Wide Area Monitoring of Interconnected Power Systems

252 Pages·2015·2.1 MB·English

by  Arturo Roman Messina

IET POWER AND ENERGYSERIES 77
Wide-Area
Monitoring of
Interconnected
Power Systems
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Volume905 Powersystemprotection,4volumes
Wide-Area
Monitoring of
Interconnected
Power Systems
Arturo Roma´n Messina
The Institution ofEngineeringand Technology
PublishedbyTheInstitutionofEngineeringandTechnology,London,UnitedKingdom
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†TheInstitutionofEngineeringandTechnology2015
Firstpublished2015
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Contents
Preface ix
1 Wide-area monitoring andanalysis systems 1
1.1 Introduction 1
1.2 Wide-area monitoring systems: a conceptual overview 2
1.3 Data collection and management 3
1.4 Challenges of future smart monitoring and analysis systems 4
References 5
2 Wide-area monitoring systemarchitectures 9
2.1 Introduction 9
2.2 WAMSarchitectures 9
2.2.1 Centralized WAMSarchitectures 11
2.2.2 Hierarchical WAMSarchitectures 12
2.2.3 Hybrid WAMSarchitectures 13
2.3 Issuesin data fusion 13
2.3.1 Data 13
2.3.2 Intelligent synchrophasor data fusion 14
2.3.3 Powersystemdata fusionstrategies 16
2.3.4 General framework for data assimilation 20
2.4 Relationship betweenmultiblock and single-block models 22
References 23
3 Spatio-temporal modeling of power systemdynamic processes 27
3.1 Introduction 27
3.2 Visualization of large space-time measurement data 28
3.3 Spatio-temporal modeling of multivariate processes 29
3.3.1 Empirical orthogonal function (EOF)analysis 29
3.3.2 SVD-basedproper orthogonal decomposition 33
3.3.3 Departure from mean value 36
3.4 Spatio-temporal interpolation methods 37
3.4.1 Background 37
3.4.2 Similarity measures 38
3.4.3 Spatial structures 40
3.4.4 Derivation of weights 40
3.4.5 Practical issues 41
vi Wide-area monitoring of interconnected power systems
3.5 Dimensionality reduction 42
3.5.1 Proximity (similarity) measures 42
3.5.2 Nonlinear spectral dimensionality reduction 43
3.6 Motivational example 47
3.6.1 Small-signal response 48
3.6.2 Large system response 48
3.6.3 Statistical analysis 49
3.7 Sensorplacement 51
3.7.1 Problem formulation 53
3.7.2 Constrained sensor placement 54
References 58
4 Advanceddataprocessingandfeature extraction 63
4.1 Introduction 63
4.2 Power oscillation monitoring 64
4.3 Time-frequency representations 65
4.3.1 Hilbert–Huang analysis 65
4.3.2 Wavelet analysis 72
4.3.3 The Teager–Kaiser operator 75
4.3.4 Dynamic harmonic regression 76
4.4 Mutivariate multiscale analysis 81
4.4.1 Multi-signal Pronyanalysis 82
4.4.2 Koopman analysis 83
4.5 Response under ambientstimulus 87
4.5.1 Formulation of the model 87
4.5.2 Modal response 89
4.5.3 Ensemble system response 90
4.6 Applicationto measured data 90
4.6.1 HHTanalysis 92
4.6.2 Wavelet analysis 94
References 96
5 Multisensor multitemporal datafusion 101
5.1 Introduction 101
5.2 Data fusion principles 101
5.3 Data pre-processing and transformation 104
5.3.1 Bandpass filtering and denoising 104
5.3.2 Local-level fusion 105
5.4 Feature extractionand feature selection 105
5.4.1 Feature extraction 105
5.4.2 Data compression 106
5.4.3 Individual scales 109
5.4.4 Filtering and multiscale monitoring 109
5.5 Multisensor fusion methodologies for system monitoring 111
5.5.1 Single-scale analysis 112
5.5.2 Nonlinear PCAusing auto-associative neural networks 112
Contents vii
5.5.3 Multiblock POD(PCA)analysis 113
5.5.4 Nonlinear PCA 119
5.5.5 Blind source separation 119
5.6 Other approaches to multisensor data fusion 124
References 128
6 Monitoring thestatusof thesystem 131
6.1 Introduction 131
6.2 Powersystem health monitoring 132
6.3 Disturbance and anomaly detection 132
6.4 Modal-based health monitoring methods 134
6.4.1 Filtering and data conditioning 134
6.4.2 Entropy and energy 138
6.4.3 Entropy-based detection of system changes 141
6.5 Wide-area inter-area oscillation monitoring 143
6.5.1 Case A 143
6.5.2 Case B 145
6.6 High-dimensional pattern recognition-based monitoring 146
6.6.1 Sparse diffusion implementation 146
6.6.2 Data clustering 148
6.6.3 Numerical example 148
6.6.4 Hybrid schemes 150
6.7 Voltage and reactive power monitoring 150
6.7.1 Measured data 150
6.7.2 Statistical approach to voltage monitoring 151
6.7.3 Complex POD/PCAanalysis 155
References 156
