To optimize the operation of power systems, monitoring of network state variables is important. Because these variables play an important role in improving economic efficiency, network reliability and analyze system status.Therefore, state estimation algorithm have been More
To optimize the operation of power systems, monitoring of network state variables is important. Because these variables play an important role in improving economic efficiency, network reliability and analyze system status.Therefore, state estimation algorithm have been used to determine an accurate estimate of state variables with limited measurements. Since modern measuring devices, such as PMUs, in addition to the measurement of electrical quantities are able to measure bus voltage angle,in this paper, a new method is proposed to obtain a more accurate estimate of all network variable. The proposed algorithm determines number and location of the measuring devices (PMUs) in such a way that state variables and electrical quantities can be obtained in the most accurate estimate. Increasing the state estimation calculations accuracy is due to the use of the derivatives of the buses voltage angle equations along with the state estimation relations. Finally, the calculation of the state estimation is performed using the least squared weighted method (WLS). The calculations performed on the IEEE 14 bus network are done using MATLAB and MATPOWER software.
The results show that the proposed method has been successful in increasing the accuracy of estimating state variables and reducing the number and proper location of PMUs
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In this paper, a new algorithm of Gaussian sum filters for state estimation of nonlinear systems is presented. The proposed method consists of several parallel Cubature Kalman filters each of which is implemented according to the simplex spherical-radial rule. In this m More
In this paper, a new algorithm of Gaussian sum filters for state estimation of nonlinear systems is presented. The proposed method consists of several parallel Cubature Kalman filters each of which is implemented according to the simplex spherical-radial rule. In this method, the probability density function is the sum of the weights of several Gaussian functions. The mean value, covariance, and weight coefficients of these Gaussian functions are calculated recursively over time, and each of the Cubature Kalman filters are responsible for updating one of these functions. Finally, the performance of the proposed filter is investigated using two nonlinear state estimation problems and the results are compared with conventional nonlinear filters. The simulation results show the appropriate accuracy of the proposed algorithm in state estimation of nonlinear systems.
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