Yazar "Rawa, Muhyaddin" seçeneğine göre listele

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    An adaptive deep learning framework to classify unknown composite power quality event using known single power quality events
    (Pergamon-Elsevier Science Ltd, 2021) Sindi, Hatem; Nour, Majid; Rawa, Muhyaddin; Öztürk, Şaban; Polat, Kemal
    Distributed generation (DG) sources are preferred to meet today's energy needs effectively. The addition of many different types of renewable energy sources to the grid causes various problems in signal quality. Detection and classification of these problems increase efficiency by both the producer and the consumer. In the literature, incredibly singular and some composite power quality disturbance (PQD) detection is performed effectively. However, the multitude of composite PQD variations degrades the performance of existing algorithms. In this study, the classification of all PQD variations that may occur is performed by using singular PQD and some composite PQD signals. A different number of subcomponents representing the signal are created according to each signal characteristic. Instantaneous energies from these subcomponents are used as deep learning (DL) input. Deep learning cycles are created as much as the instantaneous energy number of each signal. Each cycle has specific features of defining a single event. Therefore, the proposed approach is able to classify composite PQD signals that it has not encountered before. The proposed method's performance is first evaluated with the known PQD events and compared with the current state-of-the-art methods in the literature. Then, a dataset containing the combinations of different events not encountered during the training is created, and the performance is evaluated on this dataset. In the experiments performed, it is revealed that the proposed framework produces higher performance than other state-of-the-art methods
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    A novel hybrid deep learning approach including combination of 1D power signals and 2D signal images for power quality disturbance classification
    (PERGAMON-ELSEVIER SCIENCE LTD, 2021) Sindi, Hatem; Nour, Majid; Rawa, Muhyaddin; Öztürk, Şaban; Polat, Kemal
    As a result of the widespread use of power electronic equipment and the increase in consumption, the importance of effective energy policies and the smart grid begins to increase. Nonlinear loads and other loads in electric power systems are considered as the main reason for power quality disturbance. Distortions in signal quality and shape due to power quality disturbance cause a decrease in total efficiency. The proposed hybrid convolutional neural network method consists of a 1D convolutional neural network structure and a 2D convolutional neural network structure. The features acquired by these two convolutional neural network architectures are classified using the fully connected layer, which is traditionally used as the classifier of convolutional neural network architectures. Power signals are processed using a 1D convolutional neural network in their original form. Then these signals are converted into images and processed using a 2D convolutional neural network. Then, feature vectors generated by 1D and 2D convolutional neural networks are combined. Finally, this combined vector is classified by a fully connected layer. The proposed method is well suited to the nature of signal processing. It is a novel approach that covers the steps of an expert examining a signal. The proposed framework is compared with other state-of-the-art power quality disturbance classification methods in the literature. While the proposed method's classification performance is relatively high compared to other methods, the computational complexity is almost the same.
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    Random fully connected layered 1D CNN for solving the Z-bus loss allocation problem
    (Elsevier Sci Ltd, 2021) Sindi, Hatem; Nour, Majid; Rawa, Muhyaddin; Öztürk, Şaban; Polat, Kemal
    Power loss allocation methods should be efficient enough to meet the needs of the customers on the bus and effectively calculate the losses from generators and consumers. In order to perform these tasks, a highly robust model is essential to distinguish between the effects of multi-consumers. This study presents a novel convolutional neural network (CNN) architecture that is highly effective for z-bus loss allocation. The proposed CNN architecture that uses the Z-bus matrix as input is 1D. Unlike traditional 1D CNN architectures in the literature, the fully connected layer (FCL) of the proposed method is randomized. Unlike Traditional FCL layers, randomized FCL's input weights and biases are not needed to be tuned. This makes the proposed 'Randomized Fully Connected Layered 1D CNN' architecture relatively fast and straightforward. Proposed Randomized Fully Connected Layered 1D CNN is trained in an end-to-end manner with a regression task for robust loss allocation. The performance of it is higher than other state-of-the-art methods. In addition to the fact that the proposed method's regression performance is very promising, the classifier performance is quite satisfactory thanks to the changes to be made in its output.