The Experts below are selected from a list of 86994 Experts worldwide ranked by ideXlab platform
Edwin Prem G. Kumar - One of the best experts on this subject based on the ideXlab platform.
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wind resource estimation using wind speed and Power Curve models
Renewable Energy, 2015Co-Authors: M. Lydia, Immanuel A. Selvakumar, Suresh S Kumar, Edwin Prem G. KumarAbstract:Abstract Estimation of wind resource in a given area helps in identifying potential sites for establishing wind farm and aids in the calculation of annual energy produced. Estimation of annual energy improves the wind Power penetration in the electricity grid and in electricity trading. In this paper, wind resource estimation has been carried out using wind speed forecasting models and wind turbine Power Curve model. The time series model of wind speed for day ahead forecasting is developed based on linear and non-linear autoregressive models with and without exogenous variables. The daily wind speed data of five different locations in New Zealand have been used for this analysis and the annual energy produced has been obtained. The standard deviation between the mean wind speed of the previous day and the mean wind speed during corresponding day five years and ten years ago has been used as exogenous variables. The neuralnet based non-linear model built using exogenous variables (NLARX) performs better in three locations and wavenet based non-linear model performs better in the remaining two locations. Wind resource is estimated using a wind turbine Power Curve modeled using a five parametric logistic expression, whose parameters were solved using Differential Evolution (DE).
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a comprehensive review on wind turbine Power Curve modeling techniques
Renewable & Sustainable Energy Reviews, 2014Co-Authors: M. Lydia, Immanuel A. Selvakumar, S. Suresh Kumar, Edwin Prem G. KumarAbstract:The wind turbine Power Curve shows the relationship between the wind turbine Power and hub height wind speed. It essentially captures the wind turbine performance. Hence it plays an important role in condition monitoring and control of wind turbines. Power Curves made available by the manufacturers help in estimating the wind energy potential in a candidate site. Accurate models of Power Curve serve as an important tool in wind Power forecasting and aid in wind farm expansion. This paper presents an exhaustive overview on the need for modeling of wind turbine Power Curves and the different methodologies employed for the same. It also reviews in detail the parametric and non-parametric modeling techniques and critically evaluates them. The areas of further research have also been presented.
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Advanced Algorithms for Wind Turbine Power Curve Modeling
IEEE Transactions on Sustainable Energy, 2013Co-Authors: M. Lydia, Immanuel A. Selvakumar, S. Suresh Kumar, Edwin Prem G. KumarAbstract:A wind turbine Power Curve essentially captures the performance of the wind turbine. The Power Curve depicts the relationship between the wind speed and output Power of the turbine. Modeling of wind turbine Power Curve aids in performance monitoring of the turbine and also in forecasting of Power. This paper presents the development of parametric and nonparametric models of wind turbine Power Curves. Parametric models of the wind turbine Power Curve have been developed using four and five parameter logistic expressions. The parameters of these expressions have been solved using advanced algorithms like genetic algorithm (GA), evolutionary programming (EP), particle swarm optimization (PSO), and differential evolution (DE). Nonparametric models have been evolved using algorithms like neural networks, fuzzy c-means clustering, and data mining. The modeling of wind turbine Power Curve is done using five sets of data; one is a statistically generated set and the others are real-time data sets. The results obtained have been compared using suitable performance metrics and the best method for modeling of the Power Curve has been obtained.
Yun Wang - One of the best experts on this subject based on the ideXlab platform.
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Wind Power Curve Modeling With Asymmetric Error Distribution
IEEE Transactions on Sustainable Energy, 2020Co-Authors: Yun Wang, Shenglei PeiAbstract:Wind Power Curves play important roles in developing and utilizing wind energy, including wind Power forecasting, wind turbine condition monitoring, wind energy potential estimation, and wind turbine selection. In this paper, we show that the error distribution in wind Power Curve modeling is asymmetric and complex, rather than a Gaussian distribution. Considering this error characteristic, we develop two novel asymmetric spline regression models, MoAG-ASR and MoAEP-ASR, in which the error terms are assumed to obey mixture of asymmetric Gaussian distributions (MoAG) and mixture of asymmetric exponential Power distribution (MoAEP), respectively. We compare the proposed models with 16 popular wind Power Curve fitting models, and find that the proposed models rank higher than the others. Moreover, besides the asymmetry, two statistics (skewness and kurtosis) also show that the error distribution in wind Power Curve modeling is thick-tailed and leptokurtic. However, MoAG and MoAEP can precisely fit such complex error distribution. Thus, MoAG-ASR and MoAEP-ASR are effective tools for wind Power Curve modeling.
