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Improving islanded distribution system stability with adaptive decision-making framework

  • Kaka Sanaullah ORCID logo , Mingchao Xia EMAIL logo , Arif Hussain and Kashif Zulfiqar
Published/Copyright: July 29, 2024

Abstract

In an integrated distribution system incorporating distributed generation (DG), various technical challenges must be addressed when the grid becomes disconnected and transforms into an islanded system. The main focus in such circumstances revolves around ensuring the stability of the islanded network. This study presents an advanced decision-making framework for supporting islanded networks by integrating metaheuristic Black Widow Optimization (BWO) and the rate of change of the voltage stability index (RoCVSI). The Rate of Change of the Voltage Stability Index (RoCVSI) detects instability in islanded networks by continuously monitoring rapid changes in the voltage stability margin. Upon identifying potential instability, an advanced decision-making strategy utilizing the Black Widow Optimization (BWO) algorithm is deployed. BWO generates multiple load-shedding scenarios and evaluates their impact on system stability, iteratively refining the solutions through processes similar to selection and cannibalism in black widow spiders. The optimal load-shedding strategy is then implemented to selectively shed specific loads, thereby reducing demand and enhancing island stability. The proposed scheme’s effectiveness for islanded network stability is assessed by extensively analyzing the IEEE 33-bus system. The efficiency of the proposed approach is confirmed through a comparative analysis, with results indicating the better efficiency of the proposed method in the islanded network.


Correction note

Correction added on January 20, 2025 after online publication 29 July 2024: Author name changed from “Kashif Hussain” to “Kashif Zulfiqar”.



Corresponding author: Mingchao Xia, Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China, E-mail:

  1. Research ethics: Not applicable.

  2. Author contributions: Kaka Sanaullah, Mingchao Xia, Arif Hussain, Kashif Zulfiqar – the authors has accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The authors states no conflict of interest.

  4. Research funding: None declared.

  5. Data availability: The raw data can be obtained on request from the corresponding author.

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Received: 2024-03-04
Accepted: 2024-06-27
Published Online: 2024-07-29

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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