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:: Volume 8, Issue 16 (3-2022) ::
عصر برق 2022, 8(16): 58-63 Back to browse issues page
Comparing SVR and MLP neural network for modelling and fault detection of KAHAK wind farm
Abstract:   (315 Views)
In this paper we use SVR and MLP Neural Network for modelling and fault detection of KAHAK wind turbines. Electrical subsystems is our purpose for modelling and fault detection. We have actual data and real parameters about 2.5 MW wind turbines that installed in KAHAK wind farme. In first step we modelled and fault datect with two approach, Support Vector Regresion (SVR) and Multi Layer Perceptron (MLP), in second step we compare model error and accurase of SVR and MLP approach, that we can see SVR approach is better than MLP approach according modelling error. We use MATLAB software for this work.
Keywords: Neural Network, Fault Detection, Modelling, Wind Turbine, Data-based, KAHAK
Full-Text [PDF 898 kb]   (128 Downloads)    
Type of Study: Scientific-extension | Subject: Special
Received: 2022/07/29 | Accepted: 2022/03/19 | Published: 2022/03/19
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Comparing SVR and MLP neural network for modelling and fault detection of KAHAK wind farm. عصر برق 2022; 8 (16) :58-63
URL: http://kiaeee.ir/article-1-373-en.html

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Volume 8, Issue 16 (3-2022) Back to browse issues page
نشریه عصر برق - انجمن مهندسین برق و الکترونیک ایران - شاخه خراسان Khorasan Iranian Association of Electrical and Electronics Engineers (kiaeee)
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