Applicability of machine learning approaches for structural damage detection of offshore wind jacket structures based on low resolution data

Structural damage in offshore wind jacket support structures are relatively unlikely due to the precautions taken in design but it could imply dramatic consequences if undetected. This work explores the possibilities of damage detection when using low resolution data, which are available with lower costs compared to dedicated high-resolution structural health monitoring. Machine learning approaches showed to be generally feasible for detecting a structural damage based on SCADA data collected in a simulation environment. Focus is here given to investigate model uncertainties, to assess the applicability of machine learning approaches for reality. Two jacket models are utilised representing the as-designed and the as-installed system, respectively. Extensive semi-coupled simulations representing different operating load cases are conducted to generate a database of low-resolution signals serving the machine learning training and testing. The analysis shows the challenges of classification approaches, i.e. supervised learning aiming to separate healthy and damage status, in coping with the uncertainty in system dynamics. Contrarily, an unsupervised novelty detection approach shows promising results when trained with data from both, the as-designed and the as-installed system. The findings highlight the importance of investigating model uncertainties and careful selection of training data

The Financial Benefits of Various Catastrophic Failure Prevention Strategies in a Wind Farm: Two market studies (UK-Spain)

Operation of wind farms is driven by the overall aim of minimising costs while maximising energy sales. However, in certain circumstances investments are required to guarantee safe operation and survival of an asset. In this paper, we discuss the merits of various catastrophic failure prevention strategies in a Spanish wind farm. The wind farm operator was required to replace blades in two phases: temporary and final repair. We analyse the power performance of the turbine in the different states and investigate four scenarios with different timing of temporary and final repair during one year. The financial consequences of the scenarios are compared with a baseline by using a discounted cash flow analysis that considers the wholesale electricity market selling prices and interest rates. A comparison with the UK electricity market is conducted to highlight differences in the rate of return in the two countries

Optimisation of Data Acquisition in Wind Turbines with Data-Driven Conversion Functions for Sensor Measurements

Operation and Maintenance (O&M) is an important cost driver of modern wind turbines. Condition monitoring (CM) allows the implementation of predictive O&M strategies helping to reduce costs. In this work a novel approach for wind turbine condition monitoring is proposed focusing on synergistic effects of coexisting sensing technologies. The main objective is to understand the predictability of signals using information from other measurements recorded at different locations of the turbine. The approach is based on a multi-step procedure to pre-process data, train a set of conversion functions and evaluate their performance. A subsequent sensitivity analysis measuring the impact of the input variables on the predicted response reveals hidden relationships between signals. The concept feasibility is tested in a case study using Supervisory Control And Data Acquisition (SCADA) data from an offshore turbine

Feasibility of machine learning algorithms for classifying damaged offshore jacket structures using SCADA data

The best practise for structural damage detection currently relies on the installation of structural health monitoring systems for the collection of dedicated high frequency measurements. Switching to the employment of the wind turbine's SCADA (Supervisory Control and Data Acquisition) signals and their commonly recorded low frequency statistics can lead to a reduction in the number of ad-hoc monitoring sensors and quantity of data required. In this paper, aero-hydro-servo-elastic simulations for a model of a turbine are used to assess its loads and any changes in the dynamics under healthy state and a damaged configuration case study. To prove the feasibility of the damage detection through low-resolution data, the statistics of the typically recorded signals from the SCADA and the structural monitoring systems are fed into a database for training and testing of classification algorithms. The ability of the machine learning models to generalise the classification for both stochasticity and uncertainties in the environmental conditions are tested. Decision tree-based classifiers showed the capability to capture the damage for the majority of the operating conditions considered. Though the setup of the traditional SCADA sensors had to be supplemented with an additional structural health monitoring sensor, the detection of the damage has been shown feasible by referring to low-frequency statistics only

Statistical Evaluation of SCADA data for Wind Turbine Condition Monitoring and Farm Assessment

Operational data from wind farms is crucial for wind turbine condition monitoring and performance assessment. In this paper, we analyse three wind farms with the aim to monitor environmental and operational conditions that might result in underperformance or failures. The assessment includes a simple wind speed characterisation and wake analysis. The evolution of statistical parameters is used to identify anomalous turbine behaviour. In total, 88 turbines and 12 failures are analysed, covering different component failures. Notwithstanding the short period of data available, several operational parameters are found to deviate from the farm trend in some turbines affected by failures. As a result, some parameters show better monitoring capabilities than others, for the detection of certain failures. However, the limitations of SCADA statistics are also shown as not all failures showed anomalies in the observed parameters.</p

Statistical Evaluation of SCADA data for Wind Turbine Condition Monitoring and Farm Assessment

Operational data from wind farms is crucial for wind turbine condition monitoring and performance assessment. In this paper, we analyse three wind farms with the aim to monitor environmental and operational conditions that might result in underperformance or failures. The assessment includes a simple wind speed characterisation and wake analysis. The evolution of statistical parameters is used to identify anomalous turbine behaviour. In total, 88 turbines and 12 failures are analysed, covering different component failures. Notwithstanding the short period of data available, several operational parameters are found to deviate from the farm trend in some turbines affected by failures. As a result, some parameters show better monitoring capabilities than others, for the detection of certain failures. However, the limitations of SCADA statistics are also shown as not all failures showed anomalies in the observed parameters.Wind Energ

Sensitivity study of a wind farm maintenance decision - A performance and revenue analysis

Commercial operation and maintenance of wind farms always involves trying to find the most cost-effective solution from various possible options. In this paper, a maintenance action within a Spanish wind farm was studied, whereby a blade replacement was required to prevent catastrophic failure. The conducted replacement was accompanied by an underperformance resolved in a later blade re-pitching. We analyse the decision taken in terms of the power performance and net present value from the cash flow resulting from the energy sales. The impact of the timing of the maintenance is discussed in various what-if scenarios. The sensitivity to environmental causes of underperformance is compared by varying the duration of blade icing and comparing the performance in different wind directions. Country dynamics and subsidy impacts are hypothetically evaluated for the prevailing electricity market conditions as if the turbine were operating in either Spain, Netherlands or the UK. The findings highlight the uncertainty in power performance and the importance of maintenance accuracy. It is shown that the decision-making of operators should not only consider the seasonality of the wind resource, but also the seasonality in electricity markets.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Wind Energ

The Financial Benefits of Various Catastrophic Failure Prevention Strategies in a Wind Farm: Two market studies (UK-Spain)

This is an Open Access Article. It is published by IOP Publishing under the Creative Commons Attribution 3.0 Unported Licence (CC BY). Full details of this licence are avalable at: http://creativecommons.org/licenses/by/3.0/Operation of wind farms is driven by the overall aim of minimising costs while maximising energy sales. However, in certain circumstances investments are required to guarantee safe operation and survival of an asset. In this paper, we discuss the merits of various catastrophic failure prevention strategies in a Spanish wind farm. The wind farm operator was required to replace blades in two phases: temporary and final repair. We analyse the power performance of the turbine in the different states and investigate four scenarios with different timing of temporary and final repair during one year. The financial consequences of the scenarios are compared with a baseline by using a discounted cash flow analysis that considers the wholesale electricity market selling prices and interest rates. A comparison with the UK electricity market is conducted to highlight differences in the rate of return in the two countries