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Machine learning to ensure data integrity in power system topological network database

Anwar, Adnan, Mahmood, Abdun, Ray, Biplob, Mahmud, Md Apel and Tari, Zahir 2020, Machine learning to ensure data integrity in power system topological network database, Electronics, vol. 9, no. 4, pp. 1-17, doi: 10.3390/electronics9040693.

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Title Machine learning to ensure data integrity in power system topological network database
Author(s) Anwar, AdnanORCID iD for Anwar, Adnan orcid.org/0000-0003-3916-1381
Mahmood, Abdun
Ray, Biplob
Mahmud, Md ApelORCID iD for Mahmud, Md Apel orcid.org/0000-0002-5302-5338
Tari, Zahir
Journal name Electronics
Volume number 9
Issue number 4
Article ID 693
Start page 1
End page 17
Total pages 17
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2020-04-24
ISSN 2079-9292
Keyword(s) smart grid
energy system
database
MLP
machine learning
OPF
anomaly
Summary Operational and planning modules of energy systems heavily depend on the information of the underlying topological and electric parameters, which are often kept in database within the operation centre. Therefore, these operational and planning modules are vulnerable to cyber anomalies due to accidental or deliberate changes in the power system database model. To validate, we have demonstrated the impact of cyber-anomalies on the database model used for operation of energy systems. To counter these cyber-anomalies, we have proposed a defence mechanism based on widely accepted classification techniques to identify the abnormal class of anomalies. In this study, we find that our proposed method based on multilayer perceptron (MLP), which is a special class of feedforward artificial neural network (ANN), outperforms other exiting techniques. The proposed method is validated using IEEE 33-bus and 24-bus reliability test system and analysed using ten different datasets to show the effectiveness of the proposed method in securing the Optimal Power Flow (OPF) module against data integrity anomalies. This paper highlights that the proposed machine learning-based anomaly detection technique successfully identifies the energy database manipulation at a high detection rate allowing only few false alarms.
Language eng
DOI 10.3390/electronics9040693
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2020, the authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30136593

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.