State estimation in power systems is classically based on the weighted least squares method. Recently, different extensions of Kalman filters have been proposed. Among them, the 'unscented' Kalman filter (UKF) improves the results of weighted least squares methods, when there are small changes in the system, as it considers the history of the state. The novel algorithm presented in this work combines the best of both approaches. To perform this task a new index is defined to allow the algorithm to choose in real time, and for each iteration, between a static or a dynamic estimator. This combination allows overcoming the anomalies observed when the UKF faces abrupt variations of the system state and also the lack of observability that weighted least squares could present. The proposed methodology was tested with three test cases outperforming the previously mentioned algorithms.
A real-time traffic state estimation algorithm is developed and applied to a freeway. The evolution of the traffic is defined by a second order macroscopic model which computes, for each section of the freeway, the density and the mean speed according to several non linear equations. Different extensions of the Kalman method were already applied to this model, though none of them considers the natural constraints in the state variables. In this work, a new method that incorporates those natural constraints, is applied to the macroscopic model obtaining better results. To validate the proposed method, a simulation over a freeway section was made using two different tools: the macroscopic simulator called METANET and the microscopic simulator called SUMO. Promising results were obtained using both approaches.
Summary
In distribution power systems, one of the main objectives is to ensure uninterrupted service. Accordingly, the fast identification and isolation of faults is a matter of major interest. This work proposes a fault location method applicable to any power distribution system, which considers a random number of nodes as measurement points. An index value based on the voltage sags generated by different fault types is calculated for each node; then, considering the current measurements on the network and a preexisting database value, it is possible to determine fault type and location. This approach is analyzed by means of IEEE 34 node test feeder, and the results show that the proposed method can efficiently detect fault type and fault location.
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