Wide area monitoring, protection and control systems: the enabler for smarter grids

200 Pages·2016·6.3 MB·English

by  Vaccaro

IET POWER AND ENERGYSERIES 73
Wide Area
Monitoring,
Protection and
Control Systems
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Volume905 PowerSystemProtection,4volumes
Wide Area
Monitoring,
Protection and
Control Systems
The enabler for Smarter Grids
Edited by
Alfredo Vaccaro and
Ahmed Faheem Zobaa
The Institution ofEngineeringand Technology
PublishedbyTheInstitutionofEngineeringandTechnology,London,UnitedKingdom
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Contents
Preface xi
1 Widearea measurementsystem: the enabler for smarter
transmissiongrids 1
1.1 WAMS: definition and constitutive sub-processes 2
1.1.1 WAMSdata resource 2
1.1.2 WAMScommunication systems 6
1.1.3 WAMSapplications/functionalities 7
1.2 Italian WAMSarchitecture and functionalities 10
1.3 WAMSdata classification 13
1.4 Preprocessingsynchronized phasor measurement data
for power system analyses 15
References 16
2 Reliability-based substationmonitoring systemsplacement 19
2.1 Substation monitoring system 22
2.1.1 Monitoring system components 22
2.1.2 Branch PMU 23
2.1.3 Substation configuration 23
2.1.4 Substation communication system 24
2.2 Substation monitoring system reliability 25
2.2.1 Failure probability of voltage measurement 26
2.2.2 Failure probability of current measurement 27
2.3 SMSplacement based onbus reliability 28
2.4 Reliability-basedsubstation monitoring system placement
considering transmission line outages 29
2.5 Evaluation of reliability-based SMSplacement 31
2.6 Numerical studies 33
2.6.1 Reliability-basedPMUplacement in the WSCC
3-machine, 9-bussystem 34
2.6.2 Reliability-basedPMUplacement in the IEEE 57-bustest
system 35
2.6.3 Reliability analysisof the monitoring systems in the
IEEE 9-bus test system 37
2.6.4 Reliability analysisof the monitoring systems in the
IEEE 57-bus test system 38
2.7 Conclusions 39
References 41
viii Wide areamonitoring, protection and control systems
3 Systemintegrity protectionscheme basedonPMU technology 45
3.1 Introduction 45
3.2 Smart transmission grid 47
3.2.1 Demandsand requirements 47
3.3 SIPS–in general 48
3.4 Wide area disturbances 49
3.5 SIPSarchitecture 51
3.5.1 Design 52
3.5.2 Multipurpose open SIPSarchitecture 56
3.5.3 SIPScomponents 63
3.6 SIPSdata archival system 65
3.6.1 Real-time database 66
3.6.2 DASarchitecture 67
3.7 SIPSapplications 68
3.8 Data protocols 69
3.8.1 Field data acquisition protocols 70
3.8.2 Mixing protocols 71
3.9 SIPSmonitoring and testing functions 71
3.9.1 Testing facilities 71
3.9.2 Monitoring and condition-based maintenance (CBM) 72
3.9.3 Configurationmanagement 74
3.10 Example of SIPSapplication based onPMUtechnology 75
3.10.1 Operational and influence analysis of WPPon the EPS 75
3.10.2 Problemsregarding WPP Vratarusa 76
3.10.3 N-1Analysisregarding the specific disturbances in EPS 76
3.10.4 Conceptual design of automatic control and monitoring 77
3.10.5 System architecture overview 80
References 82
4 Newmethodologies for large-scale power system
dynamic analysis 85
4.1 Introduction 85
4.2 Dynamic model 90
4.3 Eigenvalues and eigenvectors 93
4.4 Necessary conditions for DVSequilibrium tracing 97
4.5 Optimization-based model for equilibrium tracing 101
4.6 Iterative algorithms for DVStracing 103
4.6.1 Predictor–corrector-based algorithm 103
4.6.2 Interval bisection-based algorithm 105
4.6.3 Q–V Sensitivity 105
4.7 Dynamic analysiswith topology control(TC)optimization 106
4.7.1 DVSAssessment 107
4.7.2 Small-signal stability analysis 107
4.7.3 Optimization of eigenvalues movement 111
Contents ix
4.8 Illustrative test results 113
4.8.1 Small-scale (New England) 39-bus,46-branch and
10-generator test system 114
4.8.2 Large-scale real-world (PJM)13709-bus,18285-branch
and 2532-generator test system 119
References 130
5 Afuzzy-baseddatamining paradigm for on-line
optimal powerflow analysis 135
5.1 Introduction 135
5.2 Problem formulation 137
5.2.1 Optimal power flow analysis 137
5.3 The proposed approach 140
5.3.1 F-transform: an overview 141
5.3.2 The offline stage 143
5.3.3 The online stage 144
5.4 Simulation results 146
5.5 Conclusive remarks 153
References 154
6 False datainjection attacks andcountermeasuresfor wide area
measurement system 159
6.1 Introduction 159
6.2 Preliminaries of SE 161
6.2.1 Nonlinear SE 161
6.2.2 DCstate estimation 161
6.2.3 Bad data detection 162
6.3 Framework of FDIAonDC SE 162
6.3.1 Generic linear measurement model 162
6.3.2 Generic FDIAonthe proposed linear measurement model 163
6.4 Framework of FDIAonAC SE 165
6.4.1 Perfect FDIAonAC SE 165
6.4.2 Practical FDIAonACSE 166
6.5 Proposedmeasurement consistency check-based FDIA
detection method 168
6.5.1 Robust Huber-estimator forpower system 169
6.5.2 Proposedstatistical test-based FDIAdetection method 170
6.6 Simulation results 171
6.6.1 Detection of FDIAonDC SE 172
6.6.2 Detection of FDIAonAC SE 173
6.7 Conclusions 175
References 176
Index 179