Power System Grid Operation Using Synchrophasor Technology (eBook)

Sarma Nuthalapati is currently working as Principal EMS Network Applications Engineer for Peak Reliability (Peak) in Vancouver, WA, USA. Peak’s Reliability Coordinator Area includes all or parts of 14 western states, British Columbia, and the northern portion of Baja California, Mexico. Sarma supports Energy Management Systems (EMS) network applications such as State Estimation, Real-time Contingency Analysis, Remedial Action Schemes, Forced Outage Detection, and others that are critical to providing wide area situational awareness in control center operations. He is also an Adjunct Professor in the Department of Electrical Engineering at Texas A&M University, College Station, TX, USA. He has been with Electric Reliability Council of Texas, Inc. (ERCOT), USA, in the Advanced Network Applications Group of the Operations Support Department from August 2007 to March 2016 and was involved in the area of network applications and involved in a Synchrophasor Project funded by the US Department of Energy (DOE) under the Smart Grid Initiatives Grants. He is currently the Chair of the IEEE Task Force on Real Time Contingency Analysis. He also actively participates in the North American SynchroPhasor Initiative (NASPI) Working Group meetings and was given NASPI Control Room Solutions Task Team Most Valuable Player (MVP) Award for ‘being a leading organizer and contributor to the Control Room Solutions Task Team (CRSTT) and the NERC Synchronized Measurement Subcommittee and a public champion for Synchrophasor Technology’. He is a senior member of IEEE and a member of IEEE Power and Energy Society (PES). He is also a distinguished lecturer in the IEEE PES Distinguished Lecturer Program. He received BTech (Electrical Engineering) and MTech (Power Systems Engineering) degrees from National Institute of Technology, Warangal, (formerly called as Regional Engineering College, Warangal), India, in 1983 and 1986 respectively. He obtained his PhD degree from Indian Institute of Technology, Delhi, India, in 1995. He carried out his PhD work in the area of 'Network Reconfiguration in Distribution Systems' under the supervision of the late Dr. K.S. Prakasa Rao. The research work involved developing new algorithms for various aspects of network reconfiguration such as reconfiguration for Service Restoration, Load Balancing and Loss Minimization in Distribution Systems. These methods are very useful in the context of Smart Grids and Distribution Automation.

Foreword 7
Preface 9
Contents 14
1 Importance of Synchrophasor Technology in Managing the Grid 16
1.1 The Value of High-Speed Time-Stamped Data 16
1.2 More About Time Synchronization 19
1.3 The Principal Applications and Benefits of Synchrophasor Technology 20
1.4 The Role of the US Department of Energy Promoting the Deployment of Synchrophasors in the North American Power System 21
2 Impact of Phasor Measurement Data Quality in Grid Operations 27
2.1 Introduction 27
2.2 Categories of Data Impairment 28
2.2.1 Data Loss 28
2.2.2 Data Corruption 30
2.2.3 Inaccurate Representation 31
2.2.4 Lack of Precision 33
2.2.5 Incorrect Identification of Data 35
2.2.6 Excessive or Inconsistent Latency 36
2.3 Data Error Control and Detection 37
2.3.1 Measurement System Planning and Design 37
2.3.2 Installation and Validation 39
2.3.3 Error Detection and Mitigation 40
2.4 Impacts of Data Impairments 43
2.4.1 Introduction 43
2.4.2 Lost Data 46
2.4.3 Data Corruption 47
2.4.4 Inaccurate Representation of Engineering Quantity 48
2.4.5 Lack of Precision 51
2.4.6 Incorrect Measurement Identification 52
2.4.7 Excessive or Inconsistent Latency 52
2.5 Summary 53
References 54
3 Testing and Validation of Synchrophasor Devices and Applications 55
3.1 Introduction 55
3.2 Review of Synchrophasor Technology and Phasor Measurement Unit (PMU) 57
