7.4.1 Motivation and Potential Approaches
7.4.2 Robust Optimization Framework
7.4.3 Methodology for Solving the R‐OPF Problem
7.4.4 Illustrative Example
7.4.4.1 Detailed Formulation of the Worst Uncertainty Pattern Computation With Respect to a Contingency
7.4.4.2 Detailed Formulation of the OPF to Check Feasibility in the Presence of Corrective Actions
7.4.4.3 Detailed Formulation of the R‐OPF Relaxation
7.4.4.4 Numerical Results
7.5 Advanced Issues and Outlook
7.5.1.1 Overall OPF Solution Methodology
7.5.1.2 Core Optimizers: Classical Methods Versus Convex Relaxations
7.5.2 Beyond the Scope of Conventional OPF: Risk, Uncertainty, Smarter Sustainable Grid
Chapter 8 Modeling Preventive and Corrective Actions Using Linear Formulation
8.2 Security Constrained OPF
8.3 Available Control Actions in AC Power Systems
8.3.1 Generator Redispatch
8.3.2 Load Shedding and Demand Side Management
8.3.3 Phase Shifting Transformer
8.3.5 Reactive Power Management
8.3.6 Special Protection Schemes
8.4 Linear Implementation of Control Actions in a SCOPF Environment
8.4.1 Generator Redispatch
8.4.2 Load Shedding and Demand Side Management
8.4.3 Phase Shifting Transformer
8.5 Case Study of Preventive and Corrective Actions
8.5.1 Case Study 1: Generator Redispatch and Load Shedding (CS1)
8.5.2 Case Study 2: Generator Redispatch, Load Shedding and PST (CS2)
8.5.3 Case Study 3: Generator Redispatch, Load Shedding and Switching (CS3)
Chapter 9 Model‐based Predictive Control for Damping Electromechanical Oscillations in Power Systems
9.2 MPC Basic Theory & Damping Controller Models
9.2.2 Damping Controller Models
9.3 MPC for Damping Oscillations
9.3.2 Mathematical Formulation
9.3.3 Proposed Control Schemes
9.3.3.2 Decentralized MPC
9.4 Test System & Simulation Setting
9.5 Performance Analysis of MPC Schemes
9.5.1.1 Basic Results in Ideal Conditions
9.5.1.2 Results Considering State Estimation Errors
9.5.1.3 Consideration of Control Delays
9.6 Conclusions and Discussions
Chapter 10 Voltage Stability Enhancement by Computational Intelligence Methods
10.2 Theoretical Background
10.2.1 Voltage Stability Assessment
10.2.2 Sensitivity Analysis
10.2.3 Optimal Power Flow
10.2.4 Artificial Neural Network
10.2.5 Ant Colony Optimisation
10.4 Example 1: Preventive Measure
10.4.2 Simulation Results
10.5 Example 2: Corrective Measure
10.5.2 Simulation Results
Chapter 11 Knowledge‐Based Primary and Optimization‐Based Secondary Control of Multi‐terminal HVDC Grids
11.2 Conventional Control Schemes in HV‐MTDC Grids
11.3 Principles of Fuzzy‐Based Control
11.4 Implementation of the Knowledge‐Based Power‐Voltage Droop Control Strategy
11.4.1 Control Scheme for Primary and Secondary Power‐Voltage Control
11.4.2 Input/Output Variables
11.4.2.1 Membership Functions and Linguistic Terms
11.4.3 Knowledge Base and Inference Engine
11.4.4 Defuzzification and Output
11.5 Optimization‐Based Secondary Control Strategy
11.6.2 Constantly Changing Reference Set Points
11.6.3 Sudden Disconnection of Wind Farm for Undefined Period
11.6.4 Permanent Outage of VSC 3
Chapter 12 Model Based Voltage/Reactive Control in Sustainable Distribution Systems
12.2.2 Model Predictive Control
12.2.3.1 Definition of Sensitivity
12.2.3.2 Computation of Sensitivity
12.3 MPC Based Voltage/Reactive Controller – an Example
12.3.2 Overall Objective Function of the MPC Based Controller
12.3.3 Implementation of the MPC Based Controller
12.4.1 Test System and Measurement Deployment
12.4.2 Parameter Setup and Algorithm Selection for the Controller
12.4.3 Results and Discussion
12.4.3.1 Loss Minimization Performance of the Controller
12.4.3.2 Voltage Correction Performance of the Controller
Chapter 13 Multi‐Agent based Approach for Intelligent Control of Reactive Power Injection in Transmission Systems