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Wind Power Curve Modeling and Wind Power Forecasting With Inconsistent Data
IEEE Transactions on Sustainable Energy, 2019Co-Authors: Yun Wang, Dipti Srinivasan, Qinghua Hu, Zheng WangAbstract:Wind Power Curve modeling is a challenging task due to the existence of inconsistent data, in which the recorded wind Power is far away from the theoretical wind Power at a given wind speed. In this case, confronted with these samples, the estimated errors of wind Power will become large. Thus, the estimated errors will present two properties: heteroscedasticity and error distribution with a long tail. In this paper, according to the above-mentioned error characteristics, the heteroscedastic spline regression model (HSRM) and robust spline regression model (RSRM) are proposed to obtain more accurate Power Curves even in the presence of the inconsistent samples. The results of Power Curve modeling on the real-world data show the effectiveness of HSRM and RSRM in different seasons. As HSRM and RSRM are optimized by variational Bayesian, except the deterministic Power Curves, probabilistic Power Curves, which can be used to detect the inconsistent samples, can also be obtained. Additionally, with the data processed by replacing the wind Power in the detected inconsistent samples with the wind Power on the estimated Power Curve, the forecasting results show that more accurate wind Power forecasts can be obtained using the above-mentioned data processing method.
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Approaches to wind Power Curve modeling: A review and discussion
Renewable and Sustainable Energy Reviews, 2019Co-Authors: Yun Wang, Aoife Foley, Dipti SrinivasanAbstract:Abstract Wind Power Curves play important roles in wind Power forecasting, wind turbine condition monitoring, estimation of wind energy potential and wind turbine selection. In practice, it is a challenging task to produce reliable wind Power Curves from raw wind data due to the presence of outliers formed in unexpected conditions, e.g., wind curtailment and blade damage. This paper comprehensively reviews wind Power Curve modeling techniques from the perspective of modeling processes, i.e., wind data analyses, wind data preprocessing and various wind Power Curve models. Moreover, the performances of many popular Power Curve models are studied in different seasons and different wind farms. The results show that no universal wind Power Curve model can always perform better than other models under any environmental conditions. In general, there are three factors that affect the final wind Power Curves: data filtering approaches; wind Power Curve models; and choice of optimization strategies (especially the method applied to construct objective functions). However, there is no guarantee that all outliers will be removed from the raw wind data. Consequently, designing robust regression models or constructing robust objective functions may be two effective ways to obtain accurate Power Curves in the presence of outliers. The above two strategies depend largely on the error characteristics of Power Curve modeling. While it is often observed that the error distribution of the Power Curve modeling may be asymmetric, few researchers have considered this trait when building wind Power Curves. Therefore, this paper proposes several strategies that focus on designing asymmetric loss functions and developing robust regression models with asymmetric error distributions. Models that benefit from these characteristics may be more suitable for Power Curve modeling tasks and are more likely to produce better wind Power Curves.
M. Lydia - One of the best experts on this subject based on the ideXlab platform.
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wind resource estimation using wind speed and Power Curve models
Renewable Energy, 2015Co-Authors: M. Lydia, Immanuel A. Selvakumar, Suresh S Kumar, Edwin Prem G. KumarAbstract:Abstract Estimation of wind resource in a given area helps in identifying potential sites for establishing wind farm and aids in the calculation of annual energy produced. Estimation of annual energy improves the wind Power penetration in the electricity grid and in electricity trading. In this paper, wind resource estimation has been carried out using wind speed forecasting models and wind turbine Power Curve model. The time series model of wind speed for day ahead forecasting is developed based on linear and non-linear autoregressive models with and without exogenous variables. The daily wind speed data of five different locations in New Zealand have been used for this analysis and the annual energy produced has been obtained. The standard deviation between the mean wind speed of the previous day and the mean wind speed during corresponding day five years and ten years ago has been used as exogenous variables. The neuralnet based non-linear model built using exogenous variables (NLARX) performs better in three locations and wavenet based non-linear model performs better in the remaining two locations. Wind resource is estimated using a wind turbine Power Curve modeled using a five parametric logistic expression, whose parameters were solved using Differential Evolution (DE).
