Preventing Cascading Failure Through Fuzzy Co-Operative Control Mechanism Using V2G
Open accessAbstract
The previously proposed algorithms for preventing cascading failures, which lead to a blackout event, involve specific load shedding schemes, which introduce incurring losses in the power system network. In this paper, a cooperative control based algorithm using a vehicle to grid (V2G) technology based on a fuzzy logic approach is proposed to prevent cascading failures without loss incurrence. The algorithm is implemented on a standard IEEE-30 bus system, and it uses mathematical combinations heuristically to identify the critical nodes through the use of a self-propagation graph to dispatch the optimum power from V2G. For the enhancement of computational speed, a network operator considers only those vulnerable nodes, which are identified by a self-propagating graph. Through this, a network operator can easily detect critical nodes by rooting straight to the vulnerable transmission lines in the IEEE-30 bus network. The probabilistic modeling in this paper is performed in such a way that network operators will mitigate cascading failures events (CFEs) after the occurrence of (N -1) and (N -1-1) contingencies/blackout events without performing load shedding. The detailed experimental analysis provides better visualization of the impact of CFEs on power grids to the power network operators and therefore significantly improves the accuracy of taking necessary actions to compensate these CFEs.
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Many professionals are working on the deployment and development of a 6G wireless network.
“Even though the full potential of a 5G network is yet to be recognized by the world, many professionals are working on the deployment and development of a 6G wireless network [82].”
From Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey (2023) · cites this paper as [82]
The adoption of the 6G network can be used in many industrial application sectors ranging from Industry 5.0 to body area networks.
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From Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey (2023) · cites this paper as [82]
In a 6G network, data generation and collection will be abundant.
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- J. Elect. Power Energy Syst., vol. 27, no. 4, pp. 318-326, May 2005.
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Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey
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Self-citation
- Authors
- Adnan, M., Ali, M., Basalamah, A., & Tariq, M.
- Affiliations
- National University of Computer and Emerging Sciences (PK)
- Umm al-Qura University (SA)
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 142607-142622
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2978994931
- Cited by
- 30
- References
- 67
- Citations by year
- 2025: 3 · 2024: 10 · 2023: 3 · 2022: 6 · 2021: 3 · 2020: 4 · 2019: 1
- FWCI
- 1.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Greater South Information System
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Power System Optimization and Stability
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optimal Power Flow Distribution
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
- Keywords
- cascading failures, vehicle-to-grid, fuzzy logic control, cooperative control, critical node identification, blackout prevention, N-1 contingency, IEEE 30-bus system, power system protection
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