3.2.1 Synchrophasor Applications in Power System Operation 59
3.2.2 Need for Testing and Validation of Synchrophasors Devices and Applications 59
3.3 Testing of Synchrophasor Devices 60
3.3.1 Review of Synchrophasors Standards and Compliance Testing of PMU 60
3.3.2 Procedure and Hardware Requirement for PMU Testing 62
3.3.3 Testbed Architecture and PMU Performance Analyzer (PPA) 64
3.3.4 Example Results for PMU Testing at South California Edison 66
3.4 Testing and Validation of Synchrophasors-Based Monitoring Application 68
3.4.1 PMU Data Quality and Impact on Applications 68
3.4.2 PARTF Framework for Analyzing Impact of PMU Data Quality on Applications 70
3.4.3 Preprocessing PMU Data for Event Detection 71
3.4.4 Synchrophasors-Based Event Detection 73
3.4.5 Testbed Architecture for Validation of Event Detection Application 73
3.4.6 Example Validation Results for PMU-Based Event Detection 76
3.5 Testing and Validation of Synchrophasors-Based Control Application 78
3.5.1 Synchrophasors-Based Remedial Action Schemes 80
3.5.2 Testbed Architecture for RAS Testing 81
3.5.3 Example RAS Testing Results 84
3.6 Summary 86
References 87
4 Synchrophasor Technology at BPA 90
4.1 History of the Synchrophasor Technology at BPA 91
4.2 BPA Synchrophasor Investment Project 92
4.3 Engineering Applications at BPA 94
4.3.1 Power Plant Model Validation 95
4.3.2 Power Plant Performance Monitoring and Analysis 98
4.3.3 System Model Validation and Event Analysis 99
4.3.4 Event Analysis 100
4.3.5 Frequency Response Analysis 102
4.3.6 Oscillation Event Analysis 103
4.3.7 Voltage Fluctuations due to Variable Transfers 105
4.3.8 State Estimation 106
4.3.9 Data Quality Monitoring 106
4.4 Control Room Applications at BPA 107
4.4.1 Oscillation Detection 107
4.4.2 Frequency Event Detection 110
4.4.3 Islanding Detection 112
4.4.4 Mode Meter or Low Oscillation Damping Detection 113
4.5 Synchrophasor-Based Controls 114
4.6 Value Realized from the BPA Synchrophasor Project 117
4.7 Technology Innovation Pipeline 122
4.7.1 Synchrophasor Infrastructure 122
4.7.2 Engineering Analysis 123
4.7.3 Control Room Applications 123
4.7.4 Wide-Area Controls 124
4.7.5 Collaboration and Technology Outreach 126
4.8 Synchrophasor Project Team 127
4.9 Relevant Technology Innovation Projects 128
Acknowledgements 128
Appendix A: July 2, 1996 Western Interconnection Outage 129
Appendix B: August 10, 1996 Western Interconnection Outage 130
Appendix C: June 14, 2004 Generation Outage in the West 130
Appendix D: Frequency Response Analysis at BPA 131
Event Detection 131
Notification 132
Visualization 132
Data Extract 134
Analysis 134
Baselining 135
Generating Fleet Performance Analysis 137
Power Pickup Analysis 138
5 Use of Synchrophasor Measurement Technology in China 141
5.1 Introduction 141
5.2 Development and Applications of PMUs in China 141
5.2.1 Status of PMU Deployment 141
5.2.2 PMU Supporting IEC 61850 Protocol 142
5.2.3 Data Transmission Network and Time Synchronization Networks in Chinese Power Grids 143
5.3 Development and Basic Applications of WAMS in China 144
5.3.1 Architecture of WAMS 144
5.3.2 Power System Model Parameter Identification and Validation 145
5.3.2.1 Load Model and Parameter Identification 145
5.3.2.2 Identification of Generator’s Moment of Inertia 147
5.3.3 Disturbance Recognition and Location 148
5.4 Estimation of Electromechanical Modes from Ambient PMU Data 151
5.4.1 Classification of PMU Data 151
5.4.2 ARMA-Based Identification Method 152
5.4.3 Application Case in CSG 153
5.4.4 Low-Frequency Oscillation Mechanism Analysis 156
5.5 Phasor Measurement-Based Wide-Area Protection (WAP) Application 160
5.5.1 Problems Faced by Conventional Protection 160
5.5.2 Architecture of WAP System 160
5.5.3 Proposed WAP Functions 162
5.5.3.1 Enhanced Current Differential Protection 162
5.5.3.2 Pilot Direction Protection 163
5.5.3.3 Protection for CB Failure 163
5.5.4 Application Case Study 164
