Research Information System
Arabian Journal for Science and Engineering, 2025 (SCI-Expanded)
The reliability of hybrid renewable energy systems (HRES) depends heavily on the identification and management of potential failure modes. This study employs a fuzzy-based BWM-MARCOS approach to systematically analyze and prioritize failure modes within wind–solar hybrid systems. The model aims to prioritize the failures considering four important risk parameters: (1) severity of the failure on system, staff, and failure, (2) failure occurrence chance, (3) effort and ease of detecting the cause of the failure, and (4) economic impact of the failure. In this context, four key risk indicators were evaluated to rank failures, revealing that SP1 (cell damage), ESS1 (battery degradation), and WT11 (battery fire) are the most critical due to their potential impact on system operations. Sensitivity analysis confirmed the stability of these rankings under varying parameter weights. Additionally, cross-method validation using fuzzy TOPSIS, SAW, and MARCOS demonstrated high correlation coefficients, underscoring the reliability of the results. Tailored mitigation strategies, including advanced diagnostics, durable materials, and robust monitoring systems, are proposed to address these critical failures. While the current methodology applies to various HRES configurations, future research should incorporate real-world operational data and machine learning techniques to enhance predictive capabilities and dynamic risk management.