Dynamic Vulnerability Assessment and Intelligent Control E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Wiley - IEEE
- Sprache: Englisch
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.
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Veröffentlichungsjahr: 2018
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Table of Contents
Cover
Title Page
Copyright
List of Contributors
Foreword
Preface
Part I: Dynamic Vulnerability Assessment
PART II: Intelligent Control
Chapter 1: Introduction: The Role of Wide Area Monitoring Systems in Dynamic Vulnerability Assessment
1.1 Introduction
1.2 Power System Vulnerability
1.3 Power System Vulnerability Symptoms
1.4 Synchronized Phasor Measurement Technology
1.5 The Fundamental Role of WAMS in Dynamic Vulnerability Assessment
1.6 Concluding Remarks
References
Chapter 2: Steady-State Security
2.1 Power System Reliability Management: A Combination of Reliability Assessment and Reliability Control
2.2 Reliability Under Various Timeframes
2.3 Reliability Criteria
2.4 Reliability and Its Cost as a Function of Uncertainty
2.5 Conclusion
References
Chapter 3: Probabilistic Indicators for the Assessment of Reliability and Security of Future Power Systems
3.1 Introduction
3.2 Time Horizons in the Planning and Operation of Power Systems
3.3 Reliability Indicators
3.4 Reliability Analysis
3.5 Application Example: EHV Underground Cables
3.6 Conclusions
References
Chapter 4: An Enhanced WAMS-based Power System Oscillation Analysis Approach
4.1 Introduction
4.2 HHT Method
4.3 The Enhanced HHT Method
4.4 Enhanced HHT Method Evaluation
4.5 Application to Real Wide Area Measurements
4.6 Summary
References
Chapter 5: Pattern Recognition-Based Approach for Dynamic Vulnerability Status Prediction
5.1 Introduction
5.2 Post-contingency Dynamic Vulnerability Regions
5.3 Recognition of Post-contingency DVRs
5.4 Real-Time Vulnerability Status Prediction
5.5 Concluding Remarks
References
Chapter 6: Performance Indicator-Based Real-Time Vulnerability Assessment
6.1 Introduction
6.2 Overview of the Proposed Vulnerability Assessment Methodology
6.3 Real-Time Area Coherency Identification
6.4 TVFS Vulnerability Performance Indicators
6.5 Slower Phenomena Vulnerability Performance Indicators
6.6 Concluding Remarks
References
Chapter 7: Challenges Ahead Risk-Based AC Optimal Power Flow Under Uncertainty for Smart Sustainable Power Systems
7.1 Chapter Overview
7.2 Conventional (Deterministic) AC Optimal Power Flow (OPF)
7.3 Risk-Based OPF
7.4 OPF Under Uncertainty
7.5 Advanced Issues and Outlook
References
Chapter 8: Modeling Preventive and Corrective Actions Using Linear Formulation
8.1 Introduction
8.2 Security Constrained OPF
8.3 Available Control Actions in AC Power Systems
8.4 Linear Implementation of Control Actions in a SCOPF Environment
8.5 Case Study of Preventive and Corrective Actions
8.6 Conclusions
References
Chapter 9: Model-based Predictive Control for Damping Electromechanical Oscillations in Power Systems
9.1 Introduction
9.2 MPC Basic Theory & Damping Controller Models
9.3 MPC for Damping Oscillations
9.4 Test System & Simulation Setting
9.5 Performance Analysis of MPC Schemes
9.6 Conclusions and Discussions
References
Chapter 10: Voltage Stability Enhancement by Computational Intelligence Methods
10.1 Introduction
10.2 Theoretical Background
10.3 Test Power System
10.4 Example 1: Preventive Measure
10.5 Example 2: Corrective Measure
10.6 Conclusions
References
Chapter 11: Smart Control of Offshore HVDC Grids
11.1 Introduction
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
11.5 Optimization-Based Secondary Control Strategy
11.6 Simulation Results
11.7 Conclusion
References
Chapter 12: Model Based Voltage/Reactive Control in Sustainable Distribution Systems
12.1 Introduction
12.2 Background Theory
12.3 MPC Based Voltage/Reactive Controller – an Example
12.4 Test Results
12.5 Conclusions
References
Chapter 13: Multi-Agent based Approach for Intelligent Control of Reactive Power Injection in Transmission Systems
13.1 Introduction
13.2 System Model and Problem Formulation
13.3 Multi-Agent Based Approach
13.4 Case Studies and Simulation Results
13.5 Conclusions
References
Chapter 14: Operation of Distribution Systems Within Secure Limits Using Real-Time Model Predictive Control
14.1 Introduction
14.2 Basic MPC Principles
14.3 Control Problem Formulation
14.4 Voltage Correction With Minimum Control Effort
14.5 Correction of Voltages and Congestion Management with Minimum Deviation from References
14.6 Test System
14.7 Simulation Results: Voltage Correction with Minimal Control Effort
14.8 Simulation Results: Voltage and/or Congestion Corrections with Minimum Deviation from Reference
14.9 Conclusion
References
Chapter 15: Local Control of Distribution Networks
15.1 Introduction
15.2 Long-Term Voltage Stability
15.3 Impact of Volt-VAR Control on Long-Term Voltage Stability
15.4 Test System Description
15.5 Case Studies and Simulation Results
15.6 Conclusion
References
Chapter 16: Electric Power Network Splitting Considering Frequency Dynamics and Transmission Overloading Constraints
16.1 Introduction
16.2 Network Splitting Mechanism
16.3 Power Imbalance Constraint Limits
16.4 Overload Assessment and Control
16.5 Test Results
16.6 Conclusions and Recommendations
References
Chapter 17: High-Speed Transmission Line Protection Based on Empirical Orthogonal Functions
17.1 Introduction
17.2 Empirical Orthogonal Functions
17.3 Applications of EOFs for Transmission Line Protection
17.4 Study Case
17.5 Conclusions
References
Chapter 18: Implementation of a Real Phasor Based Vulnerability Assessment and Control Scheme: The Ecuadorian WAMPAC System
18.1 Introduction
18.2 PMU Location in the Ecuadorian SNI
18.3 Steady-State Angle Stability
18.4 Steady-State Voltage Stability
18.5 Oscillatory Stability
18.6 Ecuadorian Special Protection Scheme (SPS)
18.7 Concluding Remarks
References
Index
End User License Agreement
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Guide
Cover
Table of Contents
Foreword
Preface
Begin Reading
List of Illustrations
Chapter 1: Introduction: The Role of Wide Area Monitoring Systems in Dynamic Vulnerability Assessment
Figure 1.1 Power system vulnerability assessment methods.
