MULTI-CRITERIA DESIGN OPTIMIZATION OF PITCH BEARING FOR WIND POWER GENERATION SYSTEM APPLYING ARTIFICIAL INTELLIGENCE TECHNIQUES FOR ENHANCED RELIABILITY

Prasun Bhattacharjee () and Somenath Bhattacharya ()
Additional contact information
Prasun Bhattacharjee: Ramakrishna Mission Shilpapitha, India
Somenath Bhattacharya: Jadavpur University, India

Journal of Information Systems & Operations Management, 2023, vol. 17, issue 2, 45-58

Abstract: Profligate industrial development and incompetent handling of hydro-carbon-based fuels have led to global warming. The unusual heating of the surface air has consistently deteriorated the ecosystem comprehensively through erratic weather patterns and consequential upsurge of sea water level. The worsening conditions of the environment have triggered socio-economic disasters and compelled the international community to enforce the Paris Agreement of 2015 to constrain the emission of greenhouse gases. The power generation sector is one of the leading contributors to worldwide greenhouse gas emanation. Pertinent growth of renewable energy techniques such as wind power can help power generation businesses to lessen greenhouse production substantially. Globally, a considerable portion of the operating time of wind power generation systems is wasted every year owing to mechanical malfunctions of its several parts. Pitch bearing is an imperative component of the wind power generating unit which facilitates the wind turbine blades to maintain the appropriate alignment required for achieving the maximum power generation capability. In this paper, the design of the pitch bearing has been optimized using artificial intelligence methodologies like Genetic Algorithm and JAYA Algorithm. Objectives like L10 life and static load factor have been deemed for maximization whereas the bearing frictional torque has been considered for minimization. The optimal designs achieved using the aforementioned artificial intelligence techniques have been contrasted. The JAYA Algorithm is more beneficial than the Genetic Algorithm for enriching the reliability of operation for the wind turbine pitch bearing.

Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.rebe.rau.ro/RePEc/rau/jisomg/WI23/JISOM-WI23-A03.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rau:jisomg:v:17:y:2023:i:2:p:45-58

Access Statistics for this article

More articles in Journal of Information Systems & Operations Management from Romanian-American University Contact information at EDIRC.
Bibliographic data for series maintained by Alex Tabusca ().