LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Wind turbine dynamics modelling by a bond graph approach

Photo by goumbik from unsplash

Wind energy is immensely promising source for power generation. Although the prospect of this field is truly reliable, but people are still facing challenges on precisely controlling the turbine due… Click to show full abstract

Wind energy is immensely promising source for power generation. Although the prospect of this field is truly reliable, but people are still facing challenges on precisely controlling the turbine due to uncertainty of wind flow, cracks in shaft, structural vibration, wear and tear in bearings and so on. An integrated and impeccable model of the wind turbine system is a momentous way in predicting numerous phenomena and there by enhance the efficacy of the control system in various conditions. Considering those indisputable facts, this research presents a novel horizontal-axis multi-body wind turbine system model based on a bond graph approach. The uniqueness of this wind turbine is to consider roller bearing in multi-body system. Furthermore, bond graph approach reveals two types of models based on the essence of causality where integral and derivative causal system models are obtained. Rigorous mathematical derivations are shown subsequently to support those two possible scenarios with Newtonian and Lagrangian methods that provide new insights in the philosophy of system modelling. Model is validated later by conducting virtual experiment in SOLIDWORKS and comparing results with a literature as well. At the end, model application in machine condition monitoring is emphasized by illustrating a backlash between gear teeth of the wind turbine system.

Keywords: turbine; bond graph; system; graph approach; wind turbine

Journal Title: International Journal of Dynamics and Control
Year Published: 2018

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.