Open Access

ARTICLE

An Optimal DF Based Method for Transient Stability Analysis

by Z. A. Zaki¹, Emad M. Ahmed^1,*, Ziad M. Ali^2,3, Imran Khan⁴

1 Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka, 2014, Al-Jouf, Saudi Arabia
2 Electrical Engineering Department, College of Engineering, Prince Sattam bin Abdulaziz University, Wadi Addawaser, 11991, Saudi Arabia
3 Electrical Engineering Department, Aswan Faculty of Engineering, Aswan University, Aswan, 81542, Egypt
4 Department of Electrical Engineering, University of Engineering and Technology Peshawar, Pakistan

* Corresponding Author: Emad M. Ahmed. Email:

Computers, Materials & Continua 2022, 70(2), 3449-3471. https://doi.org/10.32604/cmc.2022.020263

Received 17 May 2021; Accepted 30 June 2021; Issue published 27 September 2021

Abstract

The effect of energy on the natural environment has become increasingly severe as human consumption of fossil energy has increased. The capacity of the synchronous generators to keep working without losing synchronization when the system is exposed to severe faults such as short circuits is referred to as the power system's transient stability. As the power system's safe and stable operation and mechanism of action become more complicated, higher demands for accurate and rapid power system transient stability analysis are made. Current methods for analyzing transient stability are less accurate because they do not account for misclassification of unstable samples. As a result, this paper proposes a novel approach for analyzing transient stability. The key concept is to use deep forest (DF) and a neighborhood rough reduction approach together. Using the neighborhood rough sets, the original feature space is obtained by creating many optimal feature subsets at various granularity levels. Then, by deploying the DF cascade structure, the mapping connection between the transient stability state and the features is reinforced. The weighted voting technique is used in the learning process to increase the classification accuracy of unstable samples. When contrasted to current methods, simulation results indicate that the proposed approach outperforms them.

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Keywords

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