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 [23] states that state estimator MVA mismatch should be less than 10 MVA. He does not distinguish between internal and external systems.
1.4.4.1 Metrics to Evaluate SE Solution Quality
More than one metric is used to evaluate the accuracy of the results of the state estimator solution:
Cost index is also referred to as “performance index” or “quadratic cost.” In general, it measures the sum of the squares of the normalized estimate errors (residuals). Increasing cost index values could indicate deteriorating state estimator solution quality. This is the most commonly used indicator, whose values range between 45 and 58%.
Chi‐squared criterion is the second most used, and its value ranges between 36 and 42%.
Measurement error/bias analysis is used as a performance indicator.
Average residual value is used as a performance indicator.
The reliability entity should track the selected metric over time to establish the pattern and determine what indicates a problem with state estimator solution quality. Deviation from the “normal range” of these metrics should trigger state estimator maintenance and support. These metrics are important because they could affect the CA solution.
Many factors affect SE solution‐quality metrics such as:
1 Electrical device modeling, connectivity, and telemetry data mapping. If the topology is incorrect, the state estimator may not converge or may yield grossly incorrect results. A topology error may be caused by either inaccurate status of breakers and switching devices or errors in the network model.
2 Availability and quality of telemetry data. Telemetry data are essential components of the state estimation process.
3 Inadequate observability. State estimation is extended to the unobservable parts of the network through the addition of pseudo‐measurements that are computed based on load prediction using load distribution factors, or they can represent non‐telemetered generation assumed to operate at a base‐case output level. The quality of pseudo‐measurements may be bad if they are not updated regularly to reflect current conditions.
4 Measurement redundancy of the network is defined as the ratio of the number of measurements to the number of state variables in the observable area of the network.
1.4.4.2 Methods for Evaluating SE Solution Quality (Accuracy)
The following methods are used to evaluate the accuracy of the state estimator results:
Continually monitor and minimize the amount of bad data detected by correcting model, telemetry, and bad status.
Compare critical telemetry with the state estimator solution (ties, major lines, large units, etc.).
Use measurement error/bias analysis to detect and resolve telemetry and model problems.
Periodically