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a comprehensive review on wind turbine Power Curve modeling techniques
Renewable & Sustainable Energy Reviews, 2014Co-Authors: M. Lydia, Immanuel A. Selvakumar, S. Suresh Kumar, Edwin Prem G. KumarAbstract:The wind turbine Power Curve shows the relationship between the wind turbine Power and hub height wind speed. It essentially captures the wind turbine performance. Hence it plays an important role in condition monitoring and control of wind turbines. Power Curves made available by the manufacturers help in estimating the wind energy potential in a candidate site. Accurate models of Power Curve serve as an important tool in wind Power forecasting and aid in wind farm expansion. This paper presents an exhaustive overview on the need for modeling of wind turbine Power Curves and the different methodologies employed for the same. It also reviews in detail the parametric and non-parametric modeling techniques and critically evaluates them. The areas of further research have also been presented.
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Advanced Algorithms for Wind Turbine Power Curve Modeling
IEEE Transactions on Sustainable Energy, 2013Co-Authors: M. Lydia, Immanuel A. Selvakumar, S. Suresh Kumar, Edwin Prem G. KumarAbstract:A wind turbine Power Curve essentially captures the performance of the wind turbine. The Power Curve depicts the relationship between the wind speed and output Power of the turbine. Modeling of wind turbine Power Curve aids in performance monitoring of the turbine and also in forecasting of Power. This paper presents the development of parametric and nonparametric models of wind turbine Power Curves. Parametric models of the wind turbine Power Curve have been developed using four and five parameter logistic expressions. The parameters of these expressions have been solved using advanced algorithms like genetic algorithm (GA), evolutionary programming (EP), particle swarm optimization (PSO), and differential evolution (DE). Nonparametric models have been evolved using algorithms like neural networks, fuzzy c-means clustering, and data mining. The modeling of wind turbine Power Curve is done using five sets of data; one is a statistically generated set and the others are real-time data sets. The results obtained have been compared using suitable performance metrics and the best method for modeling of the Power Curve has been obtained.
Dipti Srinivasan - One of the best experts on this subject based on the ideXlab platform.
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Wind Power Curve Modeling and Wind Power Forecasting With Inconsistent Data
IEEE Transactions on Sustainable Energy, 2019Co-Authors: Yun Wang, Dipti Srinivasan, Qinghua Hu, Zheng WangAbstract:Wind Power Curve modeling is a challenging task due to the existence of inconsistent data, in which the recorded wind Power is far away from the theoretical wind Power at a given wind speed. In this case, confronted with these samples, the estimated errors of wind Power will become large. Thus, the estimated errors will present two properties: heteroscedasticity and error distribution with a long tail. In this paper, according to the above-mentioned error characteristics, the heteroscedastic spline regression model (HSRM) and robust spline regression model (RSRM) are proposed to obtain more accurate Power Curves even in the presence of the inconsistent samples. The results of Power Curve modeling on the real-world data show the effectiveness of HSRM and RSRM in different seasons. As HSRM and RSRM are optimized by variational Bayesian, except the deterministic Power Curves, probabilistic Power Curves, which can be used to detect the inconsistent samples, can also be obtained. Additionally, with the data processed by replacing the wind Power in the detected inconsistent samples with the wind Power on the estimated Power Curve, the forecasting results show that more accurate wind Power forecasts can be obtained using the above-mentioned data processing method.
