Fault Diagnosis of Industrial Systems Utilizing Meta-Heuristic Techniques
| dc.contributor.author | Abd-Errahim Falek Houssem-Eddine Bezza | |
| dc.date.accessioned | 2025-02-09T08:25:19Z | |
| dc.date.available | 2025-02-09T08:25:19Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | In this research, we employ the Enhanced Discrete Grey Wolf Optimization (EDGWO) algorithm to identify faults in a simulated Photovoltaic (PV) power plant. Our objective is to enhance fault detection accuracy and efficiency by utilizing a novel movement algorithm that optimizes the path for grey wolves. Given the growing importance of Photovoltaic power plants in sustainable energy production, reliable fault detection mechanisms are crucial to ensure optimal system operation and longevity. The Enhanced Discrete Grey Wolf Optimization (EDGWO) algorithm is an improved iteration of the conventional Grey Wolf Optimization (GWO) algorithm, specifically tailored for discrete binary optimization challenges. In our work, the grey wolves symbolize potential solutions for fault detection, where each wolf’s position is encoded as a rule classification vector. Each element in this vector corresponds to a specific parameter or feature within the PV power plant system. The optimization process seeks to identify the optimal feature combination that maximizes fault detection accuracy while minimizing false alarms. | |
| dc.identifier.uri | http://dspace.univ-khenchela.dz:4000/handle/123456789/7801 | |
| dc.language.iso | en | |
| dc.title | Fault Diagnosis of Industrial Systems Utilizing Meta-Heuristic Techniques | |
| dc.type | Thesis |
Files
Original bundle
1 - 1 of 1
No Thumbnail Available
- Name:
- Fault Diagnosis of Industrial Systems Utilizing.pdf
- Size:
- 1.55 MB
- Format:
- Adobe Portable Document Format
License bundle
1 - 1 of 1
No Thumbnail Available
- Name:
- license.txt
- Size:
- 1.71 KB
- Format:
- Item-specific license agreed to upon submission
- Description: