scholarly journals SCADA Data-Based Support Vector Machine Wind Turbine Power Curve Uncertainty Estimation and Its Comparative Studies

2020 ◽  
Vol 10 (23) ◽  
pp. 8685
Author(s):  
Ravi Pandit ◽  
Athanasios Kolios
Keyword(s):  
Support Vector Machine ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Offshore Wind ◽  
Data Driven ◽  
Accurate Estimation ◽  
Support Vector ◽  
Offshore Wind Turbine ◽  
Power Curve ◽  
Power Curves

Power curves, supplied by turbine manufacturers, are extensively used in condition monitoring, energy estimation, and improving operational efficiency. However, there is substantial uncertainty linked to power curve measurements as they usually take place only at hub height. Data-driven model accuracy is significantly affected by uncertainty. Therefore, an accurate estimation of uncertainty gives the confidence to wind farm operators for improving performance/condition monitoring and energy forecasting activities that are based on data-driven methods. The support vector machine (SVM) is a data-driven, machine learning approach, widely used in solving problems related to classification and regression. The uncertainty associated with models is quantified using confidence intervals (CIs), which are themselves estimated. This study proposes two approaches, namely, pointwise CIs and simultaneous CIs, to measure the uncertainty associated with an SVM-based power curve model. A radial basis function is taken as the kernel function to improve the accuracy of the SVM models. The proposed techniques are then verified by extensive 10 min average supervisory control and data acquisition (SCADA) data, obtained from pitch-controlled wind turbines. The results suggest that both proposed techniques are effective in measuring SVM power curve uncertainty, out of which, pointwise CIs are found to be the most accurate because they produce relatively smaller CIs. Thus, pointwise CIs have better ability to reject faulty data if fault detection algorithms were constructed based on SVM power curve and pointwise CIs. The full paper will explain the merits and demerits of the proposed research in detail and lay out a foundation regarding how this can be used for offshore wind turbine conditions and/or performance monitoring activities.

scholarly journals Comparison of New Anomaly Detection Technique for Wind Turbine Condition Monitoring Using Gearbox SCADA Data

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5152
Author(s):  
Conor McKinnon ◽  
James Carroll ◽  
Alasdair McDonald ◽  
Sofia Koukoura ◽  
David Infield ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Anomaly Detection ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Support Vector ◽  
Specific Training ◽  
Average Accuracy ◽  
Operations And Maintenance ◽  
Novel Method ◽  
Isolation Forest

Anomaly detection for wind turbine condition monitoring is an active area of research within the wind energy operations and maintenance (O & M) community. In this paper three models were compared for multi-megawatt operational wind turbine SCADA data. The models used for comparison were One-Class Support Vector Machine (OCSVM), Isolation Forest (IF), and Elliptical Envelope (EE). Each of these were compared for the same fault, and tested under various different data configurations. IF and EE have not previously been used for fault detection for wind turbines, and OCSVM has not been used for SCADA data. This paper presents a novel method of condition monitoring that only requires two months of data per turbine. These months were separated by a year, the first being healthy and the second unhealthy. The number of anomalies is compared, with a greater number in the unhealthy month being considered correct. It was found that for accuracy IF and OCSVM had similar performances in both training regimes presented. OCSVM performed better for generic training, and IF performed better for specific training. Overall, IF and OCSVM had an average accuracy of 82% for all configurations considered, compared to 77% for EE.

scholarly journals Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System

2019 ◽  
Vol 9 (4) ◽  
pp. 783 ◽  
Author(s):  
Silvio Simani ◽  
Paolo Castaldi
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Operating Conditions ◽  
Fault Detection And Isolation ◽  
Offshore Wind ◽  
Data Driven ◽  
Offshore Wind Turbine ◽  
Wind Turbine System ◽  
High Degree ◽  
Offshore Plants

Fault diagnosis of wind turbine systems is a challenging process, especially for offshore plants, and the search for solutions motivates the research discussed in this paper. In fact, these systems must have a high degree of reliability and availability to remain functional in specified operating conditions without needing expensive maintenance works. Especially for offshore plants, a clear conflict exists between ensuring a high degree of availability and reducing costly maintenance. Therefore, this paper presents viable fault detection and isolation techniques applied to a wind turbine system. The design of the so-called fault indicator relies on an estimate of the fault using data-driven methods and effective tools for managing partial knowledge of system dynamics, as well as noise and disturbance effects. In particular, the suggested data-driven strategies exploit fuzzy systems and neural networks that are used to determine nonlinear links between measurements and faults. The selected architectures are based on nonlinear autoregressive with exogenous input prototypes, which approximate dynamic relations with arbitrary accuracy. The designed fault diagnosis schemes were verified and validated using a high-fidelity simulator that describes the normal and faulty behavior of a realistic offshore wind turbine plant. Finally, by accounting for the uncertainty and disturbance in the wind turbine simulator, a hardware-in-the-loop test rig was used to assess the proposed methods for robustness and reliability. These aspects are fundamental when the developed fault diagnosis methods are applied to real offshore wind turbines.

