Addressing failure rate uncertainties of marine energy converters

Abstract

The interest in marine renewable energy is strong, but has not led to significant commercial scale investment and deployment, yet. To attract investors and promote the development of a marine renewable industry a clear concept of project risk is paramount, in particular issues relating to device reliability are critical. In the public domain, reliability information is often scarce or inappropriate at this early stage of development, as little operational experience has been gained. Thus, reliability estimates are fraught with large uncertainties. This paper explores sources and magnitudes of failure rate uncertainty and demonstrates the effect on reliability estimates for a notional marine energy converter. If generic failure rate data forms the basis of a reliability assessment, reliability estimates are not robust and may significantly over-or underestimate system reliability. The Bayesian statistical framework provides a method to overcome this issue. Generic data can be updated with more specific information that could not be statistically incorporated otherwise. It is proposed that adopting such an approach at an early stage in an iterative process will lead to an improved rate of certainty.

Updatable Probabilistic Evaluation of Failure Rates of Mechanical Components in Power Take-Off Systems of Tidal Stream Turbines

“…The lack of historical failure data creates significant uncertainty in relation to the failure rate estimation, which needs to be dealt with in a consistent and rational way. A Bayesian approach is a tool for doing this; e.g., it has been employed for the estimation of failure rates in the nuclear industry [20] and updating the failure rates of components of marine energy converters using test data [21].…”

Reducing Reliability Uncertainties for Marine Renewable Energy

“…Clearly the use of the naval, sheltered factor will result in a conservative failure rate which is representative of equipment exposed to a non-benign environment and indeed it would be prudent to use field data even if it is from a different industry (i.e., offshore wind [14]). In the absence of actual performance data a failure estimation based on this approach is highly simplified and resulting predictions are heavily influenced [15] by the following assumptions: (i) it focuses only on the central portion of the "bathtub" curve; (ii) it does not take into account developments in manufacturing or design to improve reliability; and (iii) it treats failures as independent events in a system (i.e., it ignores cascade failures). Although it is acknowledged that the widely used bottom-up statistical method [16] does have some shortcomings, given the current lack of detailed operational data in the MRE sector (particularly shared data) this method is suited to the present state of the sector.…”

“…Physical component test data provides a means of reducing the lifetime estimate uncertainties associated with using generic failure rates (which may be unverified and potentially unsuitable for the application) prior to gaining operational experience gained in the field. In particular testing allows failure modes to be established prior to installation, providing insight into component wear and reliability and thus allowing confidence levels of long-term installation durability to increase [15]. In the early design stages (TRLs <5) the expected operating conditions may not be fully defined and component performance may be unknown.…”

Operation and maintenance strategies for wave energy converters

“…From the weather data, a methodology for O&M cost estimations can be developed as done for offshore wind turbines, 7,8 where weather data and failures of components are directly included and modelled. WECspecific failure rates of components can be estimated directly from experiments 9 or can be adapted from failure rate data from nearby industries based on so-called adjustment factors 10 or by the use of Bayesian statistics. 11 This article presents a methodology for WEC application which incorporates weather data and failure formation of the considered components (damage accumulation) directly in a dynamic simulation of O&M actions.…”