Adaptive Fault Detection Scheme Using an Optimized Self-healing Ensemble Machine Learning Algorithm

Levent Yavuz; Ahmet Soran; Ahmet Önen; Xiangjun Li; S. M. Muyeen

doi:10.17775/CSEEJPES.2020.03760

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Adaptive Fault Detection Scheme Using an Optimized Self-healing Ensemble Machine Learning Algorithm

Regular Papers | 更新时间：2026-10-15

- Adaptive Fault Detection Scheme Using an Optimized Self-healing Ensemble Machine Learning Algorithm
- CSEE Journal of Power and Energy Systems Vol. 8, Issue 4, Pages: 1145-1156(2022)
- 作者机构：
  
  1. Electrical and Computer Engineering Department in Abdullah Gül University,Kayseri,Turkey
  2. Department of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University, Al-Khoud, Muscat,Oman
  3. State Key Laboratory of Control and Operation of Renewable Energy and Storage Systems, China Electric Power Research Institute,Beijing,China
  4. Department of Electrical Engineering, Qatar University, Doha, Oatar
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2020.03760
  CLC：
- Published：01 July 2022
- 稿件说明：
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Levent Yavuz, Ahmet Soran, Ahmet Önen, et al. Adaptive Fault Detection Scheme Using an Optimized Self-healing Ensemble Machine Learning Algorithm[J]. CSEE Journal of Power and Energy Systems, 2022, 8(4): 1145-1156.
DOI：

Levent Yavuz, Ahmet Soran, Ahmet Önen, et al. Adaptive Fault Detection Scheme Using an Optimized Self-healing Ensemble Machine Learning Algorithm[J]. CSEE Journal of Power and Energy Systems, 2022, 8(4): 1145-1156. DOI： 10.17775/CSEEJPES.2020.03760.

摘要

Abstract

This paper proposes a new cost-efficient

adaptive

and self-healing algorithm in real time that detects faults in a short period with high accuracy

even in the situations when it is difficult to detect. Rather than using traditional machine learning (ML) algorithms or hybrid signal processing techniques

a new framework based on an optimization enabled weighted ensemble method is developed that combines essential ML algorithms. In the proposed method

the system will select and compound appropriate ML algorithms based on Particle Swarm Optimization (PSO) weights. For this purpose

power system failures are simulated by using the PSCAD-Python co-simulation. One of the salient features of this study is that the proposed solution works on real-time raw data without using any pre-computational techniques or pre-stored information. Therefore

the proposed technique will be able to work on different systems

topologies

or data collections. The proposed fault detection technique is validated by using PSCAD-Python co-simulation on a modified and standard IEEE-14 and standard IEEE-39 bus considering network faults which are difficult to detect.

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