ISSN Number : 2584-0673
Four Issues Per Year
No Publication Fee
Free and Open Access Peer Reviewed Journal
Articles Invited in English only
Submission to a Final Decision: 45 Days
Acceptance to Publication: 12 Days
Pages:66-75
Category: Engineering & Technology
Published Date: 31-Aug-2024
Sravan Kumar Gunturi, Dipu Sarkar, Prasanna Lakshmi Akella, Ravikanth Garladinne, Veera Venkata Raghunath Indugu
Keywords:Ensemble Learning, Feature Engineering, Machine Learning, Phasor Measurement Units, Power Distribution Systems, Voltage Stability.
In modern power grids, ensuring operational stability necessitates real-time assessment of static voltage stability. This paper presents an advanced ensemble machine learning approach to estimate voltage stability in power distribution systems using data from phasor measurement units (PMUs). The methodology integrates multiple machine learning techniques into a unified predictive model to minimize bias and variance. A critical feature of this approach is the inclusion of a feature pre-processing step to eliminate redundant and irrelevant features, enhancing computational efficiency through dimensionality reduction. Additionally, a minority oversampling method for regression is utilized to address data disparity, ensuring robust model performance. Various ensemble models were evaluated using mean square error metrics, with the Bagging model ExtraTrees outperforming other algorithms. The proposed method's efficacy is validated on a 30-bus IEEE system, highlighting its potential for real-time applications. This research offers a robust solution for maintaining grid reliability and preventing power outages by providing accurate, real-time voltage stability assessments.
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