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A Novel Method of Protection to Prevent Reverse Power Flow Based on Neuro-Fuzzy Networks for Smart Grid

Author

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  • Ali Hadi Abdulwahid

    (Department of Engineering Electrical Power, Engineering Technical College, Southern Technical University, Al-Zubair Road, Basrah 61001, Iraq
    School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China)

  • Shaorong Wang

    (School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China)

Abstract

This paper addresses the energy challenges related to the weak protection of renewable energy from reverse energy flow and expanding access to high-quality energy at the same time. Furthermore, this paper focuses on participation in the global transition to clean and low-carbon energy systems. Moreover, the increased demand for renewable energy seems to likely depend on whether it will be possible to greatly accelerate rates of progress toward increased efficiency, de-carbonization, greater generating diversity and lower pollutant emissions. This paper focuses on the protection of renewable energy technologies because they can be particularly attractive in dispersed areas and therefore, represent an important option for rural areas that lack electrical energy and distribution infrastructure. This paper proposes an improved protection device for a reverse power protection system using a new intelligent decision support system (IDSS). The IDSS is a support system for decision making, which makes extensive use of artificial intelligence (AI) techniques. The new method integrates the powerful specification for neural networks and fuzzy inference systems. The main advantage of this method is that it causes a decrease in the steady state oscillation for the reverse power relay. In addition, the proposed method has the ability to monitor extreme environmental conditions. The generator can be converted into a motor when the steam supply to a turbine is interrupted while the generator is still connected to a grid (or operates in parallel with another generator). As a result, the generator will become a synchronous motor and will actually cause significant mechanical damage. The reverse energy protection device should be included in the generator protection scheme. Smart grids use communication networks with sophisticated algorithms to ensure coordination between protection systems. ZigBee is a newly developed technology that can be used in wireless sensor networks (WSNs) to comply with the IEEE 802.15.4 standard. Low data rates, low power consumption and low cost are key features of ZigBee. The execution of star, tree and mesh topologies as well as support comparison is based on end-to-end delay, throughput, medium access control load in addition to sent and received traffic parameters. The use of star topology obtained a delay of 0.2 s. The simulation results show that this method is superior to the traditional method in terms of speed and steady-state oscillation.

Suggested Citation

  • Ali Hadi Abdulwahid & Shaorong Wang, 2018. "A Novel Method of Protection to Prevent Reverse Power Flow Based on Neuro-Fuzzy Networks for Smart Grid," Sustainability, MDPI, vol. 10(4), pages 1-19, April.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:4:p:1059-:d:139359
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    References listed on IDEAS

    as
    1. Jakub Ehrenberger & Jan Švec, 2017. "Directional Overcurrent Relays Coordination Problems in Distributed Generation Systems," Energies, MDPI, vol. 10(10), pages 1-17, September.
    2. Noelia Uribe-Pérez & Itziar Angulo & David De la Vega & Txetxu Arzuaga & Igor Fernández & Amaia Arrinda, 2017. "Smart Grid Applications for a Practical Implementation of IP over Narrowband Power Line Communications," Energies, MDPI, vol. 10(11), pages 1-16, November.
    3. Yea-Kuang Chan & Jyh-Cherng Gu, 2012. "Modeling of Turbine Cycles Using a Neuro-Fuzzy Based Approach to Predict Turbine-Generator Output for Nuclear Power Plants," Energies, MDPI, vol. 5(1), pages 1-18, January.
    4. M. Sajid Khan & Mina Woo & Kichan Nam & Prakash K. Chathoth, 2017. "Smart City and Smart Tourism: A Case of Dubai," Sustainability, MDPI, vol. 9(12), pages 1-24, December.
    5. Ali Hadi Abdulwahid & Shaorong Wang, 2016. "A Novel Approach for Microgrid Protection Based upon Combined ANFIS and Hilbert Space-Based Power Setting," Energies, MDPI, vol. 9(12), pages 1-25, December.
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    Cited by:

    1. Wei Hou & Rita Yi Man Li & Thanawan Sittihai, 2022. "Management Optimization of Electricity System with Sustainability Enhancement," Sustainability, MDPI, vol. 14(11), pages 1-17, May.
    2. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.

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