Reliability analysis for wind turbines with incomplete failure data collected from after the date of initial installation

Guo, Haitao; Watson, Simon J.; Tavner, Peter; Xiang, Jianping

doi:10.1016/j.ress.2008.12.004

Cited by 141 publications

(67 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The life cycle stages of Concept, Design, Production, Utilisation, Support and Retirement apply equally in the wind energy industry, which has adopted and incorporated computer aided tools for design and data management from its infancy. As noted by Guo [19] and Hameed [20] the main life cycle stage where data management issues persist is during Utilisation/Support. This is because of the gaps that currently exist in the ability to capture, feedback and re-use in-service information of wind power plants.…”

Section: Data Management Within Each Domainmentioning

confidence: 99%

Product Life Cycle Data Management: A Cross-Sectoral Review

Ford¹,

Bartley²,

Igba³

et al. 2013

Product Lifecycle Management for Society

View full text Add to dashboard Cite

Abstract. The paper explores data management in the product life cycle of three engineering domains -civil/construction, marine and wind energy -each with distinct but potentially common issues. The approach has been to assess issues within each domain against the life cycle stages defined in ISO 15288 "Systems and software engineering -System life cycle processes", i.e., Concept, Development, Utilisation, Support and Retirement. These were then assimilated and comparisons drawn to identify common problems and areas that appear particular to the do-main. The paper presents a position statement, taken from the experience of practitioners in each of the relevant sectors; the purpose is to understand if there may be opportunities for cross-sectoral learning.Keywords: Information management, Lifecycle, Civil, Marine, Wind. IntroductionThe management of engineering data for the operation and maintenance of large complex artefacts such as ships, buildings and power generation systems is very challenging owing to the long life spans, multiplicity of stakeholders, diversity of data types and complexity of interactions in the artefacts and supporting systems. This paper explores these challenges and in particular makes a cross-sectoral analysis of issues across three distinct engineering domains: construction and civil engineering, marine (UK Defence) and renewable energy (offshore wind). Initial comparison of the domains would indicate a number of similarities, e.g., complexity, capital investment, but none-the-less significant differences exist, in business/engineering maturity and structure of the supporting industry, which have strong implications for data management approaches in each domain. The purpose of the paper was to explore opportunities for engineering communities to share good practice with others and identify issues which are still to be resolved across all engineering sectors. BackgroundThe three sectors under study are construction/civil engineering, marine and wind energy. Each is characterised by large, long-lived artefacts and by distributed operations, but there are differences, especially in the nature of the industries.

show abstract

Section: Data Management Within Each Domainmentioning

confidence: 99%

Product Life Cycle Data Management: A Cross-Sectoral Review

Ford¹,

Bartley²,

Igba³

et al. 2013

Product Lifecycle Management for Society

View full text Add to dashboard Cite

show abstract

“…Therefore, the yaw system has a high failure rate, and is time-consuming and difficult to repair once broken [13][14][15][16][17]. If the wind turbine cannot be accurately aligned toward the wind direction because of yaw error or failure, the conditions of the rotor load become worse and the power generation efficiency is reduced or threatens the safe operation of the wind turbine.…”

Section: Introductionmentioning

confidence: 99%

Effects of Yaw Error on Wind Turbine Running Characteristics Based on the Equivalent Wind Speed Model

2015

View full text Add to dashboard Cite

Natural wind is stochastic, being characterized by its speed and direction which change randomly and frequently. Because of the certain lag in control systems and the yaw body itself, wind turbines cannot be accurately aligned toward the wind direction when the wind speed and wind direction change frequently. Thus, wind turbines often suffer from a series of engineering issues during operation, including frequent yaw, vibration overruns and downtime. This paper aims to study the effects of yaw error on wind turbine running characteristics at different wind speeds and control stages by establishing a wind turbine model, yaw error model and the equivalent wind speed model that includes the wind shear and tower shadow effects. Formulas for the relevant effect coefficients Tc, Sc and Pc were derived. The simulation results indicate that the effects of the aerodynamic torque, rotor speed and power output due to yaw error at different running stages are different and that the effect rules for each coefficient are not identical when the yaw error varies. These results may provide theoretical support for optimizing the yaw control strategies for each stage to increase the running stability of wind turbines and the utilization rate of wind energy.

show abstract

“…The cost data are given in Table 2, including the failure replacement costs for the components, the fixed and variable preventive replacement costs with respect to each type and the fixed cost of sending a maintenance team to the wind farm. All data used in this example are specified based on the data given in our previous study in Tian et al (2011), which are constructed based on reported data in the literature (WindStats Newsletter, 2008Guo et al, 2009;Fingersh et al, 2006). In addition, the inspection interval is set at ten days.…”

Section: Example Introductionmentioning

confidence: 99%

Condition-based maintenance of wind power generation systems considering different turbine types and lead times

Ding¹,

Tian²,

Amayri³

2014

IJSEAM

View full text Add to dashboard Cite

Condition monitoring measurements, such as vibration data, acoustic emission data, oil analysis data, power voltage and current data, etc., can be obtained from wind turbine components and be utilised to evaluate and predict the health conditions of the components and the turbines. The objective of condition-based maintenance (CBM) is to optimise the predictive maintenance activities based on the condition monitoring and prediction information to minimise the overall costs of wind power generation systems. In existing work, all the wind turbines are assumed to be of the same type and the lead times of different components are assumed to be constant. This is not the case in many practical applications. In this paper, we develop a CBM approach for wind turbine systems considering different types of wind turbines in a wind farm and different lead times for different turbine components, which lead to more accurate modelling of CBM activities in actual wind farms. In the proposed CBM approach, we present a new CBM policy involving two design variables for each turbine type, a method for turbine failure probability evaluation considering different lead times and a CBM cost evaluation method. Numerical examples are provided to demonstrate the proposed CBM approach.

show abstract

Reliability analysis for wind turbines with incomplete failure data collected from after the date of initial installation

Cited by 141 publications

References 23 publications

Product Life Cycle Data Management: A Cross-Sectoral Review

Product Life Cycle Data Management: A Cross-Sectoral Review

Effects of Yaw Error on Wind Turbine Running Characteristics Based on the Equivalent Wind Speed Model

Condition-based maintenance of wind power generation systems considering different turbine types and lead times

Contact Info

Product

Resources

About