Reducing expert dependency in dynamic risk analysis through intelligent algorithms

Creative Commons License

Karadayı B., Kuvvetli Y., Ural S.

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION, no.189, pp.561-576, 2024 (SCI-Expanded)

  • Publication Type: Article / Article
  • Publication Date: 2024
  • Doi Number: 10.1016/j.psep.2024.06.038
  • Journal Name: PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Biotechnology Research Abstracts, CAB Abstracts, Chemical Abstracts Core, Chimica, Communication Abstracts, Compendex, Environment Index, Food Science & Technology Abstracts, Greenfile, INSPEC, Metadex, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Page Numbers: pp.561-576
  • Çukurova University Affiliated: Yes

Abstract

Risk management is a crucial tool for facilities to manage decision-making and operations. Risk management has
become essential in the energy sector because of the recent global energy supply crisis around the world. Risk
analysis, a key tool in risk management, was performed using different techniques. As in many other fields,
machine learning has been used for risk analysis along with technological developments. This study aims to
decrease the dependency on experts associated with the use of machine learning techniques for wind turbine risk
analysis. Therefore, a SCADA-related database consisting of operational and alarm data of a wind turbine was
examined. System failure and economic loss-based risk models have also been investigated. Features that affect
risk levels are automatically included in the risk model and are aimed at reducing expert opinions. The study
culminated in highly consistent real-time-based and future-based risk predictions.