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Analysis and fault diagnosis in point absorber wave energy conversion systems using fault tree and Bayesian networks

Guemmour Mohamed Boutkhil, Negadi Karim, Araria Rabah, Bey Mohamed

Diagnostyka

2025

Vol. 26, No. 2

art. no. 202509

This paper presents a comprehensive analysis and fault diagnosis approach for wave energy conversion (WEC) systems, specifically focusing on point absorber technology, using Bayesian Networks (BNs). The main objective of this work is to develop a probabilistic framework that enhances fault detection and diagnosis by modeling the interdependencies between key subsystems, including the power take-off (PTO) mechanism, mooring lines, and electrical components. Wave energy conversion systems offer a promising solution for sustainable energy generation, but fault detection remains a critical challenge in ensuring continuous and efficient operation. The proposed approach enables a probabilistic evaluation of failure modes and their impact on overall system performance by modeling the complex interdependencies between system components. By integrating environmental factors, historical failure logs, and operational data, the Bayesian network allows real-time dynamic updates of fault probabilities, facilitating predictive maintenance techniques. The proposed approach aims to improve system reliability, reduce downtime, and optimize maintenance strategies. Case studies are provided to validate the approach, demonstrating significant improvements in early fault detection. The results underscore the potential of Bayesian networks as a powerful tool for enhancing the operational resilience and sustainability of wave energy conversion systems. The analysis focuses on key subsystems, including the power take-off mechanism, mooring lines, and electrical components, where failures are most likely to occur due to harsh marine conditions.

A novel approach to wave energy conversion using CFD technique

Abbas Nawar, Barbahan Michel, Kabrial Yako, Kabrial Admoun

Polish Maritime Research

2024

nr 3

113--125

This article details the development and evaluation of a novel wave energy converter (WEC) aimed at efficiently capturing wave energy for electricity production. The study employs Computational Fluid Dynamics (CFD) techniques, specifically the URANS method and the k-ω SST turbulence model, to solve the Navier-Stokes equations and capture the free Surface using the Volume of Fluid (VOF) model. The CFD results are validated against experimental data to ensure accuracy. Various design parameters of the proposed device were tested, revealing that the arms and bottom angle significantly affect its performance. Unlike the floating Wave Dragon (WD) device, which utilises potential energy and is set in deep water, the new fixed-seabed device is positioned in the transitional wave region near the shore, where waves retain 80% of their energy. It can be constructed from environmentally friendly cement, making it resistant to hurricanes and suitable for any wave turbine in the open sea. The MP687 turbine was used to capture the wave energy in the proposed device, testing its performance in three positions: in the open sea, in the middle of the device, and at the device’s outlet. The results show that the device significantly enhances wave energy concentration, especially when the turbine is placed at the outlet. The proposed device offers numerous advantages, including its fixed position in a high-energy wave zone, the efficient use of turbulent kinetic energy, and robust construction that can withstand storms.