This paper explores the application of computational intelligence techniques in the field of fault diagnosis within power systems. With the increasing complexity of power grid infrastructure and the rising demand for reliability and efficiency, the need for effective fault diagnosis methods has become paramount. The study evaluates and compares various computational intelligence techniques, including artificial neural networks, fuzzy logic, genetic algorithms, and support vector machines, to identify and diagnose faults accurately and efficiently. The simulation results demonstrate the superiority of these techniques in terms of fault detection, classification, and localization. This paper also discusses the limitations and challenges associated with the integration of computational intelligence in fault diagnosis and proposes potential solutions to enhance system performance. Furthermore, the practical implications and future directions for the application of computational intelligence in power systems are discussed, highlighting the potential for improving system reliability, reducing downtime, and optimizing maintenance operations.
Brown, J. Computational Intelligence Techniques for Fault Diagnosis in Power Systems. Transactions on Applied Soft Computing, 2020, 2, 13. https://doi.org/10.69610/j.tasc.20201022
AMA Style
Brown J. Computational Intelligence Techniques for Fault Diagnosis in Power Systems. Transactions on Applied Soft Computing; 2020, 2(2):13. https://doi.org/10.69610/j.tasc.20201022
Chicago/Turabian Style
Brown, James 2020. "Computational Intelligence Techniques for Fault Diagnosis in Power Systems" Transactions on Applied Soft Computing 2, no.2:13. https://doi.org/10.69610/j.tasc.20201022
APA style
Brown, J. (2020). Computational Intelligence Techniques for Fault Diagnosis in Power Systems. Transactions on Applied Soft Computing, 2(2), 13. https://doi.org/10.69610/j.tasc.20201022
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