The chapter starts by providing the measurement and process model of WLS and KF SE algorithms and continues with the analytical formulation of the two families of state estimators, including their linear and non-linear versions as a function of the type of available measurements. Finally, two case studies targeting IEEE transmission and distribution reference networks are given.

Chapter Contents:

• 8.1 State estimation measurement and process model
• 8.1.1 Measurement model
• 8.1.2 Network observability
• 8.1.3 Process model
• 8.2 Static state estimation: the weighted least squares
• 8.2.1 Linear weighted least squares state estimator
• 8.2.2 Non-linear weighted least squares
• 8.3 Recursive state estimation: the Kalman filter
• 8.3.1 Discrete Kalman filter
• 8.3.2 Extended Kalman filter
• 8.3.3 Kalman Filter sensitivity with respect to the measurement and process noise covariance matrices
• 8.3.4 Assessment of the process noise covariance matrix
• 8.4 Assessment of the measurement noise covariance matrix
• 8.5 Data conditioning and bad data processing in PMU-based state estimators
• 8.6 Kalman filter vs. weighted least squares
• 8.7 Numerical validation and performance assessment of the state estimation
• 8.7.1 Linear state estimation case studies
• 8.7.2 Non-linear SE case studies
• 8.8 Kalman filter process model validation
• 8.9 Numerical validation of Theorem 6.1
• Bibliography

Inspec keywords: power distribution; phasor measurement; power transmission; power system state estimation; power grids

Other keywords: IEEE transmission; recursive PMU-based state estimation processes; static PMU-based state estimation processes; transmission power grids; measurement model; nonlinear versions; distribution power grids; KF SE algorithms; WLS algorithms; process model; distribution reference networks; state estimators

Subjects: Control of electric power systems; Distribution networks; Power system control; Power system measurement and metering