5.5.4.1 Brief Description of Duyun WAP System 164
5.5.4.2 Recorded Event with Wide-Area Backup Protection in Operation 165
5.6 Wide-Area Damping Control Utilizing HVDC Modulation 166
5.6.1 Research Background 166
5.6.2 Structure of WADC System Utilizing HVDC Modulation 168
5.6.3 Time Delay in the Control Loop and Its Countermeasures 170
5.6.4 Operational Experience of WADC 171
5.7 Monitoring and Assessment on Integrated Wind Farm 173
5.7.1 Intelligent Alarm for the Cascading Tripping of Wind Turbines 173
5.7.2 Event Analysis for Subsynchronous Interaction Between Wind Farms and AC Networks 173
5.7.3 Online Wide-Area SSR Monitoring for Wind Farms Integrated with Weak AC Networks 177
5.7.3.1 Synchronous Wide Frequency Range Measurement Unit 177
5.7.3.2 Online SSR Monitoring and Alarming 178
References 179
6 Identification of Signature Oscillatory Modes in ERCOT by Mining of Synchrophasor Data 180
6.1 Introduction 180
6.2 Data Mining for Oscillations on the ERCOT System 181
6.2.1 Sustained, Poorly Damped Oscillations 181
6.2.2 Rapid, Un- or Negatively Damped Oscillations 181
6.2.3 Data Mining for Oscillations 182
6.2.4 Phasor Data Mining Tool 183
6.2.5 Post-processing 184
6.2.6 Mode Identification 184
6.3 Metrics for Classification of Oscillations 185
6.3.1 Monthly Highest Energy 185
6.3.2 Monthly Mode Occurrence 186
6.4 Generalized Approach for Identification of Signature Oscillations in a System 186
6.5 Illustration on the ERCOT System 187
6.5.1 0.9 Hz, 2.7 Hz—Related to Wind Production 189
6.5.2 3.2 Hz—Related to Control System Settings Changes 191
6.5.3 5.0, 5.4, and 6.0 Hz—Related to Control Systems 193
6.5.4 Summary 194
6.6 Conclusion 195
References 196
7 Oscillation Detection in Real-Time Operations at ERCOT 197
7.1 Introduction 197
7.2 The ERCOT Phasor Measurement Task Force [8] 198
7.3 Detection of Oscillations in Real Time 199
7.3.1 Controller Parameter Degradation 199
7.3.2 Weak Grid Oscillations 201
7.3.3 Controller Parameter Settings 202
7.4 Conclusions 203
References 204
8 Oscillation Detection and Mitigation Using Synchrophasor Technology in the Indian Power Grid 205
8.1 Introduction 205
8.2 Cases of Low-Frequency Oscillation in the Indian Grid and Measures Taken/Required for Their Improved Damping 207
8.3 Conclusion 225
References 226
9 Experiences of Oscillation Detection and Mitigation in Grid Operations at PEAK Reliability 227
9.1 Introduction 227
9.2 Overview of Montana Tech MAS Tool 229
9.2.1 Mode Meter Functionality 229
9.3 Peak’s Experience with MAS Mode Meters (MMM) 234
9.4 Forced Oscillation Detection and Analysis at PEAK 241
9.5 Conclusion 264
Acknowledgements 265
References 265
10 Online Oscillations Management at ISO New England 267
10.1 Introduction 267
10.2 Practically Observed Sustained Oscillations in Power Systems 267
10.3 Mitigation of Oscillations 270
10.4 Locating the Source of Sustained Oscillations 272
10.4.1 Methods for Locating the Source of Oscillations 273
10.4.2 Magnitude of Oscillations as an Indicator of the Source Location 275
10.4.3 The Dissipating Energy Flow Method 276
10.4.3.1 Original Energy-Based Method 276
10.4.3.2 Challenges in Actual Power Systems 277
10.4.3.3 Modification of the Method for Use with Actual PMU Data 277
10.4.4 Testing the Oscillation Source Locating Methods 280
10.5 Testing the Dissipation Energy Flow Method 281
10.5.1 Simulated Cases 281
10.5.2 Actual Events in ISO-NE System 283
10.5.3 Actual Events in WECC 285
10.5.4 Impact of the Energy-Based Method Assumptions 286
10.6 Installation of PMUs for Locating the Source of Forced Oscillations 288
10.7 Online Oscillation Management at ISO-NE 288
10.8 Online Monitoring of the Generator Damping Contribution 290
10.9 Conclusions 291
References 292
11 Operational Use of Synchrophasor Technology for Power System Oscillations Monitoring at California ISO 294
11.1 Introduction 294
11.2 Synchrophasor Data Gathering Architecture at Caiso 296
