Abstract
This paper presents a method for the probabilistic evaluation of the failure rates of mechanical components in a typical power take-off (PTO) system of a horizontal-axis tidal stream turbine (HATT). The method is based on a modification of the method of the influence factors, when base failure rates, relevant influence factors and, subsequently, resulting failure rates are treated as random variables. The prior (i.e., initial) probabilistic distribution of the failure rates of a HATT component is generated using data for similar components from other industries, while taking into account actual characteristics of the component and site-specific operating and environmental conditions of the HATT. A posterior distribution of the failure rate is estimated numerically based on a Bayesian approach as new information about the component performance in an operating HATT becomes available. The posterior distribution is then employed to obtain the updated mean and lower and upper confidence limits of the failure rate. The proposed method is illustrated by applying it to the evaluation of the failure rates of two key components of the PTO system of a typical HATT—main seal and main bearing. In particular, it is shown that uncertainty associated with the method itself has a major influence on the failure rate evaluation. The proposed method is useful for the reliability assessment of both PTO designs of new HATTs and PTO systems of operating HATTs.
Highlights
Ocean and sea tides are an important source of renewable energy
The technology has been intensively developed in recent times and just reached a commercial stage; in particular, this concerns horizontal axis tidal turbines (HATTs), which are similar to typical wind turbines and, at present, the dominant type of tidal stream turbines [2,3]
The present paper describes a method for the probabilistic evaluation of the failure rates of mechanical components in a typical power take-off (PTO) system of a HATT
Summary
Ocean and sea tides are an important source of renewable energy. the theoretical potential of tidal energy (1200 TWh/year) is much smaller than that of waves (29,500 TWh/year) or offshore wind (420,000 TWh/year), the former source of energy has one major advantage over the latter—since tides do not depend on weather their energy is well predictable both in the short and long term in contrast to that of waves and wind [1,2]. The method addresses the issues mentioned above in connection to [13], i.e., (i) the influence factors account for the site-specific operating and environmental conditions of a HATT, and (ii) the probabilistic distributions of the failure rates can be updated using new data on the HATT’s performance collected during its operational life. The latter is based on a Bayesian approach that uses data for similar components from other industries to construct the prior distribution of the base failure rate for a component.
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