Phasor measurement units (PMUs) provide time synchronized measurements of phase angles of voltages or currents. These synchrophasors are used in electricity grids to support planning and operation. In the past decade, transmission network planners and grid operators have been using PMUs for monitoring applications. Improvements are still needed to realise the full potential of PMU data for more complex power systems with distributed renewables and increased loads. Challenges related to noise and higher frequency components from various dynamic behaviours of grid components, including electric vehicles, are prevalent.
This book conveys the technology of PMUs and the application of synchrophasors to wide-area networks and interconnected grids. The chapters cover synchrophasor-based applications and technologies, including monitoring of power oscillation, wide area control and predictive protection, congestion management in transmission networks, synchrophasor-based fault location for multi-terminal transmission systems, system integrity protection, and application of synchrophasor technology to microgrids.
This book is important reading for researchers and engineers in academia and industry, and grid operators involved with power systems and renewable energy generation, networks and communication.
Author(s): Nand Kishor, Soumya R. Mohanty
Series: IET Energy Engineering Series, 190
Publisher: Institution of Engineering and Technology
Year: 2023
Language: English
Pages: 270
City: London
Cover
Contents
About the editors
Introduction
1 Synchrophasor data for oscillation source location
1.1 Review of OSL methods
1.1.1 Traveling wave-based methods
1.1.2 Mode shape-based methods
1.1.3 Energy-based methods
1.1.4 Artificial intelligence-based methods
1.1.5 Model inference-based methods
1.1.6 Purely data-driven methods
1.1.7 Other methods
1.1.8 OSL methods for inverter-based resources
1.2 Practical considerations for implementing OSL methods
1.3 Studies on dissipating energy flow method
1.3.1 A brief introduction of DEF
1.3.2 Study of idealized longitudinal power systems
1.3.3 Study of multiple cases from IEEE-NASPI OSL contest
1.4 Conclusion and future work
References
2 Real-time testing of smart-WAMS for the monitoring of power oscillation
2.1 Introduction
2.2 State-of-the-art
2.3 Brief on smart-WAMS
2.4 Executed tests and experiments
2.4.1 NSGL Opal-RT computer platform: subtask 1
2.4.2 Non-Opal-RT laptop platform: subtask 2
2.5 Data communication and storage
2.6 Results
2.6.1 PMU signal from Nordic grid: test case 1
2.6.2 Simulated signal from IEEE-39 bus system: test case 2
2.6.3 Offline PMU signal from NASPI: test case 3
2.7 Conclusions
Acknowledgment
References
3 Wide-area control design in different aspects of oscillations
3.1 Introduction
3.1.1 WAC applications in renewable integrated power system
3.1.2 WAC challenges
3.2 WAC methodology
3.2.1 Resilient WAC design
3.2.2 Wide-area-time-delayed system model
3.3 Wide-area-predictive-control approach
3.3.1 Reordering of data frames and data loss compensation at the PDC
3.3.2 Computational method at the predictive controller
3.3.3 Control horizon setting at WAPC
3.3.4 Case study
3.4 Sub-synchronous oscillation damping in transmission network using synchrophasor technology
3.4.1 Challenges of SSO in WF-integrated transmission networks
3.4.2 Application of synchrophasor technology for SSO damping in WF-integrated transmission networks
3.4.3 Case study
3.5 SDC methodology to improve PFR in WF-integrated transmission systems
3.5.1 Synchronized P–f droop control of grid-integrated WFs
using synchrophasor technology
3.5.2 Case study
3.6 Voltage oscillation damping in WF-integrated transmission network using synchrophasor technology
3.6.1 Synchronized Q–V droop control of grid-integrated
WFs using synchrophasor technology
3.6.2 Case study
3.7 Conclusions
References
4 Power oscillation damping control for inter-area mode in reduced order model
4.1 Introduction
4.2 Small signal stability: eigenvalue analysis of NRPG system
4.3 Review on residue approach, conventional PSS and compensation associated with PSS design