Figure 1.2 Phasor representation of sinusoids: (a) sinusoidal function, (b) phasor representation.
Figure 1.3 PMU basic structure [21].
Figure 1.4 Wide area monitoring, protection and control systems [2].
Figure 1.5 Scheme for integrated self-healing functionalities to support secure system operation in real time.
Chapter 2: Steady-State Security
Figure 2.1 Interactions between the aspects determining reliability of power systems.
Figure 2.2 Overview of reliability management.
Figure 2.3 Typical categorization of contingency ranking in continental Europe.
Figure 2.4 Power system in states.
Figure 2.5 State space representation of system states. (a) Limited uncertainty (b) Increased uncertainty.
Figure 2.6 Line outage in state space representation.
Figure 2.7 Generation outage in state space representation.
Figure 2.8 The uncertainty space in various timeframes.
Figure 2.9 Total costs (solid line), interruption costs (dotted line) and reliability costs (dashed line) as a function of the reliability level .
Chapter 3: Probabilistic Indicators for the Assessment of Reliability and Security of Future Power Systems
Figure 3.1 Interactions among three TSO processes [1].
Figure 3.2 Overview of reliability analysis.
Figure 3.3 Algorithm for the reliability assessment using state enumeration.
Figure 3.4 Algorithm for the reliability assessment using Monte Carlo simulation.
Figure 3.5 Dutch EHV transmission network (380/220 kV) [12].
Figure 3.6 State enumeration-based algorithm for the case study [2].
Figure 3.7 Configurations of underground cable connections.
Figure 3.8 UGC in different connections – PLC.
Figure 3.9 UGC in different connections – Probability of overload.
Figure 3.10 UGC in different connections – Expected redispatch costs. The leftmost point corresponds with Con1, the middle point corresponds with Con2, and the rightmost point corresponds with Con3.
Figure 3.11 Monte Carlo simulation-based algorithm for the case study [2].
Chapter 4: An Enhanced WAMS-based Power System Oscillation Analysis Approach
Figure 4.1 The EMD algorithm.
Figure 4.2 Test
sine
signal.
Figure 4.3 EMD analysis result.
Figure 4.4 Time domain spectrum of HHT (Instantaneous frequency).
Figure 4.5 The signal in Eq. (4.13) (on top) and the first two IMFs (IMF 2 is plotted as dashed line).
Figure 4.6 The signal of Eq. (4.14) (on top) and the first two IMFs (IMF 2 is plotted as dashed line).
Figure 4.7 Oscillation mode extraction algorithm.
Figure 4.8 Pre-treatment process results: test (top) and filtered (bottom) data.
Figure 4.9 FFT spectrum of Butterworth filtered data.
Figure 4.10 FFT spectrum of Butterworth filtered data (zoomed view).
Figure 4.11 Pre-treatment process of nonlinear/nonstationary signal results.
Figure 4.12 FFT spectrum of band-pass Butterworth filtered data.
Figure 4.13 The mirror extension method.
Figure 4.14 Inhibiting the boundary end effect problem of the EMD algorithm by the mirror extension method.
Figure 4.15 Non-integral periodic sine signal and its Hilbert transform (T=0.23).
Figure 4.16 Instantaneous frequency of non-integral periodic sine signal.
Figure 4.17 HT computing process.
Figure 4.18 Integral periodic sampling sine signal and its Hilbert transform.
Figure 4.19 Inhibiting the boundary end effect algorithm.
Figure 4.20 Processed instantaneous frequency spectrum.
Figure 4.21 Pre-treatment EMD results (sampling time 0.01s).
Figure 4.22 Pre-treatment EMD results (sampling time 0.033s).
Figure 4.23 EMD results (sampling time 0.01s).
Figure 4.24 EMD results (sampling time 0.033s).
Figure 4.25 Bimodal test oscillation signal.
Figure 4.26 EMD results of bimodal test signal given in (4.26).
Figure 4.27 Pre-treatment EMD results.
Figure 4.30 IMF 2 and IMF 3 of the distorted signal obtained without pre-treatment EMD.
Figure 4.28 EMD results.
Figure 4.29 The first two IMFs of the distorted signal obtained with pre-treatment EMD.
Figure 4.31 PMU locations for the Campus WAMS project (Japan).
Figure 4.32 Phasor measurement unit (Toshiba NCT2000).
Figure 4.33 Waveforms of phase difference between Miyazaki University and Nagoya Institute of Technology University stations.
Figure 4.34 EMD results.
Figure 4.35 Hilbert marginal spectrum.