11.3 Oscillations Monitoring and Examples of Power System Oscillation Events Observed at Caiso 297
11.3.1 Voltage Oscillations at 500 KV Station March, 2016 299
11.3.2 High-Frequency Solar Plant Local Oscillation in SCE Area Near Devers on April 25, 2014 300
11.3.3 Local Oscillations at Pacific DC Intertie (PDCI) Station in Oregon in Jan, 2008—Operating Logs 301
11.4 Potential Future Work 304
Acknowledgements 305
References 305
12 Operational Use of Synchrophasor Technology for Wide-Area Power System Phase Angle Monitoring at California ISO 306
12.1 Introduction and Chapter Overview 306
12.2 Basic Principles of Phase Angle Monitoring and Implementation Overview at CAISO 307
12.3 Examples of Using Angle Monitoring in Operations 309
12.3.1 Validation of State Estimation Results in Certain Portions of the Network 309
12.3.2 Using Phasor Data for Situational Awareness to Allow for Safe Switching 311
12.3.3 Operator Displays 312
12.4 Potential Future Work 313
Acknowledgements 313
References 314
13 Synchrophasor-Based Linear State Estimation Techniques and Applications 315
13.1 Introduction to Synchrophasor-Based State Estimation Analytics 315
13.1.1 The Service that State Estimators Provide to the Analytics Pipeline 317
13.1.2 Considerations Regarding Network Observability with Synchrophasors 317
13.1.3 Considerations Regarding Topology Processing 320
13.2 Static Weighted Least Squares Linear State Estimation Techniques 321
13.2.1 Positive Sequence Linear WLS State Estimation 321
13.2.2 Three-Phase Linear WLS State Estimation 323
13.2.3 Implementation History 324
13.3 Dynamic State Estimation Techniques 325
13.3.1 Robust Linear Phasor-Assisted Dynamic State Estimation 326
13.3.2 Least Absolute Value Linear State Estimator 326
13.3.3 UKF-Based Dynamic State Estimation 327
13.3.4 Implementation of TRODSE 329
13.4 Applications 331
13.4.1 Load Modeling 331
13.4.1.1 Exponential Dynamic Load Model 332
13.4.1.2 Proposed Approach 332
13.4.1.3 Implementation of the Proposed Approach with Historical Field Measurements 333
13.4.2 Failure Detection of the Synchronous Generator Excitation Systems 335
13.4.2.1 Multiple Model Estimation Technique 337
13.4.2.2 Proposed Approach for the Detection of Exciter Failure in Dynamic State Estimation 337
13.4.3 Post-estimation Symmetrical Component Computation 339
13.5 Conclusion 340
References 341
14 Implementation of Synchrophasor-Based Linear State Estimator for Real-Time Operations 342
14.1 Introduction 342
14.2 Synchrophasor-Based Linear State Estimator: Theory 343
14.2.1 Traditional State Estimation Algorithm 343
14.2.2 Linear State Estimation Algorithm 345
14.3 Synchrophasor-Based Linear State Estimator: Implementation 346
14.3.1 LSE Integration 346
14.3.1.1 Network Model Integration 347
14.3.1.2 PMU Data Mapping 347
14.3.1.3 Real-Time Topology Update 348
14.3.2 LSE Application Components 348
14.3.2.1 Topology Process 348
14.3.2.2 Real-Time Observability Analysis 348
14.3.2.3 LSE Matrix Formulation 350
14.3.2.4 Bad Data Detection and Identification 351
14.3.3 LSE Operation Procedure 353
14.4 Linear State Estimator Use Cases at Utilities 355
14.4.1 Synchrophasor Data Validation and Conditioning 356
14.4.1.1 Validation and Conditioning Historical Event Field PMU Data 357
14.4.1.2 Validation and Conditioning Real-Time Field PMU Data 359
14.4.2 Independent Wide-Area Situational Awareness for Grid Resiliency and Synchrophasor Data Analytics 360
14.4.2.1 Wide-Area Situational Awareness 361
14.4.2.2 Phase Angle Difference for Grid Stress Monitoring 362
14.4.2.3 Oscillation Analysis and Monitoring 363
14.4.3 Extend Synchrophasor Measurement Coverage and Benefits for Potential Downstream Applications 364
14.4.3.1 Methodology and Deployment at SCE 364
14.4.3.2 Expanded Observability for Line Closing 366
14.4.3.3 Expanded Observability for Remedial Action Scheme (RAS) Testing 366
14.4.3.4 Expanded Observability for Operator Training 367
