Evaluation of a few wave energy converters for the Indian shelf seas based on available wave power

The energy resource assessment at a location is crucial for designing wave energy projects. However, there is a divergence in wave energy converter (WEC) design due to diversity in wave climate, energy availability, and conversion methodology. Optimizing devices for different wave climates is essent...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Ocean engineering 2022-01, Vol.244, p.110360, Article 110360
Hauptverfasser: Amrutha, M.M., Sanil Kumar, V.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The energy resource assessment at a location is crucial for designing wave energy projects. However, there is a divergence in wave energy converter (WEC) design due to diversity in wave climate, energy availability, and conversion methodology. Optimizing devices for different wave climates is essential since low risk, low variability, and moderate energy resources are attractive. This paper examines the location-wise wave characteristics with energy resource evaluation and the performance of a few WECs in the Indian shelf sea using the WAVEWATCH III model output data for 26 years (1990–2015). The annual mean wave power at 25–30 m water depth at the 20 stations varies from 1.49 kW/m to 11.79 kW/m. From offshore to nearshore waters, a large reduction in wave power is observed at a few stations. At stations off the west coast, southernmost peninsular, and northwest Bay of Bengal, the percentage wave power available during summer monsoon is 42–87% of the annual power. K-means clustering algorithm is applied to the study area and obtained two device classes. Based on Multi-Criteria Approach, different WEC for different sites are evaluated. •Energy resource evaluation done in the Indian shelf seas with 26 years data.•At few locations wave power during summer monsoon is 42–87% of annual power.•K-means clustering algorithm is applied and two device classes were obtained.•Few Wave Energy Converters evaluated for different locations by Multi-Criteria Approach.
ISSN:0029-8018
1873-5258
DOI:10.1016/j.oceaneng.2021.110360