Foretasting-Aided State Estimator for an Electrical Distribution System

Motivated by the increasing need for robust and accurate assessment of the electrical distribution system, a developed foretaste aided state estimator is proposed. Moreover, due to the lack of measurements in the electrical distribution system, the pseudo-measurements are needed to insure the observ...

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Bibliographic Details
Main Authors: Alhalali, Safoan, El-Shatshat, Ramadan
Format: Conference Proceeding
Language:English
Subjects:
distributed generation
distribution system
State estimation
Online Access:Request full text
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Summary:Motivated by the increasing need for robust and accurate assessment of the electrical distribution system, a developed foretaste aided state estimator is proposed. Moreover, due to the lack of measurements in the electrical distribution system, the pseudo-measurements are needed to insure the observability of the state estimator. Therefore, the very short term load forecasting algorithm that insures the observability and provides reliable backup data in case of sensor malfunction or communication failure is proposed. The proposed very short term load forecasting is based on the wavelet recurrent neural network (WRNN). The historical data used to train the RNN are decomposed into low-frequency, low-high frequency and high frequency components. The neural networks are trained using an extended Kalman filter (EKF) for the low frequency component and using a square root cubature Kalman filter (SCKF) for both low-high frequency and high frequency components. To estimate the system states, state estimation algorithm based SCKF is used. The results demonstrate the theoretical and practical advantages of the proposed methodology.
ISSN:1944-9933
DOI:10.1109/PESGM40551.2019.8973934