Using Gaussian process theory for wind turbine power curve analysis with emphasis on the confidence intervals

Pandit, Ravi; Infield, David

doi:10.1109/iccep.2017.8004774

Cited by 11 publications

(6 citation statements)

References 7 publications

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“…erefore, the predictive variance only accounts for the spatial uncertainty due to the model development. e extension to consider the stochastic model can be found in the research studied by Pandit and Infield [28].…”

Section: Fault Diagnosis With a Gaussian Process Metamodelmentioning

confidence: 99%

Fault Diagnosis Strategy for Wind Turbine Generator Based on the Gaussian Process Metamodel

Zhang

Yuan

Zhu

et al. 2020

Mathematical Problems in Engineering

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To facilitate continuous development of the wind power industry, maintaining technological innovation and reducing cost per kilowatt hour of the electricity generated by the wind turbine generator system (WTGS) are effective measures to facilitate the industrial development. Therefore, the improvement of the system availability for wind farms becomes an important issue which can significantly reduce the operational cost. To improve the system availability, it is necessary to diagnose the system fault for the wind turbine generator so as to find the key factors that influence the system performance and further reduce the maintenance cost. In this paper, a wind farm with 200 MW installed capacity in eastern coastal plain in China is chosen as the research object. A prediction model of wind farm’s faults is constructed based on the Gaussian process metamodel. By comparing with actual observation results, the constructed model is proved able to predict failure events of the wind turbine generator accurately. The developed model is further used to analyze the key factors that influence the system failure. These are conducive to increase the running and maintenance efficiency in wind farms, shorten downtime caused by failure, and increase earnings of wind farms.

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Section: Fault Diagnosis With a Gaussian Process Metamodelmentioning

confidence: 99%

Fault Diagnosis Strategy for Wind Turbine Generator Based on the Gaussian Process Metamodel

Zhang

Yuan

Zhu

et al. 2020

Mathematical Problems in Engineering

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“…Wind farm developers and operators are continuously searching for cost‐effective strategies to minimise O&M costs, improve reliability and safety, and increase the return of investment [5]. Offshore wind farm maintenance can be planned, condition‐based, or corrective, but the harsh offshore operational environment can lead to increase passive downtime [6].…”

Section: Introductionmentioning

confidence: 99%

Data‐driven weather forecasting models performance comparison for improving offshore wind turbine availability and maintenance

Pandit

Kolios

Infield

2020

IET Renewable Power Generation

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Wind energy is an attractive alternative to conventional sources of electricity generation due to its effectively zero carbon emissions. Wind power is highly dependent on wind speed and operations offshore are affected by wave height; these together called turbine weather datasets that are variable and intermittent over various timescales and signify offshore weather conditions. In contrast to onshore wind, offshore wind requires improved forecasting since unfavourable weather prevents repair and maintenance activities. Delayed repair results in increased downtime and reduced wind farm availability and energy yield. This paper proposes two data-driven models for long-term weather conditions forecasting to improve the wind farm availability and support operation and maintenance (O&M) decision-making process. These two data-driven approaches are Long Short-Term Memory Network, abbreviated as LSTM, and Markov chain. A LSTM is an artificial recurrent neural network (RNN), capable of learning long-term dependencies within a sequence of data and is typically used to avoid the long-term dependency problem. While, Markov is another data-driven stochastic model, which assumes that, the future states depend only on the current states, not on the events that occurred before. The readily available weather datasets are obtained from FINO3 database to train and validate the performance of these data-driven models. A performance comparison between these weather forecasted models would be carried out to determine which approach is most accurate and suitable for improving offshore wind turbine availability and support maintenance activities. The full paper outlines the weakness and strength associated with proposed models in relations to offshore wind farms operational activities.

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“…The proposed PC2-Dev method is verified by the actual wind field data and can be exploited to assess health status. References [16]- [17] proposed to use gaussian theory to analyze data of WTs for condition monitoring. The experimental results show that gaussian theory has great advantages in analyzing WTs data.…”

Section: Introductionmentioning

confidence: 99%

Multi-Running State Health Assessment of Wind Turbines Drive System Based on BiLSTM and GMM

et al. 2020

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With the continuous elevation of demand for large-scale wind turbines and operation & maintenance cost an increasing interest has been rapidly generated on CM (Condition Monitoring) system. The main components of wind turbines are the focus on all CM as they overall lead to high repair costs and equipment downtime. Thus, it is difficult to make comprehensive assessment in the assessment. In the present study, intelligent machine learning algorithms are adopted to mine SCADA (Supervisory Control and Data Acquisition) system data of WTs (wind turbines). Besides, based on bidirectional long short-term memory (BiLSTM) neural networks and gaussian mixture model (GMM) algorithm, this study developed a multi-running state health assessment model for the drive system of wind turbines. First, the stateidentification model is built with health data to overcome the effect of the time-varying characteristics of running environment and alterations of running condition during the assessment. Then, in each state, the BiLSTM algorithm is adopted to extract the residual set of valid state variables, and the GMM algorithm is employed to accurately fit the distribution of residual set. The multi-running state benchmark model based on BiLSTM and GMM is built. Subsequently, the drive system of wind turbines health degree is calculated by health index. Lastly, based on multiple driving system faults data of a wind turbine, the feasibility and validity of the model are verified.

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Using Gaussian process theory for wind turbine power curve analysis with emphasis on the confidence intervals

Cited by 11 publications

References 7 publications

Fault Diagnosis Strategy for Wind Turbine Generator Based on the Gaussian Process Metamodel

Fault Diagnosis Strategy for Wind Turbine Generator Based on the Gaussian Process Metamodel

Data‐driven weather forecasting models performance comparison for improving offshore wind turbine availability and maintenance

Multi-Running State Health Assessment of Wind Turbines Drive System Based on BiLSTM and GMM

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