Power quality issues are required to be addressed properly in forthcoming era of smart meters, smart grids and increase in renewable energy integration. In this paper, Deep Auto-encoder (DAE) networks is proposed for power quality disturbance (PQD) classification and its location detection without using complex signal processing techniques and complex classifiers. In this technique, Gabor filter is used to extract a set of general Gabor features from the convolution of PQD image. Subsequently, through sparse based DAE network, essential and optimal features are extracted and learnt which are used by a simple classifier (SoftMax) to classify the PQD type. Furthermore, the temporal information of the PQD is obtained using the PQD image to correctly locate the disturbance's initiating and terminating instants. The proposed DAE network has the benefits of Deep Learning-based networks in terms of automatic feature selection, but it requires smaller data sets. The issue of obtaining optimised, robust, and strong features from the PQD signal is thus resolved. Excellent classification accuracy of PQD is obtained with appropriate network parameter setting of the proposed DAE network. The proposed technique is compared with three other methods i.e. support vector machine (SVM), stacked auto-encoder (SAE) and principal component analysis (PCA) for PQD classification by implementing all the four techniques on python platform using the same data set. It has an overall classification accuracy of more than 97% at a signal to noise ratio (SNR) of 20dB, which is on the higher side when compared to other methods of PQD detection under noisy environment. Additionally, this method requires less computation time with the same data set than alternative approaches like SVM. Thus, the proposed method outperforms existing methods for PQD classification and detection of single disturbance and multi-disturbance in terms of greater accuracy and reduced computation complexity and computation time.

Power Quality Disturbances Detection and Classification Based on Deep Convolution Auto-Encoder Networks

Siano P.;
2023-01-01

Abstract

Power quality issues are required to be addressed properly in forthcoming era of smart meters, smart grids and increase in renewable energy integration. In this paper, Deep Auto-encoder (DAE) networks is proposed for power quality disturbance (PQD) classification and its location detection without using complex signal processing techniques and complex classifiers. In this technique, Gabor filter is used to extract a set of general Gabor features from the convolution of PQD image. Subsequently, through sparse based DAE network, essential and optimal features are extracted and learnt which are used by a simple classifier (SoftMax) to classify the PQD type. Furthermore, the temporal information of the PQD is obtained using the PQD image to correctly locate the disturbance's initiating and terminating instants. The proposed DAE network has the benefits of Deep Learning-based networks in terms of automatic feature selection, but it requires smaller data sets. The issue of obtaining optimised, robust, and strong features from the PQD signal is thus resolved. Excellent classification accuracy of PQD is obtained with appropriate network parameter setting of the proposed DAE network. The proposed technique is compared with three other methods i.e. support vector machine (SVM), stacked auto-encoder (SAE) and principal component analysis (PCA) for PQD classification by implementing all the four techniques on python platform using the same data set. It has an overall classification accuracy of more than 97% at a signal to noise ratio (SNR) of 20dB, which is on the higher side when compared to other methods of PQD detection under noisy environment. Additionally, this method requires less computation time with the same data set than alternative approaches like SVM. Thus, the proposed method outperforms existing methods for PQD classification and detection of single disturbance and multi-disturbance in terms of greater accuracy and reduced computation complexity and computation time.
2023
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4853079
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