Figure 4.36 Short time phasor difference data from the extracted oscillation mode (FFT filtered) and obtained IMF 5 (EMD filtered).
Figure 4.37 Amplitude of the extracted oscillation mode.
Figure 4.38 Short time data from the extracted oscillation mode and the obtained IMF.
Chapter 5: Pattern Recognition-Based Approach for Dynamic Vulnerability Status Prediction
Figure 5.1 Dynamic vulnerability region concept.
Figure 5.2 Methodological framework for recognition of DVRs.
Figure 5.3 Post-contingency pattern recognition method.
Figure 5.4 IEEE New England 39-Bus test system single-line diagram [19].
Figure 5.5 39-bus-system: non-vulnerable case
Figure 5.6 39-bus-system: voltage unstable case.
Figure 5.7 Relay tripping time Histograms for 39-bus-system: (a) OSR, (b) VR, (c) FR.
Figure 5.8 TW
3
voltage-magnitude-based EOFs for 39-bus-system: (a) EOF 1, (b) EOF 2, (c) EOF 3, (d) EOF 4.
Figure 5.9 TW
3
voltage-magnitude-based DVRs for 39-bus-system.
Figure 5.10 Support vectors and optimal separating hyper-plane of SVC.
Figure 5.11 Post-contingency vulnerability status prediction methodology.
Figure 5.12 SVC real-time implementation in a control center.
Chapter 6: Performance Indicator-Based Real-Time Vulnerability Assessment
Figure 6.1 Framework of the proposed real-time assessment methodology.
Figure 6.2 39-bus-system associated PMU areas: (a) associated PMU 1 area, (b) associated PMU 2 area, (c) associated PMU 3 area, (d) associated PMU 4 area, (e) associated PMU 5 area, (f) associated PMU 6 area.
Figure 6.3 39-bus-system probabilistic associated PMU areas.
Figure 6.4 39-bus-system non-vulnerable case.
Figure 6.7 39-bus-system frequency unstable case.
Figure 6.5 39-bus-system transient unstable case.
Figure 6.8 Associated PMU COI-referred rotor angles for transient unstable case of Figure 6.5.
Figure 6.9 Voltage relay triggering characteristic.
Figure 6.6 39-bus-system voltage unstable case.
Figure 6.10 TSI for transient unstable case of Figure 6.5.
Figure 6.11 VDI for voltage vulnerable case of Figure 6.6.
Figure 6.12 FDI for frequency vulnerable case of Figure 6.7.
Figure 6.13 Time window analysis for structuring the 39-bus-system logic schemes.
Figure 6.14 39-bus-test system logic schemes: a) TW
1
∨ TW
2
, b) TW
3
, c) TW
4
∨ TW
5
.
Figure 6.15 TVFS indices for transient unstable case of Figure 6.5.
Figure 6.16 TVFS indices for voltage vulnerable case of Figure 6.6.
Figure 6.17 TVFS indices for frequency vulnerable case of Figure 6.7.
Figure 6.18 Oscillatory modes detected at PMU 3: (a, b) inter-area, (c, d) local.
Chapter 7: Challenges Ahead Risk-Based AC Optimal Power Flow Under Uncertainty for Smart Sustainable Power Systems
Figure 7.1 One-line diagram of the 5-bus system.
Figure 7.2 Operation cost of RB-SCOPF versus the maximum allowed risk.
Figure 7.3 High level flowchart of the OPF solution methodology in the also corrective mode.
Chapter 8: Modeling Preventive and Corrective Actions Using Linear Formulation
Figure 8.1 Representation of transmission line with a PST.
Figure 8.2 Piece-wise linear representation of the preventive generator redispatch cost.
Figure 8.3 Model of a PST.
Figure 8.4 Roy Billinton Test System [16].
Figure 8.5 Case Study 1: Preventive generator redispatch.
Figure 8.6 Case Study 1: Corrective generator redispatch. Each square of the first eleven rows depicts the corrective generator set point relative to the maximum set point of that generator for a contingency. The different contingencies are shown in columns. The corrective generator set point is a combination of the preventive generator set point , depicted in gray, adjusted with possible upwards or downwards corrective generation redispatch, respectively depicted in green and orange. Each square in the last row depicts the part of the total load that is shed during a contingency.
Figure 8.7 Case study 2: The difference in corrective generation redispatch and load shedding between CS1 without PST in the grid and CS2 with a PST connected to transmission line T4 for contingencies C12 (T1) and C17 (T7).
Figure 8.8 Case study 3: The difference in corrective generation redispatch and load shedding between CS1 without transmission line switching in the grid and CS3 with transmission line switching for contingencies C12 (T1) and C17 (T7).
Chapter 9: Model-based Predictive Control for Damping Electromechanical Oscillations in Power Systems
Figure 9.1 MPC concept.
Figure 9.2 Exciter block diagram.
Figure 9.3 PSS block diagram.
Figure 9.4 TCSC block diagram.
Figure 9.5 MPC for generator.
Figure 9.6 MPC for TCSC.
Figure 9.7 Centralized MPC.
Figure 9.8 Decomposition of a two-area system.
Figure 9.9 Hierarchical MPC.
Figure 9.10 Test system.
Figure 9.11
P
1-2
in ideal conditions.
Figure 9.12
Spd
1
in ideal conditions.
Figure 9.13 MPC signal for exciter 1.
Figure 9.14 MPC signal for TCSC.
Figure 9.15
Spd
1
with SE errors.
Figure 9.16 Input
u
and delay τ.
Figure 9.17
P
1-2
with delay.
