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Reliability Assessment of Power Generation Systems Using Intelligent Search Based on Disparity Theory

Author

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  • Athraa Ali Kadhem

    (Department of Electrical and Electronic Engineering, University Putra Malaysia, Selangor 43400, Malaysia)

  • Noor Izzri Abdul Wahab

    (Department of Electrical and Electronic Engineering, University Putra Malaysia, Selangor 43400, Malaysia)

  • Ishak Aris

    (Department of Electrical and Electronic Engineering, University Putra Malaysia, Selangor 43400, Malaysia)

  • Jasronita Jasni

    (Department of Electrical and Electronic Engineering, University Putra Malaysia, Selangor 43400, Malaysia)

  • Ahmed N. Abdalla

    (Department of Engineering Technology, University Malaysia Pahang, Kuantan 26300, Malaysia)

Abstract

The reliability of the generating system adequacy is evaluated based on the ability of the system to satisfy the load demand. In this paper, a novel optimization technique named the disparity evolution genetic algorithm (DEGA) is proposed for reliability assessment of power generation. Disparity evolution is used to enhance the performance of the probability of mutation in a genetic algorithm (GA) by incorporating features from the paradigm into the disparity theory. The DEGA is based on metaheuristic searching for the truncated sampling of state-space for the reliability assessment of power generation system adequacy. Two reliability test systems (IEEE-RTS-79 and (IEEE-RTS-96) are used to demonstrate the effectiveness of the proposed algorithm. The simulation result shows the DEGA can generate a larger variety of the individuals in an early stage of the next population generation. It is also able to estimate the reliability indices accurately.

Suggested Citation

  • Athraa Ali Kadhem & Noor Izzri Abdul Wahab & Ishak Aris & Jasronita Jasni & Ahmed N. Abdalla, 2017. "Reliability Assessment of Power Generation Systems Using Intelligent Search Based on Disparity Theory," Energies, MDPI, vol. 10(3), pages 1-13, March.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:3:p:343-:d:92749
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    References listed on IDEAS

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    1. Akwasi F. Mensah & Leonardo Dueñas-Osorio, 2012. "A Closed-Form Technique for the Reliability and Risk Assessment of Wind Turbine Systems," Energies, MDPI, vol. 5(6), pages 1-17, June.
    2. Lin, Jin & Cheng, Lin & Chang, Yao & Zhang, Kai & Shu, Bin & Liu, Guangyi, 2014. "Reliability based power systems planning and operation with wind power integration: A review to models, algorithms and applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 921-934.
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    Cited by:

    1. Athraa Ali Kadhem & Noor Izzri Abdul Wahab & Ishak Aris & Jasronita Jasni & Ahmed N. Abdalla, 2017. "Advanced Wind Speed Prediction Model Based on a Combination of Weibull Distribution and an Artificial Neural Network," Energies, MDPI, vol. 10(11), pages 1-17, October.
    2. Martin Onyeka Okoye & Junyou Yang & Zhenjiang Lei & Jingwei Yuan & Huichao Ji & Haixin Wang & Jiawei Feng & Tunmise Ayode Otitoju & Weidong Li, 2020. "Predictive Reliability Assessment of Generation System," Energies, MDPI, vol. 13(17), pages 1-13, August.
    3. Lin He & Chang-Ling Li & Qing-Yun Nie & Yan Men & Hai Shao & Jiang Zhu, 2017. "Core Abilities Evaluation Index System Exploration and Empirical Study on Distributed PV-Generation Projects," Energies, MDPI, vol. 10(12), pages 1-18, December.

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