Power system static state estimation using Kalman filter algorithm

State estimation of power system is an important tool for operation, analysis and forecasting of electric power system. In this paper, a Kalman filter algorithm is presented for static estimation of power system state variables. IEEE 14 bus system is employed to check the accuracy of this method. Newton Raphson load flow study is first carried out on our test system and a set of data from the output of load flow program is taken as measurement input. Measurement inputs are simulated by adding Gaussian noise of zero mean. The results of Kalman estimation are compared with traditional Weight Least Square (WLS) method and it is observed that Kalman filter algorithm is numerically more efficient than traditional WLS method. Estimation accuracy is also tested for presence of parametric error in the system. In addition, numerical stability of Kalman filter algorithm is tested by considering inclusion of zero mean errors in the initial estimates

Dynamic state estimation of power system harmonics

The application of solid state drives in power systems has led to numerous issues of electric power quality. Many of these issues are related to harmonics in power systems which threaten the quality of electric power supplied to the consumer. The upgrading of the IEEE guide that set limits on harmonic injections added additional focus to the subject of harmonic state estimation and monitoring. This research gives a comprehensive and deep study of power system harmonic state estimation. In particular, dynamic state estimation of power system harmonics is the subject of this research. Dynamic state estimation algorithms have been developed to estimate and track power system harmonic changes as a result of load variation over a period of 24 hours. Kalman filter methodologies have been used to get the optimal estimate of the state vector; harmonic bus voltage magnitudes and phase angles. Two types of Kalman filter techniques have been used. First, is the centralized Kalman filter technique, in which the measurement vector is processed by one estimator (processor). This technique is usually applied to small-size power systems. Second, is the decentralized structure of Kalman filter, in which the measurement vector is partitioned in several subvectors, each is processed by a local estimator. This approach is potentially fast and reliable when implemented using parallel multi-processor