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

Author

Listed:
  • Oyeniyi Akeem Alimi

    (Department of Electrical & Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa)

  • Khmaies Ouahada

    (Department of Electrical & Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa)

  • Adnan M. Abu-Mahfouz

    (Council for Scientific and Industrial Research (CSIR), Pretoria 0184, South Africa)

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.

Suggested Citation

  • Oyeniyi Akeem Alimi & Khmaies Ouahada & 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, MDPI, vol. 11(13), pages 1-18, June.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:13:p:3586-:d:244140
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    Citations

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    Cited by:

    1. Cheong Kim & Francis Joseph Costello & Kun Chang Lee, 2019. "Integrating Qualitative Comparative Analysis and Support Vector Machine Methods to Reduce Passengers’ Resistance to Biometric E-Gates for Sustainable Airport Operations," Sustainability, MDPI, vol. 11(19), pages 1-22, September.
    2. Oyeniyi Akeem Alimi & Khmaies Ouahada & Adnan M. Abu-Mahfouz & Suvendi Rimer & Kuburat Oyeranti Adefemi Alimi, 2021. "A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification," Sustainability, MDPI, vol. 13(17), pages 1-19, August.
    3. Ana Maria Mihaela Iordache & Codruța Cornelia Dura & Cristina Coculescu & Claudia Isac & Ana Preda, 2021. "Using Neural Networks in Order to Analyze Telework Adaptability across the European Union Countries: A Case Study of the Most Relevant Scenarios to Occur in Romania," IJERPH, MDPI, vol. 18(20), pages 1-28, October.
    4. Derya Betul Unsal & Taha Selim Ustun & S. M. Suhail Hussain & Ahmet Onen, 2021. "Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation," Energies, MDPI, vol. 14(9), pages 1-36, May.

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