13.2 System Model and Problem Formulation
13.2.1 Power System Model
13.2.2 Optimal Reactive Control Problem Formulation
13.2.3 Multi‐Agent Sensitivity Model
13.2.3.1 Calculation of the First Layer
13.2.3.2 Calculation of the Second Layer
13.3 Multi‐Agent Based Approach
13.3.1 Augmented Lagrange Formulation
13.3.2 Implementation Algorithm
13.4 Case Studies and Simulation Results
13.4.2 Simulation Results
13.4.2.1 Performance Comparison Between Multi‐Agent Based and Single‐Agent Based System
13.4.2.2 Impacts of General Parameters on the Proposed Control Scheme's Performance
13.4.2.3 Impacts of Multi‐Agent Parameters on the Proposed Control Scheme's Performance
Chapter 14 Operation of Distribution Systems Within Secure Limits Using Real‐Time Model Predictive Control
14.2 Basic MPC Principles
14.3 Control Problem Formulation
14.4 Voltage Correction With Minimum Control Effort
14.4.1 Inclusion of LTC Actions as Known Disturbances
14.4.2 Problem Formulation
14.5 Correction of Voltages and Congestion Management with Minimum Deviation from References
14.5.4 Problem Formulation
14.7 Simulation Results: Voltage Correction with Minimal Control Effort
14.8 Simulation Results: Voltage and/or Congestion Corrections with Minimum Deviation from Reference
14.8.1 Scenario C: Mode 1
14.8.2 Scenario D: Modes 1 and 2 Combined
14.8.3 Scenario E: Modes 1 and 3 Combined
Chapter 15 Enhancement of Transmission System Voltage Stability through Local Control of Distribution Networks
15.2 Long‐Term Voltage Stability
15.3 Impact of Volt‐VAR Control on Long‐Term Voltage Stability
15.4 Test System Description
15.4.3 Emergency Detection
15.5 Case Studies and Simulation Results
15.5.1 Results in Stable Scenarios
15.5.2 Results in Unstable Scenarios
15.5.3 Results with Emergency Support From Distribution
Chapter 16 Electric Power Network Splitting Considering Frequency Dynamics and Transmission Overloading Constraints
16.1.1 Stage One: Vulnerability Assessment
16.1.2 Stage Two: Islanding Process
16.2 Network Splitting Mechanism
16.2.1 Graph Modeling, Update, and Reduction
16.2.2 Graph Partitioning Procedure
16.2.3 Load Shedding/Generation Tripping Schemes
16.2.4 Tie‐Lines Determination
16.3 Power Imbalance Constraint Limits
16.3.1 Reduced Frequency Response Model
16.3.2 Power Imbalance Constraint Limits Determination
16.4 Overload Assessment and Control
16.5.1 Power System Collapse
16.5.2 Application of Proposed Methodology
16.5.3 Performance of Proposed ACIS
16.6 Conclusions and Recommendations
Chapter 17 High‐Speed Transmission Line Protection Based on Empirical Orthogonal Functions
17.2 Empirical Orthogonal Functions
17.3 Applications of EOFs for Transmission Line Protection
17.3.2 Fault Classification
17.3.2.2 Fault Type Surfaces
17.3.2.3 Defining the Fault Type
17.4.1 Transmission Line Model and Simulation
17.4.2 The Power System and Transmission Line
17.4.4 Training Data Matrix
17.4.4.2 Sampling Frequency
17.4.5 Signal Conditioning
17.4.5.1 Superimposed Component
17.4.5.2 Centering the Variables
17.4.7.1 Computing the EOFs
17.4.7.2 Fault Patterns Using EOF
17.4.8 Evaluation of the Protection Scheme
17.4.9 Fault Classification
Appendix 17.A Study Cases: WECC 9‐bus, ATPDraw Models and Parameters
Chapter 18 Implementation of a Real Phasor Based Vulnerability Assessment and Control Scheme: The Ecuadorian WAMPAC System
18.2 PMU Location in the Ecuadorian SNI
18.3 Steady‐State Angle Stability
18.4 Steady‐State Voltage Stability
18.5 Oscillatory Stability
18.5.1 Power System Stabilizer Tuning
18.6 Ecuadorian Special Protection Scheme (SPS)
18.6.1 SPS Operation Analysis
Dynamic Vulnerability Assessment and Intelligent Control
：For Sustainable Power Systems
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