Figure 9.18
P
1-2
with decentralized MPC.
Figure 9.19
P
1-2
with hierarchical MPC1.
Figure 9.20
Spd
1
with Δ
t
of 0.2 seconds.
Figure 9.23
P
1-2
with communication failure.
Chapter 10: Voltage Stability Enhancement by Computational Intelligence Methods
Figure 10.1 Illustration of continuation method.
Figure 10.2 An artificial neural model.
Figure 10.3 Common ANN configurations (a) feed-forward (b) recurrent networks.
Figure 10.4 Data structure of the solution archive.
Figure 10.5 IEEE 30 bus test system.
Figure 10.6 Conceptual diagram of the proposed two-stage design.
Figure 10.7 Transmission power losses.
Figure 10.8 Voltage stability margin.
Figure 10.9 Sensitivity due to load change at different buses.
Figure 10.10 PV profile at bus 30.
Chapter 11: Smart Control of Offshore HVDC Grids
Figure 11.1 Generic fuzzy-based droop controller.
Figure 11.2 Pictorial overview of the complete system and control modules.
Figure 11.3 Plot of MF for the error signal.
Figure 11.4 Plot of MF for the square of rate of change of active power signal.
Figure 11.5 Plot of MF for the grid voltage.
Figure 11.6 3-D surface plot.
Figure 11.7 Equivalent state machine of the fuzzy controller.
Figure 11.8 System response to set point change at VSC 2.
Figure 11.9 System Response to constantly changing reference set points at VSC 2.
Figure 11.10 System response to sudden disconnection of wind power plant.
Figure 11.11 System response to outage of VSC 3.
Chapter 12: Model Based Voltage/Reactive Control in Sustainable Distribution Systems
Figure 12.1 Principle of MPC.
Figure 12.2 Energy Illustration of sensitivity for (a) linear and (b) nonlinear dependency.
Figure 12.3 Implementation framework of MPC based voltage control.
Figure 12.4 Existing centralized controller in the test system.
Figure 12.5 MPC based control scheme.
Figure 12.6 Flowchart of operational principle of MPC based controller.
Figure 12.7 Test system.
Figure 12.8 Correction of reactive power exchange.
Figure 12.9 Active power exchange inversely proportional to reduction in losses.
Figure 12.10 Voltage at bus#1166.
Figure 12.11 Correction of reactive power exchange.
Figure 12.13 Reactive power injection of DGs.
Figure 12.12 Active power injection of DGs.
Figure 12.14 Voltage at several buses.
Chapter 13: Multi-Agent based Approach for Intelligent Control of Reactive Power Injection in Transmission Systems
Figure 13.1 Partitioned power system managed by multi-agent system.
Figure 13.2 Model of tap changing transformer and its equivalent π circuit for the branch.
Figure 13.3 Flowchart of implementation algorithm.
Figure 13.4 IEEE 30-bus modified system.
Figure 13.6 Loss convergence with different change limits of voltages .
Figure 13.5 Loss convergence with different change limits of reactive power injection from generators.
Figure 13.7 Loss convergence with different change limits of tap movement.
Figure 13.8 Loss convergence with different coefficient c.
Chapter 14: Operation of Distribution Systems Within Secure Limits Using Real-Time Model Predictive Control
Figure 14.1 Prediction and control horizons.
Figure 14.2 Operation states and corrective actions.
Figure 14.3 Extension of prediction horizon to include predicted LTC actions.
Figure 14.4 Contexts of application of the proposed control scheme.
Figure 14.5 Mode 3: updating the values over three successive times.
Figure 14.6 Progressive tightening of voltage and current bounds.
Figure 14.7 Network topology and measurement allocation.
Figure 14.8 Scenario A: Voltage correction.
Figure 14.9 Scenario A: Reactive power output of the DGUs.
Figure 14.10 Scenario B: Voltage correction.
Figure 14.11 Scenario B: Reactive power output of the DGUs.
Figure 14.12 Scenario C: Active power produced by DGUs.
Figure 14.13 Scenario C: Power flows in the transformer.
Figure 14.14 Scenario C: Reactive power produced by DGUs.
Figure 14.15 Scenario D: Active power produced by dispatchable units.
Figure 14.16 Scenario D: Bus voltages.
Figure 14.17 Scenario D: Reactive power produced by dispatchable units.
Figure 14.18 Scenario D: Reactive power produced by non-dispatchable units.
Figure 14.19 Scenario E: Active power produced by various units.
Figure 14.20 Scenario E: Bus voltages.
Figure 14.21 Scenario E: Reactive power produced by dispatchable units.
Figure 14.22 Scenario E: Reactive power produced by non-dispatchable units.
Chapter 15: Local Control of Distribution Networks
Figure 15.1 Simple T&D system with DN controlled by LTC.
Figure 15.2 Loadability curves (a) Disturbance and restoration (b) Disturbance followed by OEL action.
Figure 15.3 Restoration of equilibrium point with corrective actions (a) Curve more sensitive to reactive power (b) Curve more sensitive to active power.
Figure 15.4 Evolution with time of the minimum load curtailment needed to stabilize the system [11].
Figure 15.5 Simple system with DN controlled by LTC and DGUs.
Figure 15.6 Impact of VVCs on long-term voltage stability (a) Disturbance and voltage restoration with VVC (b) Minimum load reduction needed with time after disturbance with effect of VVC.
Figure 15.7 Nordic transmission test system with detailed DNs at six buses.
Figure 15.8 Topology of each of the 40 distribution networks.
Figure 15.9 Simplified loadability curves of considered case studies (a) Case study A (b) Case study B and C.