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Approaches to wind Power Curve modeling: A review and discussion
Renewable and Sustainable Energy Reviews, 2019Co-Authors: Yun Wang, Aoife Foley, Dipti SrinivasanAbstract:Abstract Wind Power Curves play important roles in wind Power forecasting, wind turbine condition monitoring, estimation of wind energy potential and wind turbine selection. In practice, it is a challenging task to produce reliable wind Power Curves from raw wind data due to the presence of outliers formed in unexpected conditions, e.g., wind curtailment and blade damage. This paper comprehensively reviews wind Power Curve modeling techniques from the perspective of modeling processes, i.e., wind data analyses, wind data preprocessing and various wind Power Curve models. Moreover, the performances of many popular Power Curve models are studied in different seasons and different wind farms. The results show that no universal wind Power Curve model can always perform better than other models under any environmental conditions. In general, there are three factors that affect the final wind Power Curves: data filtering approaches; wind Power Curve models; and choice of optimization strategies (especially the method applied to construct objective functions). However, there is no guarantee that all outliers will be removed from the raw wind data. Consequently, designing robust regression models or constructing robust objective functions may be two effective ways to obtain accurate Power Curves in the presence of outliers. The above two strategies depend largely on the error characteristics of Power Curve modeling. While it is often observed that the error distribution of the Power Curve modeling may be asymmetric, few researchers have considered this trait when building wind Power Curves. Therefore, this paper proposes several strategies that focus on designing asymmetric loss functions and developing robust regression models with asymmetric error distributions. Models that benefit from these characteristics may be more suitable for Power Curve modeling tasks and are more likely to produce better wind Power Curves.
Francis Pelletier - One of the best experts on this subject based on the ideXlab platform.
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Comparison of Power Curve monitoring methods
E3S Web of Conferences, 2017Co-Authors: Philippe Cambron, Antoine Tahan, Christian Masson, David Torres, Francis PelletierAbstract:Performance monitoring is an important aspect of operating wind farms. This can be done through the Power Curve monitoring (PCM) of wind turbines (WT). In the past years, important work has been conducted on PCM. Various methodologies have been proposed, each one with interesting results. However, it is difficult to compare these methods because they have been developed using their respective data sets. The objective of this actual work is to compare some of the proposed PCM methods using common data sets. The metric used to compare the PCM methods is the time needed to detect a change in the Power Curve. Two Power Curve models will be covered to establish the effect the model type has on the monitoring outcomes. Each model was tested with two control charts. Other methodologies and metrics proposed in the literature for Power Curve monitoring such as areas under the Power Curve and the use of statistical copulas have also been covered. Results demonstrate that model-based PCM methods are more reliable at the detecting a performance change than other methodologies and that the effectiveness of the control chart depends on the types of shift observed.
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Power Curve monitoring using weighted moving average control charts
Renewable Energy, 2016Co-Authors: Philippe Cambron, Antoine Tahan, Christian Masson, R Lepvrier, Francis PelletierAbstract:Abstract A method for the monitoring of a wind turbine generator is proposed, based on its Power Curve and using control charts. Exponentially Weighted Moving Average (EWMA) and Generally Weighted Moving Average (GWMA) control charts are used to detect underperformances such as blade surface erosion. These variations in production amount to a few percent per year. The reference Power Curve is modeled using the bin method. A validation bench using simulated shifts on data from an MW-class wind turbine generator is used to assess the performance of the proposed method. Results show great potential, with both the EWMA and GWMA control charts able to detect a 1% per year underperformance inside 300 days of operation, based on simulated data. A short example is also given of an application using data involving a real case of underperformance: this example illustrates both the applicability and potential of this method. In this case, a shift of 3.4% in annual energy production over a period of five years could have been detected in time to plan proper maintenance. The rate of false alarms observed is one for every 667 points, which demonstrate the method's robustness.
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Wind turbine Power Curve modelling using artificial neural network
Renewable Energy, 2016Co-Authors: Francis Pelletier, Christian Masson, Antoine TahanAbstract:Technical improvements over the past decade have increased the size and Power output capacity of wind Power plants. Small increases in Power performance are now financially attractive to owners. For this reason, the need for more accurate evaluations of wind turbine Power Curves is increasing. New investigations are underway with the main objective of improving the precision of Power Curve modeling. Due to the non-linear relationship between the Power output of a turbine and its primary and derived parameters, Artificial Neural Network (ANN) has proven to be well suited for Power Curve modelling. It has been shown that a multi-stage modelling techniques using multilayer perceptron with two layers of neurons was able to reduce the level of both the absolute and random error in comparison with IEC methods and other newly developed modelling techniques. This newly developed ANN modeling technique also demonstrated its ability to simultaneously handle more than two parameters. Wind turbine Power Curves with six parameters have been modelled successfully. The choice of the six parameters is crucial and has been selected amongst more than fifty parameters tested in term of variability in differences between observed and predicted Power output. Further input parameters could be added as needed.