14.4.4 Performance Assessment 367
14.5 Conclusions 368
References 368
15 Post-event Analysis in the ERCOT System Using Synchrophasor Data 370
15.1 Introduction 370
15.2 Post-event Analysis 371
15.2.1 System Frequency 372
15.2.2 Voltage Magnitude Swings 373
15.2.3 Voltage Angle Swings 374
15.2.4 Oscillation Modes 376
15.3 Case Study 1—Loss of Generation Event 376
15.3.1 System Frequency 376
15.3.2 Voltage Magnitude Swings 377
15.3.3 Voltage Angle Swings 379
15.3.4 Oscillation Modes 380
15.4 Case Study 2—Compound Event Inducing a Loss of Generation Event 383
15.4.1 The Power Load Unbalance (PLU) Relay 384
15.4.2 System Frequency 385
15.4.3 System Phase-to-Ground Fault 385
15.4.4 Loss of Generation 386
15.5 Conclusions 389
References 389
16 Validation and Tuning of Remedial Action Schemes in Indian Grid Operations Using Synchrophasor Technology 391
16.1 Introduction 391
16.2 SPS Performance Evaluation 393
16.3 WAMS in Indian Grid 394
16.4 Monitoring of SPS Using Synchrophasors 395
16.5 Case Studies 396
16.6 Conclusion 405
References 406
17 Indian Power System Operation Utilizing Multiple HVDCs and WAMS 408
17.1 Introduction 408
17.2 Conclusions/Way Forward 436
References 437
18 Model Validation Using Synchrophasor Technology 438
18.1 Introduction 438
18.2 Plant Model Validation 439
18.2.1 System Model Validation 442
18.2.2 Benchmarking System Interface 445
18.2.3 Benchmarking Frequency Response of Interconnection 447
18.3 Conclusions 451
References 451
19 A Software Suite for Power System Stability Monitoring Based on Synchrophasor Measurements 453
19.1 Introduction 453
19.2 GSAS Architecture 454
19.2.1 Design Consideration 454
19.3 System Architecture 455
19.4 GSAS Stability Monitoring Modules 457
19.4.1 Oscillation Monitoring Tool 457
19.4.2 Voltage Stability Monitoring Tool 458
19.4.3 Transient Instability Monitoring Tool 459
19.4.4 Angle Difference Monitoring Tool 460
19.4.5 Event Detection Tool 460
19.5 GSAS Alarming 461
19.5.1 GSAS Alarming Mechanisms 461
19.5.2 GSAS Alarming Dashboard 461
19.5.3 GSAS Alarming Logs 463
19.6 Off-line Validation of GSAS Performance 463
19.6.1 Overview 463
19.6.2 Procedure and Methodology for Off-line Performance Validation 465
19.6.2.1 Validation Procedure 465
19.6.2.2 Validation Cases 465
19.6.2.3 Testing Approach 466
19.6.2.4 Six General Cases 467
19.6.3 Performance of Voltage Stability Monitoring Tool 467
19.6.3.1 Overview of Voltage Stability Analysis Method 467
19.6.3.2 Results of Validations Based on PSS/E Simulations 469
19.6.4 Performance of Oscillation Monitoring Tool 472
19.6.4.1 Overview of Oscillation Analysis Method 472
19.6.4.2 Results of Off-line Validation Based on PSS/E Simulations 473
19.6.5 Performance of Transient Stability Monitoring Tool 474
19.6.5.1 Overview of Transient Stability Monitoring Method 474
19.6.5.2 Results of Off-line Validation Based on Simulations 476
19.7 Conclusions 478
References 479
20 A Cloud-Hosted Synchrophasor Data Sharing Platform 481
20.1 Introduction 481
20.1.1 Synchrophasor System at ISO New England 482
20.1.2 PMU Data Exchange with External Regions 483
20.1.3 Problem Statement of the Existing Implementation of PMU Data Exchange 484
20.2 Why the Cloud? 486
20.2.1 Overview of Cloud Computing Technology 486
20.2.2 GridCloud Platform 489
20.2.2.1 The GridCloud Security Architecture 490
20.2.2.2 The GridCloud Data Collection Layer 490
20.2.2.3 The GridCloud Archival Data Storage Subsystem 490
20.2.2.4 Cloud Manager 491
20.3 Proof-of-Concept Cloud-Hosted Wide Area Monitoring System 491
20.3.1 Conceptual Overview of the Cloud-Hosted Synchrophasor Platform 492
20.3.2 Proof-of-Concept Experiment Setup 494
20.3.3 Experiment Findings 495
20.3.3.1 Security 495
20.3.3.2 Latency 496
20.3.3.3 Data Consistency and Fault Tolerance 497
20.3.3.4 Operational Cost 500
20.4 Challenges and Future Research Directions 500
References 501
Index 503