Feed-Forward and Long Short-Term Neural Network Models for Power System State Estimation

The primary objective of this paper is to propose the two new combined the Generalized Maximum-Likelihood Estimator using the Projection statistics method are used to estimate the voltage magnitude and phase angle. Secondly, the Feed-Forward Neural Network model is proposed to combine the obtained voltages and angles. The optimal structure of the proposed Feed-Forward Neural Network model is defined model is proposed as an alternative hybrid power system state estimation approach. Finally, the different case studies including IEEE 9-bus system and IEEE 14-bus system are used to validate the effectiveness of the proposed approaches. The final results imply that the proposed approaches can provide more effective solutions than the existing approaches criteria.