scholarly journals Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System **

2018 ◽  
Author(s):  
Silvio Simani ◽  
Paolo Castaldi
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Fault Detection And Isolation ◽  
Offshore Wind ◽  
Data Driven ◽  
Offshore Wind Turbine ◽  
Isolation Techniques ◽  
Wind Turbine System ◽  
Offshore Installations ◽  
Fault Indicator

The fault diagnosis of wind turbine systems represent a challenging issue, especially for offshore installations, thus justifying the research topics developed in this work. Therefore, this paper addresses the problem of the fault diagnosis of wind turbines, and it present viable solutions of fault detection and isolation techniques. The design of the so--called fault indicator consists of its estimate, which involves data--driven methods, as they result effective tools for managing partial analytical knowledge of the system dynamics, together with noise and disturbance effects. In particular, the suggested data--driven strategies exploit fuzzy systems and neural networks that are employed to determine nonlinear links between measurements and faults. The selected architectures are based on nonlinear autoregressive with exogenous input prototypes, as they approximate the dynamic evolution of the system along time. The designed fault diagnosis schemes are verified via a high--fidelity simulator, which describes the normal and the faulty behaviour of an offshore wind turbine plant. Finally, by taking into account the presence of uncertainty and disturbance implemented in the wind turbine simulator, the robustness and the reliability features of the proposed methods are also assessed. This aspect is fundamental when the proposed fault diagnosis methods have to be applied to offshore installations.

scholarly journals Wind Turbine Power Curve Modeling with a Hybrid Machine Learning Technique

2019 ◽  
Vol 9 (22) ◽  
pp. 4930 ◽  
Author(s):  
Shenglei Pei ◽  
Yifen Li
Keyword(s):  
Wind Turbine ◽  
Wind Power ◽  
Power Output ◽  
Parametric Models ◽  
Wind Data ◽  
Support Vector ◽  
Power Curve ◽  
Hybrid Strategy ◽  
Curve Modeling ◽  
Power Curves

A power curve of a wind turbine describes the nonlinear relationship between wind speed and the corresponding power output. It shows the generation performance of a wind turbine. It plays vital roles in wind power forecasting, wind energy potential estimation, wind turbine selection, and wind turbine condition monitoring. In this paper, a hybrid power curve modeling technique is proposed. First, fuzzy c-means clustering is employed to detect and remove outliers from the original wind data. Then, different extreme learning machines are trained with the processed data. The corresponding wind power forecasts can also be obtained with the trained models. Finally, support vector regression is used to take advantage of different forecasts from different models. The results show that (1) five-parameter logistic function is superior to the others among the parametric models; (2) generally, nonparametric power curve models perform better than parametric models; (3) the proposed hybrid model can generate more accurate power output estimations than the other compared models, thus resulting in better wind turbine power curves. Overall, the proposed hybrid strategy can also be applied in power curve modeling, and is an effective tool to get better wind turbine power curves, even when the collected wind data is corrupted by outliers.

Design, Modelling and Analysis of a Combined Semi-Submersible Floating Wind Turbine and Wave Energy Point-Absorber

2013 ◽  
Author(s):  
Imanol Touzón González ◽  
Pierpaolo Ricci ◽  
Miren Josune Sánchez Lara ◽  
Germán Pérez Morán ◽  
Francesco Boscolo Papo
Keyword(s):  
Wind Turbine ◽  
Wave Energy ◽  
Frequency Domain Analysis ◽  
Economic Feasibility ◽  
Domain Model ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Power Performance ◽  
Point Absorber ◽  
Power Curves

Floating platforms for offshore wind tend to be very expensive and different solutions have been proposed to increase their cost-effectiveness. One of them involves the combination of offshore wind generation with other forms of ocean renewable energy, as is the subject of the FP7 project Marina Platform. In particular, wave energy from the sea has been investigated since the ’70s and although a few technologies have already reached a pre-commercial stage, their actual economic feasibility can still be questioned so that the possibility of sharing cables, moorings and even the structure with offshore wind turbine is very interesting also from the point of view of wave energy developers. This paper presents the design, modeling and analysis of a combined concept composed of a semi-submersible platform hosting a single 5 MW wind turbine and a heaving point-absorber consisting of a floating cylinder placed at the geometric center of the platform. A preliminary design of the concept is carried out by a frequency-domain analysis focused on the definition of the most suitable geometry with the objective of a limited dynamic response of the platform and satisfactory wave power absorption at the same time. It is shown how the requirement of maintaining reduced amplitude on the platform motions imposes the adoption of relatively slender cylinders as point-absorbers. After a conventional catenary mooring arrangement is assumed and its basic line parameters determined by applying a quasi-static approach, a global coupled time-domain model is built based on the Cummins equation and the use of panel codes (e.g. WAMIT, AQWA) for the computation of the hydrodynamic coefficients. Moorings are modeled as individual catenary lines whereas the dynamics of the wind turbine are modeled by introducing thrust and power curves as function of the motions of the platform, after previous determination with the Blade Element Momentum theory. The analysis is carried out over a set of operational sea states for different locations around Europe. Through the analysis of power performance, platform and point-absorber motions and mooring tensions, it is shown how the introduction of a Wave Energy Converter (WEC) can occasionally have a positive effect on the whole response of the platform though the significance of its energy contribution is relatively small and additional synergies have to be sought to justify its adoption.

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