7 Near real-time analysis andmonitoring 159
7.1 Introduction 159
7.2 Toward near real-time monitoring of system behavior 159
7.3 Data processing and conditioning 160
7.3.1 Wavelet denoisingand filtering 160
7.3.2 EMD-based filtering 162
7.4 Damage detection from changes in system behavior 163
7.4.1 Event trigger 164
7.4.2 Event detection based on linear filtering 164
7.4.3 Anillustration 166
7.5 Time-series approaches to detection of abnormal operation 166
7.5.1 Near real-time implementations 166
7.5.2 Near real-time implementation of the
Hilbert transform 169
7.5.3 Local meanspeed 173
7.6 Pattern recognition-baseddisturbance detection 176
7.7 Sliding window-based methods 177
7.7.1 Local HHT analysis 177
viii Wide-area monitoring of interconnected powersystems
7.7.2 Numerical example 180
7.7.3 Sliding window-based Koopman mode analysis 181
7.8 Recursive processingmethods 182
7.8.1 State-space model forlinear regression 182
7.8.2 Adaptive tracking of system oscillatory modes 183
References 188
8 Interpretation andvisualization of wide-area PMU measurements 191
8.1 Introduction 191
8.2 Lossof generation oscillation event 191
8.2.1 Operational context 192
8.2.2 Recorded measurements 192
8.3 Analysis and visualization of recorded data 196
8.3.1 Mode shape characterization 196
8.3.2 Damping estimation 197
8.3.3 Instantaneousparameters 197
8.3.4 Multitemporal, multiscale analysis of measured data 205
8.3.5 Performance evaluation 209
8.4 Pattern recognition analysis 211
8.4.1 Diffusionmap analysis 211
8.4.2 Comparison withother approaches 214
8.5 POD/BSSanalysis 216
8.6 Validation of power system model 218
8.6.1 Small signal performance 218
8.6.2 Large system performance 218
8.7 Evaluation of control performance 221
References 225
AppendixA Physical meaning of proper orthogonal modes 227
AppendixB Datafor the five-machine test system 231
AppendixC Masking techniquesto improve empirical mode
decomposition 235
Index 237
Preface
Inthelastfewyears,theinterestinmonitoringandanalysisofkeysystemvariables
throughoutthetransmissionanddistributionsystemhassignificantlyincreaseddue
tothe need toassessthe power system health in near real-time. The emergence of
new sensors, advanced communication systems, and improved processing techni-
ques makes real-time system-wide monitoring increasingly possible. These
advances result in large data sets that must be integrated to reduce uncertainty in
power system security and reliability assessment.
Thepurposeofthisbookistoprovideacomprehensivetreatmentofadvanced
data-driven signal processing techniques for the analysis and characterization of
both ambient power system data and transient oscillations resulting from major
disturbance. Inspired by recent developments in multisensor data fusion, multi-
temporal data assimilation techniques for power system monitoring are proposed
and tested in the context of modern wide-area monitoring system (WAMS) archi-
tectures. Recent advances in understanding and modeling nonlinear, time-varying
power system processes are reviewed and factors affecting the performance of
these techniques are discussed.
Anumber ofalgorithms andexamples are presented throughoutthetext asan
aid to understanding the basic material provided. Challenges involved in realistic
monitoring,visualization,andanalysisofactualdisturbanceeventsareemphasized
and examples of applications to a wide range of power networksare provided.
Structurally,thebookisdividedintothreebasicparts.Theprincipaltheoretical
thrust of the book is embodied in Chapters 3 through 7.Chapters 1 and 2 examine
themostimportantproblemsinWAMS,stressingtheproblemofdatamanagement
and data fusion. They also introduce simulation methodology. Chapters 3 and 4
discuss the development of advanced algorithms for the analysis and characteriza-
tion of spatio-temporal dynamics and illustrate and compare some of the proposed
analyticalprocedures.
Chapters 5 through 7 examine the application of novel multivariate, multi-
temporaldataanalysistechniquestotheanalysisandvisualizationofsynchrophasor
data. Examples are used throughout to demonstrate the application of the theory.
Chapter 8 examines the application of WAMS strategies to monitor and visualize
multiplestreamsofphasormeasurementunit(PMU)data.
The appendixes at the end of the book provide necessary complementary
information tothe book.
Arturo Roma´nMessina
March2015