Figure 15.10 Cases A1 & A2: voltages at three TN buses.
Figure 15.11 Cases A1 & A2: voltages at two DN buses.
Figure 15.12 Cases A1 & A2: reactive power from TN to DNs.
Figure 15.13 Cases B1 & B2: voltages at two TN buses.
Figure 15.14 Cases B1 & B2: voltage at a DN bus controlled by an LTC.
Figure 15.15 Case B2: total active and reactive power transfer from TN to DNs.
Figure 15.16 Case B2: reactive power produced by TN-connected generators.
Figure 15.17 Cases B1 & C1: voltages at two TN buses.
Figure 15.18 Cases B1, C2 & C3: voltages at two TN buses.
Figure 15.19 Case C2: voltages at various DN buses of the same feeder.
Figure 15.20 Case C2: total active & reactive power transfer from TN to DNs.
Figure 15.21 Case C2: reactive power produced by TN-connected generators.
Figure 15.22 Case C2: Effect of time delay on emergency signal.
Figure 15.23 Case C2: voltage at a TN bus with two different load model parameter pairs; voltage reduction pu.
Figure 15.24 Case C2: regions of successful stabilization in the (,) space, for various voltage reduction values .
Figure 15.25 Case C3: voltages at various DN buses of the same feeder.
Figure 15.26 Case C3: total active & reactive power transfer from TN to DNs.
Chapter 16: Electric Power Network Splitting Considering Frequency Dynamics and Transmission Overloading Constraints
Figure 16.1 Synthesis of proposed methodologies for CIS.
Figure 16.2 Flowchart of the proposed methodology.
Figure 16.3 Flowchart of the network splitting mechanism.
Figure 16.4 Graph reduction rules.
Figure 16.5 Graph partitioning procedure.
Figure 16.6 Frequency response model application domain.
Figure 16.7 Power imbalance constraint limits determination.
Figure 16.8 Test system—IEEE New England 10-machine 39-bus system.
Figure 16.9 Collapse case—COI-referred and generator rotor angles [deg].
Figure 16.10 Collapse case—Bus frequency [Hz].
Figure 16.11 Frequency deviation—Complete and analytical frequency response models [Hz].
Figure 16.12 Proposed ACIS—Bus voltage magnitude [pu].
Figure 16.13 Proposed ACIS—Bus frequency [Hz].
Figure 16.14 Proposed ACIS—Line loadability [%].
Chapter 17: High-Speed Transmission Line Protection Based on Empirical Orthogonal Functions
Figure 17.1 Proposed algorithm, general scheme.
Figure 17.2 Directional protection, general scheme.
Figure 17.3 Representation of voltage and current signals in the EOF plane for: (a) forward fault and (b) backward fault.
Figure 17.4 Fault classification and fault location, general scheme.
Figure 17.5 Nine-bus test system implemented in ATP.
Figure 17.6 Effect of reflected waves from remote node (Node B4), for the following configurations: one power transformer and (i) two transmission lines, (ii) three transmission lines, (iii) four transmission lines. For twice the traveling time of the neighbor line (640 µs), there is no significant change in the signal waveform.
Figure 17.7 RMS fault current, fault type, and fault location pattern, , data window = 600 µs.
Figure 17.8 RMS fault current, fault type, and fault inception angle pattern, location = 1 km, data window = 600 µs.
Figure 17.9 RMS fault current, fault inception angle and location pattern for AG faults, .
Figure 17.10 RMS fault current, fault location pattern for different data windows, θ
0
= 90°.
Figure 17.11 RMS fault current, fault location pattern for different θ
0
, data window = 3 ms.
Figure 17.12 Coefficients of the first twelve EOFs, .
Figure 17.13 Effect of sampling frequency in the two first EOFs.
Figure 17.17 Interpolation of fault location pattern for AG faults, .
Figure 17.18 Interpolation of inception angle pattern for ABG faults, .
Figure 17.14 Different fault types in the two first EOF, fault location from 0 to 180 km, .
Figure 17.15 Forward single phase to ground faults (AG), , representation on the first two EOFs, (a) current signals, (b) voltage signals.
Figure 17.16 Backward single phase to ground faults (AG), , representation on the first two EOFs, (a) current signals, (b) voltage signals.
Figure 17.19 Three-dimensional representation of ABG and AG current faults using the three first EOFs. Fault location from 0 to 180 km, fault inception from 0 ° to 355 °.
Figure 17.20 WECC 9-bus test system, ATPDraw Model.
Figure 17.21 Detailed transmission line model including a fault circuit.
Chapter 18: Implementation of a Real Phasor Based Vulnerability Assessment and Control Scheme: The Ecuadorian WAMPAC System
Figure 18.1 Power transfer between two system buses.
Figure 18.2 “
π
” equivalent of system branches.
Figure 18.3 Power–angle curve.
Figure 18.4 Dynamic Contour plot of angle differences.
Figure 18.5 Methodology for determining the steady-state angle stability limits.
Figure 18.6 Histogram of angle differences between Pascuales and Molino buses for high hydrological scenarios.
Figure 18.7 Transmission corridor monitored by PMUs.
Figure 18.8 Thevenin equivalent of a transmission corridor.
Figure 18.9 P-V curve and voltage profile stability band.
Figure 18.10 Methodology for determining the voltage profile stability transfer limit of transmission corridors.
Figure 18.11 P-V curves of the Totoras–Santa Rosa 230 kV transmission line.
Figure 18.12 Oscillatory event recorded by WAProtector.
Figure 18.13 Methodology for determining amplitude oscillation limits.
