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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)

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Impact Factor : 8.14

Issue per Year : 12

Volume Published : 7

Issue Published : 74

Article Submitted : 3609

Article Published : 2079

Total Authors : 5515

Total Reviewer : 528

Total Countries : 39

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Published Paper Details
Paper Title: Artificial Intelligence Based Power System Stability
Authors Name: Manisha Dilip Landge , Vaishali Chavhan
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IJRTI_182334
Published Paper Id: IJRTI2206085
Published In: Volume 7 Issue 6, June-2022
DOI:
Abstract: The stability analysis is considered as a crucial problem for a reliable and safe power system. Voltage stability refers to a power system's ability to maintain consistent, acceptable voltages across all of its buses. The measurement of the voltage stability index (VSI) for a power system condition can operate as an accurate and quick indicator of the systems near to voltage instability. The application of intelligent methods based on artificial neural networks (ANN), fuzzy logic, and evolutionary algorithms to the problem of voltage stability assessment has recently piqued interest. With the capacity to provide non-linear input/output mapping, parallel processing, learning, and generalization, ANNs have the potential to be suitable for estimating power system VSIs without solving the governing power system equations. This paper presents the power stability analysis based on ANN and K-nearest neighbor. For each scenario, Power Stability Analysis (PSA) utilizing VSI is performed for several option loading strategies of power network creating ANN models. The outcome was proven to be effective in analyzing voltage stability issues, specifically in ranking network buses in order of vulnerability.
Keywords: power Stability Analysis, Artificial Neural Network, K-Nearest Neighbor, Machine Learning
Cite Article: "Artificial Intelligence Based Power System Stability", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 6, page no.501 - 505, June-2022, Available :http://www.ijrti.org/papers/IJRTI2206085.pdf
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ISSN: 2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publication Details: Published Paper ID: IJRTI2206085
Registration ID:182334
Published In: Volume 7 Issue 6, June-2022
DOI (Digital Object Identifier):
Page No: 501 - 505
Country: Sangamner, Maharashtra, India
Research Area: Electrical Engineering 
Publisher : IJ Publication
Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2206085
Published Paper PDF: https://www.ijrti.org/papers/IJRTI2206085
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016

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