scholarly journals Ultrasound-Based Smart Corrosion Monitoring System for Offshore Wind Turbines

2022 ◽  
Vol 12 (2) ◽  
pp. 808
Author(s):  
Upeksha Chathurani Thibbotuwa ◽  
Ainhoa Cortés ◽  
Andoni Irizar

The ultrasound technique is a well-known non-destructive and efficient testing method for on-line corrosion monitoring. Wall thickness loss rate is the major parameter that defines the corrosion process in this approach. This paper presents a smart corrosion monitoring system for offshore wind turbines based on the ultrasound pulse-echo technique. The solution is first developed as an ultrasound testbed with the aim of upgrading it into a low-cost and low-power miniaturized system to be deployed inside offshore wind turbines. This paper discusses different important stages of the presented monitoring system as design methodology, the precision of the measurements, and system performance verification. The obtained results during the testing of a variety of samples show meaningful information about the thickness loss due to corrosion. Furthermore, the developed system allows us to measure the Time-of-Flight (ToF) with high precision on steel samples of different thicknesses and on coated steel samples using the offshore standard coating NORSOK 7A.

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 464
Author(s):  
Jinje Park ◽  
Changhyun Kim ◽  
Minh-Chau Dinh ◽  
Minwon Park

Renewable energy is being adopted worldwide, and the proportion of offshore wind turbines is increasing. Offshore wind turbines operate in harsh weather conditions, resulting in various failures and high maintenance costs. In this paper, a condition diagnosis model for condition monitoring of an offshore wind turbine has been developed. The generator, main bearing, pitch system, and yaw system were selected as components subject to the condition monitoring by considering the failure rate and downtime of the wind turbine. The condition diagnosis model works by comparing real-time and predictive operating data of the wind turbine, and about four years of Supervisory Control and Data Acquisition (SCADA) data from a 2 MW wind turbine was used to develop the model. A deep neural network and an artificial neural network were used as machine learning to predict the operational data in the condition diagnosis model, and a confusion matrix was used to measure the accuracy of the failure determination. As a result of the condition monitoring derived by inputting SCADA data to the designed system, it was possible to maintain the failure determination accuracy of more than 90%. The proposed condition monitoring system will be effectively utilized for the maintenance of wind turbines.


2005 ◽  
Vol 29 (6) ◽  
pp. 531-563 ◽  
Author(s):  
Per Vølund

This paper compares the costs of using concrete foundations against steel monopile foundations for offshore wind turbines, and argues that concrete foundations will be cheaper. Most offshore windfarms have steel monopile foundations, but in Denmark concrete gravity foundations have been used with success. Two projects have tendered for steel monopiles and for concrete foundations and have implemented the concrete foundations that proved cheaper. No project has tendered for both foundation concepts and chosen steel monopiles. Nysted Offshore Windfarm with concrete foundations has the cheapest foundations of any offshore windfarm so far. A conceptual foundation study carried out for the London Array West Offshore Windfarm indicates that the same method and very low-cost foundations as for Nysted can be used. Optimised design of light-weight concrete constructions is the key to low-cost installation. Cheap manufacture can be carried out near the site or at even lower cost in Eastern Europa from where it can be shipped at little extra cost. The main construction of steel monopile foundations will become twice as costly as of concrete gravity foundations, and though installation is more costly for the gravity foundations it seems most likely that tendering between steel monopile and concrete gravity for London Array West will prove concrete considerably cheaper. It is argued that these considerations are to a wide extent generally valid, and also for very large turbines in deeper water. Concrete foundations will in 2006 be installed at Lillegrunden Offshore Windfarm in Sweden, and at Belgian Thornton Bank in 2006–7. So indications are strong that concrete is the future for offshore foundations.