Figure 18.14 Histogram of inter-area mode amplitude.
Figure 18.15 Presence of the inter-area mode in the Ecuador–Colombia interconnected system.
Figure 18.16 Diagram of the 230 kV Ecuadorian transmission corridors.
Figure 18.17 Scatter plot of the frequency and damping percentage of the inter-area modes observed in 2015.
Figure 18.18 Oscillations in the Ecuadorian power system caused by a loss of generation event.
Figure 18.19 Phase analysis of the angle of signals obtained from PMUs.
Figure 18.20 Procedure for tuning Paute plant AB stabilizers.
Figure 18.21 Bar plot comparing the appearances of the 0.45 Hz inter-area mode pre-tuning and post-tuning.
Figure 18.22 Diagram of the implemented strategies in the SPS.
List of Tables
Chapter 1: Introduction: The Role of Wide Area Monitoring Systems in Dynamic Vulnerability Assessment
Table 1.1 Actions and operations within the power system [1]
Table 1.2 Grid blackouts registered around the world [14–16]
Table 1.3 Time delay of a WAMPAC scheme per process [24]
Chapter 2: Steady-State Security
Table 2.1 Advantages and disadvantages of methods for steady-state security assessment
Table 2.2 Advantages and challenges of probabilistic reliability criteria
Table 2.3 Illustrative example of reliability cost and interruption cost of different network operator actions
Chapter 3: Probabilistic Indicators for the Assessment of Reliability and Security of Future Power Systems
Table 3.1 Actions taken during different time horizons [1]
Table 3.2 Risk categories, redundancy levels and remedial actions
Table 3.3 Failure frequency of EHV overhead lines and underground cables
Table 3.4 Repair time of EHV overhead lines and underground cables
Chapter 4: An Enhanced WAMS-based Power System Oscillation Analysis Approach
Table 4.1 Parameter identification results
Table 4.2 Parameter identification results with pre-treatment
Table 4.3 Parameter identification results with EMD-based filtering
Chapter 5: Pattern Recognition-Based Approach for Dynamic Vulnerability Status Prediction
Table 5.1 Time window definition for 39-bus-system
Table 5.2 Classification performance of 39-bus-system
Table 5.3 Vulnerability status prediction performance for 39-bus-system
Chapter 6: Performance Indicator-Based Real-Time Vulnerability Assessment
Table 6.1 39-bus-system associated PMU area database
Table 6.2 Distribution of the poorly-damped modes per each PMU
Table 6.3 OSIs per PMU—Summary of number of cases
Table 6.4 SDFs resulting from branch outages
Table 6.5 OVIs for Branch Outages—Summary of number of cases
Chapter 7: Challenges Ahead Risk-Based AC Optimal Power Flow Under Uncertainty for Smart Sustainable Power Systems
Table 7.1 5-bus system data and initial state
Table 7.2 5-bus system: line data
Table 7.3 Values of the objective, decision variables, and critical post-contingency constraints for various operating modes
Table 7.4 Results of security analysis performed at the OPF solution in “no contingencies” mode
Table 7.5 Overall and individual load shedding (MW) for contingency L2
Table 7.6 Values of the objective, decision variables, and critical post-contingency constraints for various operating modes
Chapter 8: Modeling Preventive and Corrective Actions Using Linear Formulation
Table 8.2 Load data
Table 8.3 Transmission line data
Table 8.4 Generator data
Table 8.1 Available actions for each case study
Chapter 10: Voltage Stability Enhancement by Computational Intelligence Methods
Table 10.1 Generator reactive power limit (pu)
Table 10.2 Reactive power source limits (pu)
Table 10.3 Partitioning of dataset
Table 10.4 Performance of the classifier on testing dataset
Table 10.5 Power interruption cost in different sectors
Table 10.6 Information for load shedding at selected locations
Chapter 12: Model Based Voltage/Reactive Control in Sustainable Distribution Systems
Table 12.1 Parameters of the controller
Chapter 13: Multi-Agent based Approach for Intelligent Control of Reactive Power Injection in Transmission Systems
Table 13.1 Setup Parameters
Table 13.2 Comparison on Power Loss Convergence
Chapter 14: Operation of Distribution Systems Within Secure Limits Using Real-Time Model Predictive Control
Table 14.1 Controller Anticipation of the LTC Actions
Chapter 15: Local Control of Distribution Networks
Table 15.1 Overview of simulated scenarios
Chapter 16: Electric Power Network Splitting Considering Frequency Dynamics and Transmission Overloading Constraints
Table 16.1 Results of the proposed methodology
Table 16.2 Dynamic performance of main system variables
Table 16.3 Computation times summary
Chapter 17: High-Speed Transmission Line Protection Based on Empirical Orthogonal Functions
Table 17.1 Conditions for fault simulation
Table 17.2 Forward faults (line B6-B9)
Table 17.3 Reverse faults (line B4-B6)
Table 17.4 Explained variability of the first eight EOFs for different sampling frequencies, data window 600 µs
Table 17.5 Test faults, line B6-B9
Table 17.6 Classification results, confusion matrix
Table 17.7 Results of fault location
Table 17.8 Some results of fault classification
Table 17A.1 ATPdraw line/cable model, lines: B4-B6, B6-B9, and B8-B9
Table 17A.2 ATPdraw line/cable data, line B4-B6
Table 17A.3 ATPdraw line/cable data, line B6-B9
Table 17A.4 ATPdraw line/cable data, line B8-B9
Table 17A.5 Line impedances and admittances
Chapter 18: Implementation of a Real Phasor Based Vulnerability Assessment and Control Scheme: The Ecuadorian WAMPAC System
Table 18.1 Alert and alarm limits for SNI buses as regards Molino bus—high hydrological scenarios
Table 18.2 Alert and alarm voltage limits of Totoras–Santa Rosa 230 kV transmission line per circuit
Table 18.3 Alert and alarm amplitude limits for SNI oscillatory modes
Table 18.4 Implemented strategies in the SPS
Table 18.5 Energy Not Supplied cost with and without the actuation of SPS
