Fault identification-based voltage sag state estimation using artificial neural network

LIAO, Huilian and ANANI, Nader (2017). Fault identification-based voltage sag state estimation using artificial neural network. Energy Procedia, 134, 40-47.

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Official URL: https://www.sciencedirect.com/science/article/pii/...
Link to published version:: https://doi.org/10.1016/j.egypro.2017.09.596

Abstract

This paper presents an artificial neural network (ANN) based approach to identify faults for voltage sag state estimation. Usually ANN cannot be used to abstract relationship between monitored data and arbitrarily named fault indices which are not related at all logically in numerical level. This paper presents a novel approach to overcome this problem. In this approach, not only the networks are trained to adapt to the given training data, the training data (the expected outputs of fault indices) is also updated to adapt to the neural network. During the training procedure, both the neural networks and training data are updated interactively. With the proposed approach, various faults can be accurately identified using limited monitored data. The approach is robust to measurement uncertainty which usually exists in practical monitoring systems. Furthermore, the updated fault indices are able to suggest the difference of the impact of various faults on bus voltages.

Item Type: Article
Additional Information: Paper originally published at the 9th International Conference on Sustainability in Energy and Buildings, SEB-17, 5-7 July 2017, Chania, Crete, Greece
Research Institute, Centre or Group: Materials and Engineering Research Institute > Thin Films Research Centre > Electronic Materials and Sensors Research Group
Identification Number: https://doi.org/10.1016/j.egypro.2017.09.596
Depositing User: Carmel House
Date Deposited: 30 Jan 2018 12:32
Last Modified: 31 Jan 2018 00:25
URI: http://shura.shu.ac.uk/id/eprint/17392

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