Development of an enhanced parametric model for wind turbine power curve

Modeling of wind turbine power curve is greatly important in performance monitoring of the turbine and also in forecasting the wind power generation. In this paper, an accurate parametric model called modified hyperbolic tangent (MHTan) is proposed to characterize power curve of the wind turbine. The paper also presents the development of both parametric and nonparametric models of wind turbine power curve. In addition, least square error (LSE) and maximum likelihood estimation (MLE) are employed to estimate vector parameter of the proposed model. Here, three evolutionary algorithms, namely, particle swarm optimization, Cuckoo search, and backtracking search algorithm aid LSE and MLE. The performance of all presented methods is evaluated by a real data collected from a wind farm in Iran as well as three statistically generated data sets. The results demonstrate the efficiency of the proposed model compared to some other existing parametric and nonparametric models.

[1]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[2]  Maria Grazia De Giorgi,et al.  Error analysis of short term wind power prediction models , 2011 .

[3]  M. R. AlRashidi,et al.  LONG TERM ELECTRIC LOAD FORECASTING BASED ON PARTICLE SWARM OPTIMIZATION , 2010 .

[4]  Ioannis P. Panapakidis,et al.  Day-ahead electricity price forecasting via the application of artificial neural network based models , 2016 .

[5]  Saifur Rahman,et al.  A decision support technique for the design of hybrid solar-wind power systems , 1998 .

[6]  Ainuddin Wahid Abdul Wahab,et al.  An appraisal of wind speed distribution prediction by soft computing methodologies: A comparative study , 2014 .

[7]  P. Gottschalk,et al.  The five-parameter logistic: a characterization and comparison with the four-parameter logistic. , 2005, Analytical biochemistry.

[8]  Antonio Messineo,et al.  Monitoring of wind farms’ power curves using machine learning techniques , 2012 .

[9]  Andrew Kusiak,et al.  On-line monitoring of power curves , 2009 .

[10]  Ali Mohammad Ranjbar,et al.  Fuzzy modeling techniques and artificial neural networks to estimate annual energy output of a wind turbine , 2010 .

[11]  Dalibor Petković,et al.  Generalized adaptive neuro-fuzzy based method for wind speed distribution prediction , 2015 .

[12]  Pinar Çivicioglu,et al.  Backtracking Search Optimization Algorithm for numerical optimization problems , 2013, Appl. Math. Comput..

[13]  Shahaboddin Shamshirband,et al.  Adaptive neuro-fuzzy optimization of wind farm project net profit , 2014 .

[14]  Shahaboddin Shamshirband,et al.  Trend detection of wind speed probability distribution by adaptive neuro-fuzzy methodology , 2015 .

[15]  Andrew Kusiak,et al.  Models for monitoring wind farm power , 2009 .

[16]  V. K. Sethi,et al.  Critical analysis of methods for mathematical modelling of wind turbines , 2011 .

[17]  M. Schlechtingen,et al.  Using Data-Mining Approaches for Wind Turbine Power Curve Monitoring: A Comparative Study , 2013, IEEE Transactions on Sustainable Energy.

[18]  Haoran Zhao,et al.  Review of energy storage system for wind power integration support , 2015 .

[19]  Ryozo Ooka,et al.  Metaheuristic optimization methods for a comprehensive operating schedule of battery, thermal energy storage, and heat source in a building energy system , 2015 .

[20]  A. Kusiak,et al.  Monitoring Wind Farms With Performance Curves , 2013, IEEE Transactions on Sustainable Energy.

[21]  Mohammad Jafari Jozani,et al.  Wind Turbine Power Curve Modeling Using Advanced Parametric and Nonparametric Methods , 2014, IEEE Transactions on Sustainable Energy.

[22]  Yongqian Liu,et al.  Hybrid Forecasting Model for Very-Short Term Wind Power Forecasting Based on Grey Relational Analysis and Wind Speed Distribution Features , 2014, IEEE Transactions on Smart Grid.

[23]  Shuping Dang,et al.  A source–grid–load coordinated power planning model considering the integration of wind power generation , 2016 .

[24]  Thang Trung Nguyen,et al.  Cuckoo search algorithm for short-term hydrothermal scheduling , 2014 .

[25]  Dalibor Petković,et al.  Adaptive neuro-fuzzy approach for estimation of wind speed distribution , 2015 .

[26]  Dalibor Petković,et al.  Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation , 2013 .

[27]  Nasrudin Abd Rahim,et al.  Using data-driven approach for wind power prediction: A comparative study , 2016 .

[28]  Douglas C. Montgomery,et al.  Introduction to Statistical Quality Control , 1986 .

[29]  A. Immanuel Selvakumar,et al.  A comprehensive review on wind turbine power curve modeling techniques , 2014 .

[30]  Paul Giorsetto,et al.  Development of a New Procedure for Reliability Modeling of Wind Turbine Generators , 1983, IEEE Transactions on Power Apparatus and Systems.

[31]  Jae-Kyung Lee,et al.  Development of a Novel Power Curve Monitoring Method for Wind Turbines and Its Field Tests , 2014, IEEE Transactions on Energy Conversion.

[32]  M. Lydia,et al.  Advanced Algorithms for Wind Turbine Power Curve Modeling , 2013, IEEE Transactions on Sustainable Energy.

[33]  K. F. Fong,et al.  Energy management and design of centralized air-conditioning systems through the non-revisiting strategy for heuristic optimization methods , 2010 .

[34]  Janusz Piechocki,et al.  Use of Modified Cuckoo Search algorithm in the design process of integrated power systems for modern and energy self-sufficient farms , 2014 .

[35]  Peter Tavner,et al.  Reliability of wind turbine subassemblies , 2009 .