1. Introduction. 1.1 Background. 1.2 Electrical Transmission Networks. 1.3 Conventional Control Mechanisms. 1.4 Flexible ac Transmission Systems (FACTS). 1.5 Emerging Transmission Networks. 2. Reactor--Power Control in Electrical Power Transmission Systems. 2.1 Reacrive Power. 2.2 Uncompensated Transmission Lines. 2.3 Passive Compensation. 2.4 Summary. 3. Principles of Conventional Reactive--Power Compensators. 3.1 Introduction. 3.2 Synchronous Condensers. 3.3 The Saturated Reactor (SR). 3.4 The Thyristor--Controlled Reactor (TCR). 3.5 The Thyristor--Controlled Transformer (TCT). 3.6 The Fixed Capacitor--Thyristor--Controlled Reactor (FC--TCR). 3.7 The Mechanically Switched Capacitor--Thristor--Controlled Reactor (MSC--TCR). 3.8 The Thyristor--Switched capacitor and Reactor. 3.9 The Thyristor--Switched capacitor--Thyristor--Controlled Reactor (TSC--TCR). 3.10 A Comparison of Different SVCs. 3.11 Summary. 4. SVC Control Components and Models. 4.1 Introduction 4.2 Measurement Systems. 4.3 The Voltage Regulator. 4.4 Gate--Pulse Generation. 4.5 The Synchronizing System. 4.6 Additional Control and Protection Functions. 4.7 Modeling of SVC for Power--System Studies. 4.8 Summary. 5. Conceepts of SVC Voltage Control. 5.1 Introduction 5.2 Voltage Control. 5.3 Effect of Network Resonances on the Controller Response. 5.4 The 2nd Harmonic Interaction Between the SVC and ac Network. 5.5 Application of the SVC to Series--Compensated ac Systems. 5.6 3rd Harmonic Distortion. 5.7 Voltage--Controlled Design Studies. 5.8 Summary. 6. Applications. 6.1 Introduction. 6.2 Increase in Steady--State Power--Transfer Capacity. 6.3 Enhancement of Transient Stability. 6.4 Augmentation of Power--System Damping. 6.5 SVC Mitigation of Subsychronous Resonance (SSR). 6.6 Prevention of Voltage Instability. 6.7 Improvement of HVDC Link Performance. 6.8 Summary. 7. The Thyristor--Controlled SeriesCapacitor (TCSC). 7.1 Series Compensation. 7.2 The TCSC Controller. 7.3 Operation of the TCSC. 7.4 The TSSC. 7.5 Analysis of the TCSC. 7.6 Capability Characteristics. 7.7 Harmonic Performance. 7.8 Losses. 7.9 Response of the TCSC. 7.10 Modeling of the TCSC. 7.11 Summary. 8. TCSC Applications. 8.1 Introduction. 8.2 Open--Loop Control. 8.3 Closed--Loop Control. 8.4 Improvement of the System--Stability Limit. 8.5 Enhancement of System Damping. 8.6 Subsynchronous Resonanace (SSR) Mitigation. 8.7 Voltage--Collapse Prevention. 8.8 TCSC Installations. 8.9 Summary. 9. Coordination of FACTS Controllers. 9.1 Introduction 9.2 Controller Interactions. 9.3 SVC--SVC Interaction. 9.4 SVC--HVDC Interaction. 9.5 SVC--TCSC Interaction. 9.6 TCSC--TCSC Interaction. 9.7 Performance Criteria for Damping--Controller Design. 9.8 Coordination of Multiple Controllers Using Linear--Control Techniques. 9.9 Coordination of Multiple Controllers using Nonlinear--Control Techniques. 9.10 Summary. 10. Emerging FACTS Controllers. 10.1 Introduction. 10.2 The STATCOM. 10.3 THE SSSC. 10.4 The UPFC. 10.5 Comparative Evaluation of Different FACTS Controllers. 10.6 Future Direction of FACTS Technology. 10.7 Summary. Appendix A. Design of an SVC Voltage Regulator. A.1 Study System. A.2 Method of System Gain. A.3 Elgen Value Analysis. A.4 Simulator Studies. A.5 A Comparison of Physical Simulator results With Analytical and Digital Simulator Results Using Linearized Models. Appendix B. Transient--Stability Enhancement in a Midpoint SVC--Compensated SMIB System. Appendix C. Approximate Multimodal decomposition Method for the Design of FACTS Controllers. C.1 Introduction. C.2 Modal Analysis of the ith Swing Mode, C.3 Implications of Different Transfer Functions. C.4 Design of the Damping Controller. Appendix D. FACTS Terms and Definitions. Index.

