with the highest values of the weighted residual. Newer bad data identification techniques use both the weighted residual and the normalized residual. Either the values of the normalized residual or the ratios of the normalized ones from the weighted residual are used to identify bad data. Bad data rejection is a time‐consuming procedure at control centers especially if there are more than one measurement in error.
The concept of security control cannot be fully realized without a complete information base that is derived from the voltage magnitudes and phase angles of all buses in a power system obtained via state estimation [1–3].
1.4 STATIC STATE ESTIMATION IN PRACTICE
The material in this section is based on a NERC Task Force report [7]. To quote the Task Force:
This report presents the findings and recommendations of the North American Electric Reliability Corporation (NERC) Real‐Time Tools Best Practices Task Force (RTBPTF) concerning minimum acceptable capabilities and best practices for real‐time tools necessary to ensure reliable electric system operation and reliability coordination. RTBPTF's undertaking is based on the U.S.‐Canada Power System Outage Task Force findings that key causes of the August 14, 2003 northeast blackout included absence of situational awareness and inadequate reliability tools. That report also notes the need for visualization display systems to monitor system reliability.
RTBPTF's recommendations result from an extensive, three‐year process of fact‐finding and analysis supported by the results of the Real‐Time Tools Survey, the most comprehensive survey ever conducted of current electric industry practices.
RTBPTF's findings and recommendations are firmly grounded in the results of the Real‐Time Tools Survey, a more than 300‐page, web‐based document with nearly 2,000 questions on a broad scope of current industry practices and plans for using real‐time tools.
While [21] referred to RTUs as the eyes and ears and hands of the master station, the phrase came to be commonly used to refer to the state estimator as the eyes and ears of the real‐time operator. Indeed, in current practice the state estimator prevails as an “essential” tool for power system operators' “situational awareness.” Existing NERC reliability standards assume the use of state estimators to aid RCs and TOPs in maintaining situational awareness for the bulk electric system. The state estimator must be available and able to produce an accurate solution because many applications rely on the state estimator solution as base case.
State estimators are commercially available allowing SCADA/EMS vendors to provide viable state estimators off the shelf with some customization and fully integrated with users' production SCADA/EMS systems. State estimators are used as input to monitor MVA/ampere loadings and low and high bus voltages, voltage drop, voltage node angle separation, SCADA, and visualization. Therefore, it is important that it be available and produce an accurate solution.
Single‐pass methods execute one estimation that simultaneously includes internal and external networks, observable/internal network, and nonobservable/external network solved together. The two‐pass method deals with two state estimates, one for the internal system and another for the external system or for the entire system (observable/internal network and nonobservable/external network solved separately). Many in the power industry prefer using a single‐pass over a two‐pass solution.
According to [22], the single‐pass method suffers from numerical instability. An enhancement to the one‐pass method uses a set of critical external pseudo‐measurements. Some alternative two‐pass state estimators require a load flow study for the external system. Both two‐pass methods reduce the effects of boundary errors in the internal system solution by properly weighing the external pseudo‐measurements, but they may result in very high or negative loads and generations in the external system. Zero‐injection buses are more commonly treated as high‐confidence bus injection measurements than as hard constraints.
For an overwhelming majority of users, the state estimator solution is used as a base case for reliability‐analysis applications such as contingency analysis (CA), power flow (PF), and as input to system analysis tools such as:
1 Online/operator PF
2 Offline PF
3 Locational marginal pricing (LMP)
4 Voltage stability analysis
5 Security‐constrained economic dispatch
In some cases, the state estimator is used primarily as the basis for information communicated to operators regarding power system status; e.g. the state estimator drives the alarm application that alerts operators to impending power system events.
1.4.1 SE Performance Issues
It is common practice to rely on periodic triggers to run state estimators every two minutes. Manual and SCADA events such as breaker trips and analog rates of change are also used. Moreover, the average state estimator execution time ranges from one second to two minutes (with an average of about 20 seconds).
It is difficult to recommend specific state estimator voltage and angle convergence tolerances because of the different algorithms employed by different state estimators and the way specific convergence parameters are used in these algorithms. For example, some state estimators check convergence based on changes of the absolute values of voltage magnitudes and voltage phase angles (relative to ground) between successive iterations.
Common industry practice for the voltage‐magnitude convergence‐tolerance criteria (per unit) is a maximum of 0.1 (0.01 kV per unit) for both internal/observable and external/unobservable systems. For the angle difference in radians, the tolerance is 0.0100.
1.4.2 Weights Assigned to Measurements
The state estimator requires measurement weights (confidences) that affect its solution. The weights for telemetered and non‐telemetered measurements are selected according to the following:
1 Use individually defined weights for at least some of the telemetered measurements used by the state estimators.
2 Use globally defined weights for at least some of the telemetered measurements used by the state estimators.
The basis for weights applied to at least some analog values used by the state estimator is either a generic percentage metering error or specific meter accuracies.
1.4.3 SE Availability Considerations
The state estimator must be highly available and must also be able to provide a reasonable, accurate, and robust solution that meets the purposes for which it is intended. Practitioners report that the average time during which state estimator solutions are unavailable is 15 minutes or less per outage for almost all users. In addition, unavailability of the state estimator for up to 30 minutes is considered as having no significant impact on system operations.
Having state estimator failures less than 30 minutes apart is perceived as having a “significant” impact on system operations. This however varies according to internal policies and market considerations.
1.4.4 SE Solution Quality (Accuracy)
State estimator availability requirements are complemented by solution‐quality requirements to ensure that operators are given accurate information allowing them to be fully aware of the system situation in a timely manner.
Operators report that they can detect and identify bad analog measurements and remove them from the state estimator measurement set. Users quantified the real/reactive power mismatch tolerance criteria for their internal/observable systems is in the 0.05 MW (per unit) – 170 MW real power mismatch tolerance range and a 0.001 Mvar (per unit) – 500 Mvar reactive power mismatch tolerance range. The average real and reactive mismatch tolerance criteria reported were 35 MW and 69.5 Mvar, respectively.
Macedo