Wind energy has seen great development during the past decade. However, wind turbine availability and reliability, especially for offshore sites, still need to be improved, which strongly affect the cost of wind energy. Wind turbine operational cost is closely depending on component failure and repair rate, while fault detection and isolation will be very helpful to improve the availability and reliability factors. In this paper, an efficient machine learning method, random forests (RFs) in combination with extreme gradient boosting (XGBoost), is used to establish the data-driven wind turbine fault detection framework. In the proposed design, RF is used to rank the features by importance, which are either direct sensor signals or constructed variables from prior knowledge. Then, based on the top-ranking features, XGBoost trains the ensemble classifier for each specific fault. In order to verify the effectiveness of the proposed approach, numerical simulations using the state-of-the-art wind turbine simulator FAST are conducted for three different types of wind turbines in both the below and above rated conditions. It is shown that the proposed approach is robust to various wind turbine models including offshore ones in different working conditions. Besides, the proposed ensemble classifier is able to protect against overfitting, and it achieves better wind turbine fault detection results than the support vector machine method when dealing with multidimensional data.

A Data-Driven Design for Fault Detection of Wind Turbines Using Random Forests and XGboost

https://cyberleninka.org/article/n/247326.pdf

Levelised cost of energy for offshore floating wind turbines in a life cycle perspective

Levelized cost of electricity (LCOE) of renewable energies and required subsidies in China

Multidisciplinary design optimization of offshore wind turbines for minimum levelized cost of energy

Wind energy has played an increasingly vital role in renewable power generation, driving the need for more cost-effective wind energy solutions. Health monitoring of turbines could provide a variety of economic and other benefits to aid in wind growth. A number of commercial and research health monitoring systems have been implemented for wind turbines. This paper surveys these systems, providing an analysis of the current state of turbine health monitoring and the challenges associated with monitoring each of the major turbine components. This paper also contextualizes the survey with the various potential benefits of health monitoring for turbines.

A survey of health monitoring systems for wind turbines

This report describes the levelized cost of energy (LCOE) for a typical land-based wind turbine installed in the United States in 2011, as well as the modeled LCOE for a fixed-bottom offshore wind turbine installed in the United States in 2011. Each of the four major components of the LCOE equation are explained in detail, such as installed capital cost, annual energy production, annual operating expenses, and financing, and including sensitivity ranges that show how each component can affect LCOE. These LCOE calculations are used for planning and other purposes by the U.S. Department of Energy's Wind Program.

/pdf/2011-cost-of-wind-energy-review-lh5xwu5y0i.pdf

2011 Cost of Wind Energy Review

This document provides a detailed description of NREL's levelized cost of wind energy equation, assumptions and results in 2010, including historical cost trends and future projections for land-based and offshore utility-scale wind.

/pdf/2010-cost-of-wind-energy-review-3qlhi9hshp.pdf

2010 Cost of Wind Energy Review

/pdf/2013-cost-of-wind-energy-review-jeyoc1gb6n.pdf

2013 Cost of Wind Energy Review

This report uses representative commercial projects to estimate the levelized cost of energy (LCOE) for both land-based and offshore wind plants in the United States for 2014. Scheduled to be published on an annual basis, the analysis relies on both market and modeled data to maintain an up-to-date understanding of wind generation cost trends and drivers. It is intended to provide insight into current component-level costs and a basis for understanding variability in the LCOE across the industry. Data and tools developed by the National Renewable Energy Laboratory (NREL) are used in this analysis to inform wind technology cost projections, goals, and improvement opportunities.

/pdf/2014-cost-of-wind-energy-review-257i5mcjpn.pdf

2014 Cost of Wind Energy Review

Drive train configurations differ in many of the modern MW scale wind turbines available. These differences occur from manufacturer to manufacturer and even within a single manufactures own portfolio. The wind energy industry aims to reduce the cost of ene rgy (CoE) for offshore wind turbines to make it cost competitive with ot her forms of energy generation (gas, coal, onshore wind etc.) This paper aims to assist with t hat CoE reduction by modelling four wind turbine types with different drive trains to dete rmine which turbine type offers the lowest CoE.

Ben Maples

Papers

2011 Cost of Wind Energy Review

2010 Cost of Wind Energy Review

2013 Cost of Wind Energy Review

2014 Cost of Wind Energy Review

Cost of energy for offshore wind turbines with different drive train types