dc.contributor.author |
Alimi, OA
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dc.contributor.author |
Ouahada, K
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dc.contributor.author |
Abu-Mahfouz, Adnan MI
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dc.date.accessioned |
2019-10-04T06:43:08Z |
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dc.date.available |
2019-10-04T06:43:08Z |
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dc.date.issued |
2019-06 |
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dc.identifier.citation |
Alimi, O.A., Ouahada, K. & Abu-Mahfouz, A.M.I. 2019. Real time security assessment of the power system using a hybrid support vector machine and multilayer perceptron neural network algorithms. Sustainability, vol 11(13), pp. 1-18 |
en_US |
dc.identifier.issn |
2071-1050 |
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dc.identifier.uri |
https://doi.org/10.3390/su11133586
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dc.identifier.uri |
https://www.mdpi.com/2071-1050/11/13/3586
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dc.identifier.uri |
http://hdl.handle.net/10204/11146
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dc.description |
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
en_US |
dc.description.abstract |
In today’s grid, the technological based cyber-physical systems have continued to be plagued with cyberattacks and intrusions. Any intrusive action on the power system’s Optimal Power Flow (OPF) modules can cause a series of operational instabilities, failures, and financial losses. Real time intrusion detection has become a major challenge for the power community and energy stakeholders. Current conventional methods have continued to exhibit shortfalls in tackling these security issues. In order to address this security issue, this paper proposes a hybrid Support Vector Machine and Multilayer Perceptron Neural Network (SVMNN) algorithm that involves the combination of Support Vector Machine (SVM) and multilayer perceptron neural network (MPLNN) algorithms for predicting and detecting cyber intrusion attacks into power system networks. In this paper, a modified version of the IEEE Garver 6-bus test system and a 24-bus system were used as case studies. The IEEE Garver 6-bus test system was used to describe the attack scenarios, whereas load flow analysis was conducted on real time data of a modified Nigerian 24-bus system to generate the bus voltage dataset that considered several cyberattack events for the hybrid algorithm. Sising various performance metricion and load/generator injections, en included in the manuscriptmulation results showed the relevant influences of cyberattacks on power systems in terms of voltage, power, and current flows. To demonstrate the performance of the proposed hybrid SVMNN algorithm, the results are compared with other models in related studies. The results demonstrated that the hybrid algorithm achieved a detection accuracy of 99.6%, which is better than recently proposed schemes. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
MDPI |
en_US |
dc.relation.ispartofseries |
Workflow;22678 |
|
dc.subject |
Cyberattacks |
en_US |
dc.subject |
Hybrid support vector machines |
en_US |
dc.subject |
Intruder detection systems |
en_US |
dc.subject |
Multilayer perceptron neural networks |
en_US |
dc.subject |
Network algorithms |
en_US |
dc.subject |
Optimal power flow |
en_US |
dc.subject |
Power systems |
en_US |
dc.subject |
Real time security assessment |
en_US |
dc.subject |
Smart grid security |
en_US |
dc.title |
Real time security assessment of the power system using a hybrid support vector machine and multilayer perceptron neural network algorithms |
en_US |
dc.type |
Article |
en_US |
dc.identifier.apacitation |
Alimi, O., Ouahada, K., & Abu-Mahfouz, A. M. (2019). Real time security assessment of the power system using a hybrid support vector machine and multilayer perceptron neural network algorithms. http://hdl.handle.net/10204/11146 |
en_ZA |
dc.identifier.chicagocitation |
Alimi, OA, K Ouahada, and Adnan MI Abu-Mahfouz "Real time security assessment of the power system using a hybrid support vector machine and multilayer perceptron neural network algorithms." (2019) http://hdl.handle.net/10204/11146 |
en_ZA |
dc.identifier.vancouvercitation |
Alimi O, Ouahada K, Abu-Mahfouz AM. Real time security assessment of the power system using a hybrid support vector machine and multilayer perceptron neural network algorithms. 2019; http://hdl.handle.net/10204/11146. |
en_ZA |
dc.identifier.ris |
TY - Article
AU - Alimi, OA
AU - Ouahada, K
AU - Abu-Mahfouz, Adnan MI
AB - In today’s grid, the technological based cyber-physical systems have continued to be plagued with cyberattacks and intrusions. Any intrusive action on the power system’s Optimal Power Flow (OPF) modules can cause a series of operational instabilities, failures, and financial losses. Real time intrusion detection has become a major challenge for the power community and energy stakeholders. Current conventional methods have continued to exhibit shortfalls in tackling these security issues. In order to address this security issue, this paper proposes a hybrid Support Vector Machine and Multilayer Perceptron Neural Network (SVMNN) algorithm that involves the combination of Support Vector Machine (SVM) and multilayer perceptron neural network (MPLNN) algorithms for predicting and detecting cyber intrusion attacks into power system networks. In this paper, a modified version of the IEEE Garver 6-bus test system and a 24-bus system were used as case studies. The IEEE Garver 6-bus test system was used to describe the attack scenarios, whereas load flow analysis was conducted on real time data of a modified Nigerian 24-bus system to generate the bus voltage dataset that considered several cyberattack events for the hybrid algorithm. Sising various performance metricion and load/generator injections, en included in the manuscriptmulation results showed the relevant influences of cyberattacks on power systems in terms of voltage, power, and current flows. To demonstrate the performance of the proposed hybrid SVMNN algorithm, the results are compared with other models in related studies. The results demonstrated that the hybrid algorithm achieved a detection accuracy of 99.6%, which is better than recently proposed schemes.
DA - 2019-06
DB - ResearchSpace
DP - CSIR
KW - Cyberattacks
KW - Hybrid support vector machines
KW - Intruder detection systems
KW - Multilayer perceptron neural networks
KW - Network algorithms
KW - Optimal power flow
KW - Power systems
KW - Real time security assessment
KW - Smart grid security
LK - https://researchspace.csir.co.za
PY - 2019
SM - 2071-1050
T1 - Real time security assessment of the power system using a hybrid support vector machine and multilayer perceptron neural network algorithms
TI - Real time security assessment of the power system using a hybrid support vector machine and multilayer perceptron neural network algorithms
UR - http://hdl.handle.net/10204/11146
ER -
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en_ZA |