A new design methodology to predict wind farm energy production by means of a spiking neural network-based system

被引：19

作者：

Brusca, Sebastian ^{[1
]}

Capizzi, Giacomo ^{[2
]}

Lo Sciuto, Grazia ^{[2
]}

Susi, Gianluca ^{[3
]}

机构：

[1] Univ Messina, Dept Engn, Messina, Italy

[2] Univ Catania, Dept Elect Elect & Informat Engn, Viale A Doria 6, Catania, Italy

[3] Tech Univ Madrid, Ctr Biomed Technol, Lab Cognit & Computat Neurosci UCM UPM, Madrid, Spain

来源：

INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS | 2019年 / 32卷 / 04期

关键词：

NeuCube; spiking neural network; wind; wind power forecasting; wind power plant; TURBINE WAKES; DEPENDENCE; DYNAMICS; NEUCUBE; NEURONS; BRAIN; MODEL;

D O I：

10.1002/jnm.2267

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, a spiking neural network-based architecture for the prediction of wind farm energy production is proposed. The model is also able to evaluate the wake effects due to interactions between the elements of a wind farm on the energy production of the whole farm. This method has been applied to a large wind power plant, composed of 28 turbines and 3 anemometric towers, located in the rural area of Vizzini's municipality in province of Catania, Italy, that is characterised by a complex orography and an extension of 30 km(2). For the implementation of this architecture it was used the "NeuCube" simulator. The results show that the presented method can be successfully applied for predictions of wind energy generation in real wind farm also in presence of faults.

引用

页数：14

共 36 条

[1] A new method to estimate Weibull parameters for wind energy applications [J].

Akdag, Seyit A. ;

Dinler, Ali .

ENERGY CONVERSION AND MANAGEMENT, 2009, 50 (07) :1761-1766

[2] Modelling and Measuring Flow and Wind Turbine Wakes in Large Wind Farms Offshore [J].

Barthelmie, R. J. ;

Hansen, K. ;

Frandsen, S. T. ;

Rathmann, O. ;

Schepers, J. G. ;

Schlez, W. ;

Phillips, J. ;

Rados, K. ;

Zervos, A. ;

Politis, E. S. ;

Chaviaropoulos, P. K. .

WIND ENERGY, 2009, 12 (05) :431-444

[3] Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type [J].

Bi, GQ ;

Poo, MM .

JOURNAL OF NEUROSCIENCE, 1998, 18 (24) :10464-10472

[4]

Bishop C. M., 2006, PATTERN RECOGNITION, DOI DOI 10.1117/1.2819119

[5]

Bonanno F., 2016, 2016 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), P872, DOI 10.1109/SPEEDAM.2016.7526001

[6]

Bonanno F, 2015, 2015 INTERNATIONAL CONFERENCE ON CLEAN ELECTRICAL POWER (ICCEP), P602, DOI 10.1109/ICCEP.2015.7177554

[7] Wind Turbine Placement Optimization by means of the Monte Carlo Simulation Method [J].

Brusca, S. ;

Lanzafame, R. ;

Messina, M. .

MODELLING AND SIMULATION IN ENGINEERING, 2014, 2014

[8] Analysis of connectivity in NeuCube spiking neural network models trained on EEG data for the understanding of functional changes in the brain: A case study on opiate dependence treatment [J].

Capecci, Elisa ;

Kasabov, Nikola ;

Wang, Grace Y. .

NEURAL NETWORKS, 2015, 68 :62-77

[9] Spike-driven synaptic plasticity: Theory, simulation, VLSI implementation [J].

Fusi, S ;

Annunziato, M ;

Badoni, D ;

Salamon, A ;

Amit, DJ .

NEURAL COMPUTATION, 2000, 12 (10) :2227-2258

[10]

Gaber MM, 2007, DATA STREAMS, P39, DOI [DOI 10.1007/978, DOI 10.1007/978-0-387-47534-9_]

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