Thyristor-Based Facts Controllers for Electrical Transmission Systems

This paper summarizes the technical activities of the Task Force on Power System Dynamic State and Parameter Estimation. This Task Force was established by the IEEE Working Group on State Estimation Algorithms to investigate the added benefits of dynamic state and parameter estimation for the enhancement of the reliability, security, and resilience of electric power systems. The motivations and engineering values of dynamic state estimation (DSE) are discussed in detail. Then, a set of potential applications that will rely on DSE is presented and discussed. Furthermore, a unified framework is proposed to clarify the important concepts related to DSE, forecasting-aided state estimation, tracking state estimation, and static state estimation. An overview of the current progress in DSE and dynamic parameter estimation is provided. The paper also provides future research needs and directions for the power engineering community.

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Power System Dynamic State Estimation: Motivations, Definitions, Methodologies, and Future Work

This paper describes an online dynamic security assessment scheme for large-scale interconnected power systems using phasor measurements and decision trees. The scheme builds and periodically updates decision trees offline to decide critical attributes as security indicators. Decision trees provide online security assessment and preventive control guidelines based on real-time measurements of the indicators from phasor measurement units. The scheme uses a new classification method involving each whole path of a decision tree instead of only classification results at terminal nodes to provide more reliable security assessment results for changes in system conditions. The approaches developed are tested on a 2100-bus, 2600-line, 240-generator operational model of the Entergy system. The test results demonstrate that the proposed scheme is able to identify key security indicators and give reliable and accurate online dynamic security predictions.

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An Online Dynamic Security Assessment Scheme Using Phasor Measurements and Decision Trees

Prony analysis has proven to be a valuable tool in estimating the modal content of power oscillations from measured ringdowns. The accuracy of the mode estimates is limited by the noise content always found in field measured signals. Current Prony analysis methods assume the system to be single output, and individual signals are analyzed independently often resulting in conflicting frequency and damping estimates (due to noise effects). This paper considers a simple extension to Prony analysis that allows multiple signals to be analyzed simultaneously resulting in one set of mode estimates. Examples are used to show that this extension improves the accuracy of modal estimates and simplifies the analysis steps. The first example uses a Monte Carlo type simulation model and the second analyzes field measured data from the western North American power system.

Making Prony analysis more accurate using multiple signals

In this paper, the authors use the Numerical algorithm for Subspace State Space System IDentification (N4SID) to extract dynamic parameters from phasor measurements collected on the western North American Power Grid. The data were obtained during tests on June 7, 2000, and they represent wide area response to several kinds of probing signals, including low-level pseudo-random noise (LLPRN) and single-mode square wave (SMSW) injected at the Celilo terminal of the Pacific HVDC Intertie (PDCI). An identified model is validated using a cross validation method. Also, the obtained electromechanical modes are compared with the results from Prony analysis of a ringdown and with signal analysis of ambient data measured under similar operating conditions. The consistent results show that methods in this class can be highly effective, even when the probing signal is small

Initial results in power system identification from injected probing signals using a subspace method

The frequency and damping of electromechanical modes offer considerable insight into the dynamic stability properties of a power system. The performance properties of three mode-estimation block-processing algorithms from the perspective of near real-time automated stability assessment are demonstrated and examined. The algorithms are: the extended modified Yule Walker (YW); extended modified Yule Walker with spectral analysis (YWS); and sub-space system identification (N4SID). The YW and N4SID have been introduced in previous publications while the YWS is introduced here. Issues addressed include: stability assessment requirements; automated subset selecting identified modes; using algorithms in an automated format; data assumptions and quality; and expected algorithm estimation performance.

Performance of Three Mode-Meter Block-Processing Algorithms for Automated Dynamic Stability Assessment

The authors present an introduction to concepts and applications of transfer function identification in power systems. They begin with a brief introduction to transfer function identification methods using least-squares approaches and then discuss applications which include static VAR compensators, model validation applications, and software validation. A comparison is also made between eigenvalues obtained from transfer function identification and small signal analysis. Methods for testing the validity of identified transfer functions are also discussed. >

J.F. Hauer

Papers

Making Prony analysis more accurate using multiple signals

Initial results in power system identification from injected probing signals using a subspace method

Performance of Three Mode-Meter Block-Processing Algorithms for Automated Dynamic Stability Assessment

Transfer function identification in power system applications

Identification of Electromechanical Modes in Power Systems