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Comparing SVR and MLP neural network for modelling and fault detection of KAHAK wind farm
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Abstract: (781 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. |
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| Keywords: Neural Network, Fault Detection, Modelling, Wind Turbine, Data-based, KAHAK |
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Full-Text [PDF 898 kb]
(334 Downloads)
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Type of Study: Scientific-extension |
Subject:
Special
Received: 2022/07/29 | Accepted: 2022/03/19 | Published: 2022/03/19
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