Dynamic Vulnerability Assessment and Intelligent Control for Sustainable Power Systems
Edited by
Professor José Luis Rueda-Torres
Delft University of Technology The Netherlands
Professor Francisco González-Longatt
Loughborough University Leicestershire, United Kingdom
This edition first published 2018
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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 for sustainable 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) | ISBN 9781119214977 (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-dc23
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List of Contributors
Jaime C. Cepeda
Operador Nacional de Electricidad (CENACE), and Escuela Politécnica Nacional (EPN)
Quito
Ecuador
Dirk Van Hertem
ESAT – Electa
University of Leuven
Belgium
Steven De Boeck
ESAT – Electa
University of Leuven
Belgium
Hakan Ergun
ESAT – Electa
University of Leuven
Belgium
Evelyn Heylen
ESAT – Electa
University of Leuven
Belgium
Tom Van Acker
ESAT – Electa
University of Leuven
Belgium
Marten Ovaere
Department of Economics
University of Leuven
Belgium
Bart W. Tuinema
Delft University of Technology
The Netherlands
Nikoleta Kandalepa
TenneT TSO B.V
Arnhem
The Netherlands
Qing Liu
Kyushu Institute of Technology
Kitakyushu
Japan
Hassan Bevrani
University of Kurdistan
Sanandaj
Iran
Yasunori Mitani
Kyushu Institute of Technology
Kitakyushu
Japan
Delia G. Colomé
Universidad Nacional de San Juan
Argentina
István Erlich
University Duisburg-Essen
Duisburg
Germany
Florin Capitanescu
Luxembourg Institute of Science and Technology, Belvaux
Luxembourg
Da Wang
Delft University of Technology
The Netherlands
Worawat Nakawiro
King Mongkut's Institute of Technology Ladkrabang
Bangkok
Thailand
Adedotun J. Agbemuko
Institut de Recerca en Energia de Catalunya (IREC)
Barcelona
Spain
Mario Ndreko
TenneT TSO GmbH
Bayreuth
Germany
Marjan Popov
Delft University of Technology
The Netherlands
Mart A.M.M. van der Meijden
TenneT TSO B.V
Arnhem
The Netherlands and Delft University of Technology
The Netherlands
Hoan Van Pham
Power Generation Corporation 2
Vietnam Electricity and School of Engineering and Technology
Tra Vinh University
Vietnam
Sultan Nasiruddin Ahmed
FGH GmbH
Aachen
Germany
Gustavo Valverde
University of Costa Rica
San Jose
Costa Rica
Hamid Soleimani Bidgoli
Université de Liège
Belgium
Petros Aristidou
University of Leeds
United Kingdom
Mevludin Glavic
Université de Liège
Belgium
Thierry Van Cutsem
Université de Liège
Belgium
Nelson Granda
Escuela Politécnica Nacional
Quito
Ecuador
Rommel P. Aguilar
Universidad Nacional de San Juan
Argentina
Fabián E. Pérez-Yauli
Escuela Politécnica Nacional
Quito
Ecuador
Pablo X. Verdugo
Operador Nacional de Electricidad (CENACE)
Quito
Ecuador
Aharon B. De La Torre
Operador Nacional de Electricidad (CENACE)
Quito
Ecuador
Diego E. Echeverría
Operador Nacional de Electricidad (CENACE)
Quito
Ecuador
Foreword
Over the last decades, the electrical power system has gone through a fundamental transformation never seen before. The liberalisation of the power industry that set the whole process in motion has opened up the possibility of electricity trading across utility and even national boundaries. The distance between where power is generated and where the final consumption takes place and with it the power transit through the high voltage transmission lines has increased immensely. A further development compounding the competitive electricity market and power transmission over long distances has been the large-scale installation of renewables-based power generation units. In addition to the volatility and stochasticity of the power outputs of these units, utilities now also have to contend with possible bi-directional power flows in the distribution networks.
Due to the different dynamic characteristics of renewable generation units compared with conventional power plants, the increasing share of renewables-based generation capacity in the system can give rise to new dynamic phenomena that can reduce the existing security of the whole system. Additionally, restrictions regarding expansion or reinforcement of the existing network mean that lines have to be loaded up to or near their maximum current carrying capabilities. It can thus be safely concluded that the increasing uncertainty regarding load flows and the use of power plants in a heavily loaded network, together with the new power generation technologies such as wind and solar as well as transmission technologies such as VSC-HVDC, would necessarily lead to the reduction of existing security levels unless appropriate countermeasures are implemented.
This book takes up this most up-to-date topic and provides valuable contributions in the areas of both vulnerability assessment and intelligent control. The use of many of the methods under discussion has been made possible by the powerful computers and communication technologies that are now available. Also, in the last decade, significant advances in the area of computational intelligence have been made. These results are now mature enough for use in the planning and operation of power systems. During a contingency, for example, the operator is often overwhelmed by the rapidly changing situation and the associated flood of information, on the basis of which appropriate steps have to be taken. Clearly, the dispatcher cannot be expected to form an objective judgment 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 best possible decision within the shortest time possible, and computer-based decision support systems come in handy here. Other promising techniques in this context are the model based predictive control approaches. If a contingency or unfavourable operating condition is predicted some time ahead of its occurrence, a suitable countermeasure can 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-time applications are required.
