Citation for the version of the work held in 'OpenAIR@RGU. This work puts emphasis on using failure analysis as a basis for designing a condition based prognostic maintenance plan in order to control cost of power and make maintenance more efficient. An essential aspect of such failure analysis is to identify wind turbine components, ascertain their failures and find root causes of the failures. However as a first step, identification of prominent failures in the critical assemblies of a wind turbine using available inspection methods and making provisions to control their occurrence would make significant contribution in improving wind turbine reliability. This work introduces Failure Modes Effects and Criticality Analysis (FMECA) as an important failure analysis tool that has in the past successfully benefitted the airlines, marine, nuclear and spacecraft industries. FMECA is a structured failure analysis technique that can also evaluate the risk and priority number of a failure and hence assist in prioritising maintenance works. The work shows, how with a slight modification of the existing FMECA method, a very useful failure analysis method can be developed for offshore wind turbines including its operational uniqueness. This work further proposes modifying the format for calculating the Risk Priority Number (RPN) for wind turbine failure. By using wind turbine gearbox as a case study, this work illustrates the usefulness of RPN number in identifying failures which can assist in designing cost effective maintenance plan. Some preliminary results of a FMECA tool that has been developed to automatically evaluate the effects and criticality of a failure in a wind turbine at the component level is included.

No abstract

This is an author produced version of a paper published in Wind Engineering (ISSN 0309-524X, eISSN 2048-402X) This version may not include final proof corrections and does not include published layout or pagination. Citation DetailsCitation CopyrightItems in 'OpenAIR@RGU', Robert Gordon University Open Access Institutional Repository, are protected by copyright and intellectual property law. If you believe that any material held in 'OpenAIR@RGU' infringes copyright, please contact openair-help@rgu.ac.uk with details. The item will be removed from the repository while the claim is investigated. This is the author's reviewed version, the full published article may be found at http://dx.doi.org/10.1260/0309-524X.39. AbstractWind Turbines require numerous and varied types of maintenance activities throughout their lifespan, the frequency of which increases with years in operation. At present the proportion of maintenance cost to the total cost for wind turbines is significant particularly for offshore wind turbines (OWT) where this ratio is ~35%. If this ratio is to be reduced in-spite of adverse operating conditions, pre-mature component failures and absence of reliability database for wind turbine components, there is a need to design unconventional maintenance scheme preferably by including novel failure prediction methodologies. Several researchers have advocated the use of Artificial Neural Networks (ANN), Bayesian Network Theory (BNT) and other statistical methods to predict failure so as to plan efficient maintenance of wind turbines, however novelty and randomness of failures, nature and number of parameters involved in statistical calculations and absence of required amount of fundamental work required for such advanced analysis have continued to maintain the high cost of maintenance. This work builds upon the benefits of condition monitoring to design methods to predict generic failures in wind turbine components and exhibits how such prediction methods can assist in cutting the maintenance cost of wind turbines. This study proposes using a dedicated tool to assist with failure prediction and planning and execution of wind turbine maintenance. The design and development of such an all-inclusive tool will assist in performing administrative works, inventory control, financial calculations and service management apart from failure prediction in wind turbine components. Its database will contain reference to standard management practices, regulatory provisions, staff details and their skillsets, service call register, troubleshooting manuals, installation guide, service history, details of customers and clients etc. that would cater to multiple avenues of wind turbine maintenance. In order to build such a software package, a robust design of its database is crucial. This work lists prerequisites for choosing a physical database and identifies the benefits of relational database software in controlling large amounts of data of various formats that are stored in such physical databases. Such a database wou...

This is an author produced version of a paper published in Wind Engineering (ISSN 0309-524X, eISSN 2048-402X) This version may not include final proof corrections and does not include published layout or pagination. Citation Details CopyrightItems in 'OpenAIR@RGU', Robert Gordon University Open Access Institutional Repository, are protected by copyright and intellectual property law. If you believe that any material held in 'OpenAIR@RGU' infringes copyright, please contact openair-help@rgu.ac.uk with details. The item will be removed from the repository while the claim is investigated. This is the author's reviewed version, the full published article may be found at http://dx.doi.org/10.1260/0309-524X.37. AbstractOffshore Wind Turbine (OWT) maintenance costs in between 20 -35% of the lifetime power generation cost. Many techniques and tools that are being developed to curtail this cost are challenged by the stochastic climatic conditions of offshore location and the wind energy market. A generic and OWT centric software packages that can smartly adapt to the requirement of any offshore wind farm and optimise its maintenance, logistics and spares-holding while giving due consideration to offshore climate and market conditions will enable OWT operators to centralise their operation and maintenance planning and make significant cost reductions. This work aims to introduce the idea of a comprehensive tool that can meet the above objectives, and give examples of data and functions required. The package uses wind turbine condition monitoring data to anticipate component failure and proposes a time and maintenance implementation strategies that is developed as per the requirements of HSE and government regulations for working in the offshore locations and at heights. The software database contains key failure analysis data that will be an invaluable asset for future researchers, turbine manufacturers and operators, that will optimise OWT power generation cost and better understand OWT working. The work also lists some prevalent tools and techniques developed by industries and researchers for the wind industry.

Keywords-) Modular multilevel converter, pulse width modulation, selective harmonic elimination, Newton-Raphson method