An agent-based approach to power system dynamic state estimation through dual unscented Kalman filter and artificial neural network
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This paper proposes a novel approach to the power system dynamic state estimation by using the concepts of dynamic artificial neural networks and dual unscented Kalman filter. A dynamic artificial neural network is applied for developing a discrete-time state transition model of power system which is used for one-step-ahead prediction of the state vector. The model is also applicable for the generation of pseudo-measurements while it is needed. The dual unscented Kalman filter estimates the state vector of power system and calculates the weights of the dynamic artificial neural network simultaneously. A multi-agent-based model is utilized to structure the computational architecture of the proposed approach. The autonomous agents are designed to carry out the complex computations needed in the power system dynamic state estimation. The agents distribute multiple execution tasks among themselves, and they share information interactively. The agent-based modeling of the proposed method enhances the computing efficiency through decomposition of the computations among several autonomous agents. The effectiveness of the proposed method is examined through comprehensive simulations. The results guarantee the performance of the proposed approach as a practical solution for the power system dynamic state estimation.
KeywordsArtificial neural network Dynamic power system state estimation Machine learning Multi agent systems Unscented Kalman filter
Compliance with ethical standards
Conflict of interest
Authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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