Yazar "Açıkgöz, Hakan" seçeneğine göre listele

Listeleniyor 1 - 4 / 4
  • Küçük Resim Yok
    Öğe
    Altitude and Attitude Control of a Quadcopter Based on Neuro-Fuzzy Controller
    (Springer Science and Business Media Deutschland GmbH, 2022) Korkmaz, Deniz; Açıkgöz, Hakan; Üstündağ, Mehmet
    In this paper, a 6-degrees of freedom (DoF) nonlinear dynamic model of the quadcopter is derived and a robust altitude and attitude control is proposed. The motion control is performed with four neuro-fuzzy controllers that ensure rapid and robust performances for nonlinear and uncertain systems. The aim of the designed control scheme is to provide to track the desired yaw, pitch, roll, and altitude trajectories simultaneously. The simulations are realized in the MATLAB/Simulink environment. The obtained results show that the designed control scheme is robust and efficient in both altitude and attitude responses with different uncertain trajectories.
  • Küçük Resim
    Öğe
    An efficient fault classification method in solar photovoltaic modules using transfer learning and multi-scale convolutional neural network
    (Elsevier, 2022) Korkmaz, Deniz; Açıkgöz, Hakan
    Photovoltaic (PV) power generation is one of the remarkable energy types to provide clean and sustainable energy. Therefore, rapid fault detection and classification of PV modules can help to increase the reliability of the PV systems and reduce operating costs. In this study, an efficient PV fault detection method is proposed to classify different types of PV module anomalies using thermographic images. The proposed method is designed as a multi-scale convolutional neural network (CNN) with three branches based on the transfer learning strategy. The convolutional branches include multi-scale kernels with levels of visual perception and utilize pre-trained knowledge of the transferred network to improve the representation capability of the network. To overcome the imbalanced class distribution of the raw dataset, the oversampling technique is performed with the offline augmentation method, and the network performance is increased. In the experiments, 11 types of PV module faults such as cracking, diode, hot spot, offline module, and other classes are utilized. The average accuracy is obtained as 97.32% for fault detection and 93.51% for 11 anomaly types. The experimental results indicate that the proposed method gives higher classification accuracy and robustness in PV panel faults and outperforms the other deep learning methods and existing studies
  • Küçük Resim
    Öğe
    An improved residual-based convolutional neural network for very short-term wind power forecasting
    (ELSEVIER, 2021) Yıldız, Ceyhun; Açıkgöz, Hakan; Korkmaz, Deniz; Budak, Ümit
    An accurate forecast of wind power is very important in terms of economic dispatch and the operation of power systems. However, effectively mitigating the risks arising from wind power in power system operations greatly reduces the risk of wind energy producers, exposing them to potential additional costs. Being aware of this challenge, we introduced a two-step novel deep learning method for wind power forecasting. The first stage includes processes of Variational Mode Decomposition (VMD)-based feature extraction and converting these features into images. In the second stage, an improved residual-based deep Convolutional Neural Network (CNN) was utilized to forecast wind power. Meteorological wind speed, wind direction, and wind power data, which are directly related to each other, were employed as a dataset. The combined dataset was procured from a wind farm in Turkey between January 1 and December 31, 2018. The results of the proposed method were compared with the results obtained from the state-of-the-art deep learning architectures namely SqueezeNet, GoogLeNet, ResNet-18, AlexNet, and VGG-16 as well as physical model based on available meteorological forecast data. The proposed method outperformed the other architectures and demonstrated promising results for very short-term wind power forecasting due to its competitive performance.
  • Küçük Resim Yok
    Öğe
    A Novel Short-Term Photovoltaic Power Forecasting Approach based on Deep Convolutional Neural Network
    (2021) Korkmaz, Deniz; Açıkgöz, Hakan; Yıldız, Ceyhun
    In this study, a novel photovoltaic power forecasting system that utilizes a deep Convolutional Neural Network (CNN) structure and an input signal decomposition algorithm is proposed. The proposed CNN architecture extracts deep features to forecast short-term power using transfer learning-based AlexNet. The historical power, solar radiation, wind speed, and temperature data are selected as the input. The signal decomposition algorithm called Empirical Mode Decomposition (EMD) is utilized to decompose the historical power signal into sub-components. In order to extract deep features, all input parameters are converted to 2D feature maps and feed to the input of the CNN. The experiments are realized on a grid-tied Photovoltaic Power Plant (PVPP) that has 1000 kW installed capacity located in Turkey. The experiments are performed under four weather conditions as partial cloudy, cloudy-rainy, heavy-rainy, and sunny days to show the effectiveness of the proposed method. The obtained results are compared with the benchmark regression algorithms. When the results are analyzed, the proposed method gives the highest Correlation Coefficient (R) and the lowest Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and SMAPE values under all horizons and weather conditions. For 1-h to 5-h ahead, the average R values of the proposed method are obtained as 97.28%, 95.77%, 94.49%, 93.61%, and 92.62%, respectively. The average RMSE values are observed as 4.90%, 6.30%, 7.50%, 8.00%, and 9.17% for 1-h to 5-h ahead. The experimental results confirm that the proposed method outperforms the conventional regression algorithms and reveals effective results with its competitive performance.