A Bayesian Approach for Predicting Wind Turbine Failures based on Meteorological Conditions

With the growing wind energy sector, the need for advanced operation and maintenance (O&M) strategies has emerged. So far, mainly corrective or preventive O&M actions are applied. Predictive modelling, however, is expected to significantly enhance existing O&M practice. Here, anticipating wind turbine component failures can enable operators to lower the O&M cost and is particularly useful for wind farms located in remote areas or offshore locations. Previous research has shown that the failure behaviour of wind turbines and their components is highly influenced by the meteorological conditions under which the turbines operate. Hence, there is a significant need for robust models for failure prediction taking into consideration these conditions. Furthermore, solutions need to be found in order to determine the most suitable input variables for enhancing their prediction accuracy. This study uses failure data obtained from 984 wind turbines during 87 operational WT years. Bayesian belief networks (BBN) are trained based on failure records, technology specific covariates, as well as measurements of the environmental and operational conditions at site. Subsequently, the failure events in a wind farm during a period of 36 months are predicted with the BNN, whereas the failure events of six main components are predicted separately. Furthermore, an extensive sensitivity study is carried out to find the model with the highest prediction accuracy for each component. The influence of each meteorological, operational or technical covariate are discussed in detail. The models achieved a very good accuracy and were able to predict the majority of the component failures over the prediction period

Diagnosis of failures in solar plants based on performance monitoring

Photovoltaic (PV) solar energy has become a reference in electrical generation. The plants currently installed, and those planned have a huge capacity and occupy large areas. The increase in size of the plants presents new challenges in operation and maintenance areas, such as the optimization of the number of sensors installed, large data management and the reduction of the timework in maintenance. The aim of this paper is to show a methodology, to diagnose failures, based on the measured data in the plant. The methodology used is supervised regression machine learning and comparison algorithms. This methodology allows the study of the sensors, the inverters, the joint boxes and the power reduction caused by soiling. The result would allow the detection of around 1-5% of production loss in the plant. The algorithms have been tested with real data of PV plants, and have detected common failures such as production drops in strings and losses due to soiling

Wind Turbine Failures - Tackling current Problems in Failure Data Analysis

The wind industry has been growing significantly over the past decades, resulting in a remarkable increase in installed wind power capacity. Turbine technologies are rapidly evolving in terms of complexity and size, and there is an urgent need for cost effective operation and maintenance (O&M) strategies. Especially unplanned downtime represents one of the main cost drivers of a modern wind farm. Here, reliability and failure prediction models can enable operators to apply preventive O&M strategies rather than corrective actions. In order to develop these models, the failure rates and downtimes of wind turbine (WT) components have to be understood profoundly. This paper is focused on tackling three of the main issues related to WT failure analyses. These are, the non-uniform data treatment, the scarcity of available failure analyses, and the lack of investigation on alternative data sources. For this, a modernised form of an existing WT taxonomy is introduced. Additionally, an extensive analysis of historical failure and downtime data of more than 4300 turbines is presented. Finally, the possibilities to encounter the lack of available failure data by complementing historical databases with Supervisory Control and Data Acquisition (SCADA) alarms are evaluated

SCADA alarms processing for wind turbine component failure detection

Wind turbine failure and downtime can often compromise the profitability of a wind farm due to their high impact on the operation and maintenance (O&M) costs. Early detection of failures can facilitate the changeover from corrective maintenance towards a pre- dictive approach. This paper presents a cost-effective methodology to combine various alarm analysis techniques, using data from the Supervisory Control and Data Acquisition (SCADA) system, in order to detect component failures. The approach categorises the alarms according to a reviewed taxonomy, turning overwhelming data into valuable information to assess component status. Then, different alarms analysis techniques are applied for two purposes: the evaluation of the SCADA alarm system capability to detect failures, and the investigation of the relation between components faults being followed by failure occurrences in others. Various case studies are presented and discussed. The study highlights the relationship between faulty behaviour in different components and between failures and adverse environmental conditions

Determining Remaining Lifetime of Wind Turbine Gearbox Using a Health Status Indicator Signal

Wind turbine component's failure prognosis allows wind farm owners to apply predictive maintenance techniques to their fleets. This permits optimal scheduling of the maintenance actions considering the best time to stop the turbines and perform those actions. Determining the health status of a turbine's component typically requires verifying a wide number of variables that should be monitored simultaneously. The scope of this study is the investigation and the selection of an effective combination of variables and smoothing and forecasting methodologies for obtaining a wind turbine gearbox health status indicator, in order to interpret clearly the remaining lifetime of the gearbox. The proposed methodology is based on Gaussian Mixture Copula Model (GMCM) models combined with the smoothing treatment and the forecasting model to define the health index of the wind turbine gearbox. Then, the resulting index is tested using various warning and critical thresholds. These thresholds should be chosen adequately in order to indicate appropriate inspection visit and preventive maintenance intervention date. Then, the best combination found, for the studied cases, was 50% and 70% for warning and critical respectively. This combination ensures that the developed procedure is capable of providing long enough time window for maintenance decision making

On the use of high-frequency SCADA data for improved wind turbine performance monitoring

SCADA-based condition monitoring of wind turbines facilitates the move from costly corrective repairs towards more proactive maintenance strategies. In this work, we advocate the use of high-frequency SCADA data and quantile regression to build a cost effective performance monitoring tool. The benefits of the approach are demonstrated through the comparison between state-of-the-art deterministic power curve modelling techniques and the suggested probabilistic model. Detection capabilities are compared for low and high-frequency SCADA data, providing evidence for monitoring at higher resolutions. Operational data from healthy and faulty turbines are used to provide a practical example of usage with the proposed tool, effectively achieving the detection of an incipient gearbox malfunction at a time horizon of more than one month prior to the actual occurrence of the failure

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

Sensitivity of Modern Lighting Technologies to Rapid Voltage Changes

Rapid Voltage Changes (RVCs) are one of the Power Quality disturbances that are recently receiving a lot of attention from the point of view of international standards. However, although they can cause or contribute to flicker, IEC 61000-4-15 only addresses periodic amplitude fluctuations and more effort is needed to regulate the occurrence of RVCs, according to their effect on flicker perceptibility. Alongside, flicker perception is challenged by the integration of modern lighting technology, whose response is different from the traditional incandescent lamp. This paper studies the connection between the increasing importance of RVCs and the evolution of illumination technologies. Sensitivity of modern lighting technologies to RVCs is studied by measuring flicker with a high precision light flickermeter. A large set of modern lamps is tested and the relationship between RVCs parameters and flicker perceptibility is analyzed

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

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