Title :
Radial basis neural network state estimation of electric power networks
Author :
Singh, D. ; Pandey, J.P. ; Chauhan, D.S.
Author_Institution :
Dept. of Electr. Eng., Kamla Nehru Inst. of Technol., Sultanpur, India
Abstract :
An original application of radial basis function (RBF) neural network for power system state estimation is proposed in this paper. The property of massive parallelism of neural networks is employed for this. The application of RBF neural network for state estimation is investigated by testing its applicability on a IEEE 14 bus system. The proposed estimator is compared with conventional weighted least squares (WLS) state estimator on basis of time, accuracy and robustness. It is observed that the time taken by the proposed estimator is quite low. The proposed estimator is more accurate and robust in case of gross errors and topological errors present in the measurement data.
Keywords :
least squares approximations; power engineering computing; power system state estimation; radial basis function networks; IEEE 14 bus system; RBF neural network; electric power networks state estimation; massive parallelism; radial basis function neural network; weighted least squares state estimator; Least squares approximation; Neural networks; Parallel processing; Power system management; Power system modeling; Power system security; Power systems; Robustness; State estimation; Voltage;
Conference_Titel :
Electric Utility Deregulation, Restructuring and Power Technologies, 2004. (DRPT 2004). Proceedings of the 2004 IEEE International Conference on
Print_ISBN :
0-7803-8237-4
DOI :
10.1109/DRPT.2004.1338474
