determination of system operating limits based on a recent snapshot of the real‐time system. VSA may derive minimum voltages at key buses below which voltage collapse might occur if the system experiences additional stresses. It may also provide information on minimum dynamic reactive reserves required in local areas.
VSA is different from offline voltage stability analysis tools used by planners for medium‐term or long‐term studies. But if such tools are used for studying near real‐time snapshots to answer operator's voltage stability questions, they would be included in the VSA real‐time tool suite.
1.5.6 Dynamic Stability Assessment
The dynamic stability assessment (DSA) application (or a suite of applications) executes in near real time to assist in determining stability‐related system operating limits using a snapshot of the real‐time system (i.e. current state estimator output). It may also provide an indication of dynamic stability margin for the most critical fault/contingency condition.
DSA is different from offline stability analysis tools usually used for medium‐ or long‐term studies [37–39]. However, if such tools are used by operators studying near real‐time snapshots to aid in voltage stability evaluation, these tools should be included as part of the DSA suite.
For more background material, the reader is referred to [40–42].
1.6 OVERVIEW OF CHAPTERS
Apart from this introductory chapter, there are 13 chapters devoted to state of the art in this vibrant area.
In Chapter 2, Eduardo Caro and Araceli Hernández discuss a mathematical programming approach to state estimation in power systems. They focus on WLS, least absolute value (LAV), quadratic constant, and quadratic linear criterions, among others. Additionally, the statistical correlation among measurements is analyzed and included, enhancing both estimation accuracy and the bad data identification capabilities. All procedures are illustrated by simple but insightful examples.
From a computational perspective, quadratic constant and LAV techniques perform faster than the conventional WLS estimator, saving up to 75% CPU time (compared with the WLS method directly solved as an optimization problem). On the other hand, mathematical programming formulation of some estimators (such as least median of squares and least trimmed of squares approaches) encounter non‐convexities and a significant number of binary variables, resulting in higher computational burdens.
If the measurement set is corrupted with errors that the χ2 test cannot detect, WLS estimation results quality deteriorates, providing the worst estimation quality (if LMS and LTS approaches are not considered). It is observed that QL and QC techniques outperform the rest of estimators, followed by LMR procedure. As expected, the authors report that their computational experiments reported in this chapter suggest that alternative estimators are potential substitutes for traditional WLS method. The state of the art of current nonlinear optimization solvers and recent advances in computational equipment allows using robust estimators in real electric energy systems.
Chapter 3 draws on two bodies of knowledge – electric power engineering and network (graph) theory – to develop and apply a new failure network, an application of line outage distribution factors. Here Hyde M. Merrill and James W. Feltes present an approach to measure how susceptible is an electric power system to cascading outages (stress) that lead to blackouts. The problem is defined in the context of a new perspective of the electric power system. A failure network is defined, based on well‐known line outage distribution factors. Following the practice of network (graph) theory, the structure and properties of this network are analyzed with metrics that measure stress (susceptibility to cascading outages). The metrics can be applied in real‐time operations or in planning to identify vulnerability to cascading. Three studies are described, two on very large North American systems, the other on the smaller national system of Peru. New insights are presented, and a new class of power system options is identified, to reduce susceptibility to cascading rather than to increase transfer capability.
The ideas have application in planning as well as in real time. For operations, it depends, as Schweppe expected, on data from a state estimator. But the additional computations go far beyond simply calculating flows and injections using Ohm's and Kirchhoff's laws.
The authors of Chapter 4, “Model‐Based Anomaly Detection for Power System State Estimation”, Aditya Ashok, Manimaran Govindarasu, and Venkataramana Ajjarapu, recognize that state estimators depend on SCADA measurements from the various remote substations, which introduces several vulnerabilities due to malicious cyberattacks. The security and resiliency of the power system state estimator are important since its output is used by several other network applications in the EMS such as real‐time CA, power markets, etc. While SE is designed to detect and recover from some degree of bad data injected due to measurement errors, or even measurement loss due to telemetry issues, they could be impacted by malicious cyberattacks causing loss of observability, operational, and market impacts. A holistic approach to attack‐resilient SE should involve a combination of attack‐resilient planning approaches to improve attack prevention capabilities in conjunction with attack‐resilient anomaly detection and robust SE formulations to improve attack detection and mitigation resulting in a defense‐in‐depth architecture.
This chapter starts with a broad survey of relevant state‐of‐the‐art literature that addresses the vulnerability of SE to stealthy false data injection attacks and topology‐based attacks. This is followed by a review of some offline attack prevention approaches to enhance the redundancy of SE against those stealthy cyberattacks and online techniques for attack detection and mitigation that address anomaly detection, bad data detection, and other formulations of SE. A model‐based anomaly detection approach uses short‐term load forecasts, generation schedules, and available secure PMU data to detect anomalies due to stealthy cyberattacks. This approach is complementary to traditional bad data detection methods in SE to detect stealthy cyberattacks. This chapter offers some insights into the performance of the proposed anomaly detection approach using a case study on the IEEE 14‐bus system. Finally, this chapter provides a summary of the contributions and promising directions for emerging research topics that show promise for the future including PMU‐based linear state estimator, integrated hybrid SE formulations with PMU data and SCADA, robust and dynamic SE formulations, and MTD‐based approaches for SE that leverage redundancy and randomization of measurements.
A. P. Sakis Meliopoulos, Yu Liu, Sungyun Choi, and George J. Cokkinides propose in Chapter 5 a scheme for real‐time operation and protection of microgrids based on distributed dynamic state estimation (DDSE). First, the DDSE can be used for setting‐less component protection that applies dynamic state estimation on a component under protection with real‐time measurements and dynamic models of the component. Based on the results, the well‐known χ2 test yields the confidence level that quantifies the goodness of fit of models to measurements, indicating the health status of the component. With this approach, renewable DERs in microgrids can be protected on an autonomous and adaptive basis. Meanwhile, the estimated state variables of each component are converted to phasor data with time tags and then collected to the DERMS of microgrids. These aggregated phasor data that are once filtered by the DDSE are input to the static state estimator in the DERMS along with unfiltered data sent from conventional meters, relays, and digital fault recorders, ultimately generating real‐time operating conditions of microgrids. This chapter also provides numerical simulations to compare the DDSE‐based approach with conventional centralized state estimation in terms of data accuracy and computational speeds.
Any component in microgrids can be protected by the proposed setting‐less protection method, capable of tracking full dynamic characteristics of a device under protection. This method can provide adaptive protection in microgrids, where unpredictable fault conditions or abnormal states may arise. It is