2014 ◽  
Vol 134 (8) ◽  
pp. 1096-1103 ◽  
Author(s):  
Sho Tsujimoto ◽  
Ségolène Dessort ◽  
Naoyuki Hara ◽  
Keiji Konishi

Author(s):  
Jose´ G. Rangel-Rami´rez ◽  
John D. So̸rensen

Deterioration processes such as fatigue and corrosion are typically affecting offshore structures. To “control” this deterioration, inspection and maintenance activities are developed. Probabilistic methodologies represent an important tool to identify the suitable strategy to inspect and control the deterioration in structures such as offshore wind turbines (OWT). Besides these methods, the integration of condition monitoring information (CMI) can optimize the mitigation activities as an updating tool. In this paper, a framework for risk-based inspection and maintenance planning (RBI) is applied for OWT incorporating CMI, addressing this analysis to fatigue prone details in welded steel joints at jacket or tripod steel support structures for offshore wind turbines. The increase of turbulence in wind farms is taken into account by using a code-based turbulence model. Further, additional modes t integrate CMI in the RBI approach for optimal planning of inspection and maintenance. As part of the results, the life cycle reliabilities and inspection times are calculated, showing that earlier inspections are needed at in-wind farm sites. This is expected due to the wake turbulence increasing the wind load. With the integration of CMI by means Bayesian inference, a slightly change of first inspection times are coming up, influenced by the reduction of the uncertainty and harsher or milder external agents.


2021 ◽  
Vol 11 (2) ◽  
pp. 574
Author(s):  
Rundong Yan ◽  
Sarah Dunnett

In order to improve the operation and maintenance (O&M) of offshore wind turbines, a new Petri net (PN)-based offshore wind turbine maintenance model is developed in this paper to simulate the O&M activities in an offshore wind farm. With the aid of the PN model developed, three new potential wind turbine maintenance strategies are studied. They are (1) carrying out periodic maintenance of the wind turbine components at different frequencies according to their specific reliability features; (2) conducting a full inspection of the entire wind turbine system following a major repair; and (3) equipping the wind turbine with a condition monitoring system (CMS) that has powerful fault detection capability. From the research results, it is found that periodic maintenance is essential, but in order to ensure that the turbine is operated economically, this maintenance needs to be carried out at an optimal frequency. Conducting a full inspection of the entire wind turbine system following a major repair enables efficient utilisation of the maintenance resources. If periodic maintenance is performed infrequently, this measure leads to less unexpected shutdowns, lower downtime, and lower maintenance costs. It has been shown that to install the wind turbine with a CMS is helpful to relieve the burden of periodic maintenance. Moreover, the higher the quality of the CMS, the more the downtime and maintenance costs can be reduced. However, the cost of the CMS needs to be considered, as a high cost may make the operation of the offshore wind turbine uneconomical.


2021 ◽  
Vol 13 (5) ◽  
pp. 2862
Author(s):  
Amer Al-Hinai ◽  
Yassine Charabi ◽  
Seyed H. Aghay Kaboli

Despite the long shoreline of Oman, the wind energy industry is still confined to onshore due to the lack of knowledge about offshore wind potential. A spatial-temporal wind data analysis is performed in this research to find the locations in Oman’s territorial seas with the highest potential for offshore wind energy. Thus, wind data are statistically analyzed for assessing wind characteristics. Statistical analysis of wind data include the wind power density, and Weibull scale and shape factors. In addition, there is an estimation of the possible energy production and capacity factor by three commercial offshore wind turbines suitable for 80 up to a 110 m hub height. The findings show that offshore wind turbines can produce at least 1.34 times more energy than land-based and nearshore wind turbines. Additionally, offshore wind turbines generate more power in the Omani peak electricity demand during the summer. Thus, offshore wind turbines have great advantages over land-based wind turbines in Oman. Overall, this work provides guidance on the deployment and production of offshore wind energy in Oman. A thorough study using bankable wind data along with various logistical considerations would still be required to turn offshore wind potential into real wind farms in Oman.


2021 ◽  
Vol 20 (3) ◽  
pp. 501-511
Author(s):  
Deming Ma ◽  
Yongsheng Li ◽  
Yanxiong Liu ◽  
Jianwei Cai ◽  
Rui Zhao

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