A Conversion of Signal to Image Method for Two-Dimension Convolutional Neural Networks Implementation in Power Quality Disturbances Identification

Sunneng Sandino Berutu - Immanuel Christian University, Solo Rd, Yogyakarta, 55571, Indonesia
Yeong-Chin Chen - Asia University, 500, Lioufeng Rd, Wufeng, Taichung, 41354, Taiwan
Heri Wijayanto - University of Mataram, Mataram, Nusa Tenggara Barat, 83125, Indonesia
Haeni Budiati - Immanuel Christian University, Solo Rd, Yogyakarta, 55571, Indonesia

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DOI: http://dx.doi.org/10.30630/joiv.6.4.1529


The power quality is identified and monitored to prevent the worst effects arise on the electrical devices. These effects can be device failure, performance degradation, and replacement of some device parts. The deep convolutional neural networks (DCNNs) method can extract the complexity of image features. This method is adopted for the power quality disruption identification of the model. However, the power quality signal data is a time series. Therefore, this paper proposes an approach for the conversion of a power quality disturbance signal to an image. This research is conducted in several stages for constructing the approach proposed. Firstly, the size of a matrix is determined based on the sampling frequency values and cycle number of the signal. Secondly, a zero-cross algorithm is adopted to specify the number of signal sample points inserted into rows of the matrix. The matrix is then converted into a grayscale image. Furthermore, the resulting images are fed to the two-dimension (2D) CNNs model for the PQDs feature learning process. When the classification model is fit, then the model is tested for power quality data prediction. Finally, the model performance is evaluated by employing the confusion matrix method. The model testing result exhibits that the parameter values such as accuracy, recall, precision, and f1-score achieve at 99.81%, 98.95%, 98.84, and 98.87 %, respectively. In addition, the proposed method's performance is superior to the previous methods. 


Power quality disturbances; conversion; identification; convolutional neural network.

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