The current challenges, and particularly those ahead in the upcoming years, urgently require the introduction of new methods and approaches to ensure the preservation of the existing level of system security, which is taken for granted and assumed so far to be self-evident. The approaches described in this book grew out of the work of talented and committed young scientists working in the area. On the one hand, the contributions serve as a thought-provoking impulse for practising engineers who are looking 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 the other hand, this book also allows graduate students to get an overview of modern mathematical and computational methods. Certainly, the book presupposes a thorough knowledge of power system analysis, dynamics and control. Building on this, however, it introduces the reader to an exciting world of new approaches. The combination of practice-orientation and introduction of modern methods for vulnerability assessment and control applications make this book particularly valuable, and recommended reading for a wide audience in the area of power engineering.
January 2017
Prof. István Erlich Chair Professor of Department of Electrical Engineering and Information Technologies Head of the Institute of Electrical Power Systems University Duisburg-Essen
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 uses.
We hope that the book proves to be a useful source of information on the understating of dynamic vulnerability assessment and intelligent control, but at the same time provides 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 completely covered in a single edition, it is expected that a second edition of the book will be made available soon.
José Luis Rueda-Torres, Delft University of Technology, The Netherlands.
Francisco González-Longatt, Loughborough University, UK.
Chapter 1Introduction: The Role of Wide Area Monitoring Systems in Dynamic Vulnerability Assessment
Jaime C. Cepeda1 and José Luis Rueda-Torres2
1Head of Research and Development and University Professor, Technical Development Department and Electrical Energy Department, Operador Nacional de Electricidad CENACE, and Escuela Politécnica Nacional EPN, Quito Ecuador
2Assistant professor of Intelligent Electrical Power Systems, Department of Electrical Sustainable Energy, Delft University of Technology, The Netherlands
1.1 Introduction
Currently, most social, political, and economic activities depend on the reliability of several energy infrastructures. This fact has established the necessity of improving the security and robustness of Electric Power Systems [1]. In addition, the lack of investment, the use of congested transmission lines, and other technical reasons, such as environmental constraints, have been pushing Bulk Power Systems dangerously close to their physical limits [2]. Under these conditions, certain sudden perturbations can cause cascading events that may lead to system blackouts [1, 3]. It is crucial to ensure that these perturbations do not affect security, so the development of protection systems that guarantee service continuity is required. In this regard, Special Protection Schemes (SPS) are designed in order to detect abnormal conditions and carry out corrective actions that mitigate possible consequences and allow an acceptable system performance [4].
However, the conditions that lead the system to a blackout are not easy to identify because the process of system collapse depends on multiple interactions [5, 6]. Vulnerability assessment (VA) is carried out by checking the system performance under the severest contingencies with the purpose of detecting the conditions that might initiate cascading failures and may provoke system collapse [7]. A vulnerable system is a system that operates with a “reduced level of security that renders it vulnerable to the cumulative effects of a series of moderate disturbances” [7]. The concept of vulnerability involves a system's security level (static and dynamic security) and the tendency of its conditions 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 necessity of 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 few milliseconds, have allowed the development of modern approaches that come closer to the target of real time vulnerability assessment [6, 7]. Most of these real time applications have been focused on identifying signals that suggest a possibly insecure steady state. 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., preventive control) [9]. Nevertheless, the use of PMUs has great potential to allow the performance of post-contingency Dynamic Vulnerability Assessment (DVA) that could be used to trigger SPSs in order to implement corrective control actions. In this connection, a Wide Area Monitoring System (WAMS), based on synchrophasor technology, constitutes the basic infrastructure for implementing a comprehensive scheme for carrying out real time DVA and afterwards executing real time protection and control actions. This comprehensive scheme is called a Wide Area Monitoring, Protection, and Control system (WAMPAC) [10]. This chapter presents a general overview of concepts related to power system vulnerability assessment and phasor measurement technology and subsequently highlights 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 that renders it vulnerable to the cumulative effects of a series of moderate disturbances.” Vulnerability is a measure of system weakness regarding the occurrence of cascading events [7].
The concept of vulnerability involves a system's security level (i.e., static and dynamic security) 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 to develop. The occurrence of an abnormal contingency in vulnerable areas and a highly stressed 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 system during a specific static or dynamic operating condition regarding the occurrence of cascading events.” This concept makes vulnerability an essential indicator of system collapse proximity.
Although there are a lot of vulnerability causes, which vary from natural disasters to human failures, system vulnerability is characterized by four different symptoms of system stress: angle instability, voltage instability, frequency instability, and overloads [5]. So, vulnerability assessment should be performed through analyzing the system status as regards these symptoms of system stress.
1.2.1 Vulnerability Assessment
Vulnerability assessment has the objective of preventing the occurrence of collapses due to catastrophic perturbations [11]. Performing VA requires specific mathematical models capable of analyzing the multiple interactions taking place between the different power system components [11]. These models have to consider the varied phenomena involved in the vulnerability condition and also the diverse timeframes in which the corresponding phenomena occur.
Many methods have been proposed for vulnerability assessment, which have been classified based on various criteria [6, 7]. However, in terms of their potential implementation in control centers, the techniques to assess vulnerability can be classified into off-line, on-line, and real time methods.Figure 1.1 depicts the proposed classification of vulnerability assessment methods.
Figure 1.1 Power system vulnerability assessment methods.
Off-line assessment:
