gainML (version 0.1.0)

Machine Learning-Based Analysis of Potential Power Gain from Passive Device Installation on Wind Turbine Generators

Description

Provides an effective machine learning-based tool that quantifies the gain of passive device installation on wind turbine generators. H. Hwangbo, Y. Ding, and D. Cabezon (2019) .

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Version

Install

install.packages('gainML')

Monthly Downloads

142

Version

0.1.0

License

GPL-3

Maintainer

Hoon Hwangbo

Last Published

June 28th, 2019

Functions in gainML (0.1.0)

pw.freq

Long-Term Frequency of Power Output

analyze.p1

Apply Period 1 Analysis

wtg

Wind turbine operational data

arrange.data

Split, Merge, and Filter Given Datasets for the Subsequent Analysis

quantify.gain

Quantify Gain Based on Period 1 and Period 2 Prediction

analyze.gain

Analyze Potential Gain from Passive Device Installation on WTGs by Using a Machine Learning-Based Tool

analyze.p2

Apply Period 2 Analysis

bootstrap.gain

Construct a Confidence Interval of the Gain Estimate