4.3.1 Residue approach for location of PSS
4.3.2 Conventional PSS: basics
4.3.3 Compensation using PVr characteristics
4.4 Model order reduction of NRPG system
4.5 PSS design for NRPG system
4.6 Case study for investigating PSS44 and PSS15 performance
4.7 Conclusions
References
5 Real-time congestion management in transmission networks
5.1 Thermal analysis of transmission lines
5.2 Conventional methods for congestion control
5.3 Real-time congestion control by strategic power trade with electric vehicles
5.3.1 Control model and PMU deployment
5.3.2 Proof
5.3.3 Test results
5.4 Model predictive control-based generation re-dispatch for real-time congestion management
5.4.1 System modeling
5.4.2 Stability analysis
5.4.3 Test results and discussion
5.5 Appendix
5.5.1 Generator modeling
5.5.2 Governor-turbine model
5.5.3 BESS
5.5.4 Generation shift factor
5.5.5 Stability proof of MPC
References
6 Out-of-step predictive wide-area protection
6.1 Introduction
6.2 Analytic prediction vs. artificial intelligent-based prediction
6.3 Prediction of power and angle curves by time series modeling
A. Time series modeling using ARMA
B. Estimation by extended Kalman filtering algorithm
C. Estimation of ARMA model parameters by EKF
D. Prediction of power and angle curves
6.4 Proposed OOS prediction algorithm
6.4.1 General description
6.4.2 Details
6.5 Simulation results
6.5.1 Case 1: single-machine system
6.5.2 Case 2: multi-machine two-area system
6.5.3 Case 3: multi-machine multi-area system
6.6 Conclusion
References
7 Fault location algorithm for multi-terminal transmission system
Nomenclature
7.1 Introduction
7.2 Derivation of fault location equations for multi-terminal networks
7.2.1 Derivation for asymmetrical fault location for fault on a main section which have sources at their ends-PU section
7.2.2 Generalized asymmetrical fault location equations for sections which does not have source ends
7.2.3 Generalized symmetrical fault location equations for faults which occurred at source ends
7.2.4 Generalized symmetrical fault location equations for faults that occurred at sections which does not have sources
7.3 Application of the proposed methodology on four-terminal network
7.3.1 Asymmetrical fault location for faults on PU section
7.3.2 Symmetrical fault location for faults on PU section
7.4 Charging current compensation
7.5 Procedure for fault identification and fault location estimation for four-terminal network
7.6 Results
7.6.1 Fault section identification
7.6.2 Fault location estimation
7.6.3 Synchronization error
7.6.4 Effect of mutual coupling
7.6.5 Effect of bad data
7.6.6 Communication failure
7.7 Conclusion
7.8 Appendix
7.8.1 Asymmetrical fault location for faults on QU section
7.8.2 Asymmetrical fault location for faults on RT section
7.8.3 Asymmetrical fault location for faults on ST section
7.8.4 Asymmetrical fault location for faults on TU section
7.8.5 Symmetrical fault location for faults on QU section
7.8.6 Symmetrical fault location for faults on RT section
7.8.7 Symmetrical fault location for faults on ST section
7.8.8 Symmetrical fault location for faults on TU section
References
8 System integrity protection schemes for future power systems
8.1 Overview of SIPS
8.1.1 SIPS classification
8.1.2 SIPS design consideration
8.2 Methods of SIPS implementation
8.2.1 Power system monitoring for SIPS
8.2.2 Relay operations in SIPS
8.2.3 Power system control approaches in SIPS
8.3 SIPS: operational experience
8.4 Challenges with SIPS for future power systems
8.4.1 Challenges in future power system monitoring
8.4.2 Challenges in future power system protection
8.4.3 Challenges in future power system control
8.5 SIPS reliability and testing requirements for future power systems
8.6 Conclusions
References
9 Application of synchrophasor technology for microgrid protection
9.1 Introduction
9.2 Basic operation
9.3 Application of PMU for microgrid protection
9.3.1 Microgrid protection
9.3.2 PMU-assisted microgrid protection layout
9.4 Literature survey
9.5 Future research scope
9.6 Summary
References
Index
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