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Article

Real Time Security Assessment of the Power System Using a Hybrid Support Vector Machine and Multilayer Perceptron Neural Network Algorithms

1
Department of Electrical & Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa
2
Council for Scientific and Industrial Research (CSIR), Pretoria 0184, South Africa
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(13), 3586; https://doi.org/10.3390/su11133586
Received: 31 May 2019 / Revised: 21 June 2019 / Accepted: 24 June 2019 / Published: 29 June 2019
(This article belongs to the Special Issue Smart Energy Management for Smart Grids)
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. View Full-Text
Keywords: multilayer perceptron neural network; support vector machine; cyberattacks; optimal power flow; smart grid security; intruder detection system multilayer perceptron neural network; support vector machine; cyberattacks; optimal power flow; smart grid security; intruder detection system
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MDPI and ACS Style

Alimi, O.A.; Ouahada, K.; Abu-Mahfouz, A.M. Real Time Security Assessment of the Power System Using a Hybrid Support Vector Machine and Multilayer Perceptron Neural Network Algorithms. Sustainability 2019, 11, 3586. https://doi.org/10.3390/su11133586

AMA Style

Alimi OA, 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. Sustainability. 2019; 11(13):3586. https://doi.org/10.3390/su11133586

Chicago/Turabian Style

Alimi, Oyeniyi A., Khmaies Ouahada, and Adnan M. Abu-Mahfouz 2019. "Real Time Security Assessment of the Power System Using a Hybrid Support Vector Machine and Multilayer Perceptron Neural Network Algorithms" Sustainability 11, no. 13: 3586. https://doi.org/10.3390/su11133586

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