Classification of multiple power quality events via compressed deep learning

Abstract

Summary This paper presents a recently established compressed sensing (CS) and sparse autoencoder (SAE) based on deep learning (DL) method for classification of single and multiple power quality disturbances (PQDs). The CS technique is paying considerable attention in recent years due to below sampling rate comparatively Nyquist sampling. Initially, the CS technique is applied to extract the features of PQD waveforms. The extracted features are applied as inputs to the sparse autoencoder based on DL for classification of nine single and 22 combined classes of PQDs. The DL helps to remove a redundant feature and improves classification performance. Finally, backpropagation is applied to fine‐tune the entire network. The effectiveness of the proposed algorithm has been tested with more than 6580 numbers of real and synthetic single and multiple PQD data, and the results are recorded. High correct classification rate is obtained with noise and without noise level. Noise level was considered from 20 to 50 dB. The performance of the proposed technique has been assessed by comparing the results against recently reported methods. Results show that the proposed CS‐ and SAE‐based DL algorithms can be efficiently used for single and multiple PQDs classifications.

A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances

“…From a quantitative point of view, the feature learning performance is also validated, as the resulting reconstruction error estimated through MSE is 0.0143 for Figure 5a and 0.002 for Figure 5b. Such values are representative, owing to the fact that the average MSE of the whole data set is 0.0327, in all cases with a very low resulting error [24]. The qualitative inspection of such results shows that the autoencoder properly performs the characterization of the original signals since neither significant deviation nor averaged patterns are delivered.…”

“…Such experimental database contains representative signals of some of the PQ scenarios considered. The experimental database accounted in this study is supplied by the IEEE P1159.2 working group and referred to by some studies in the field: in [24], in order to verify the effectiveness of his proposed for the classification of PQD signals waveforms; in [34], eleven waveforms signals from this database have been utilized to demonstrate the successful classification with his method; and, in [35], five real signals have been used to demonstrate the ability of the proposed approach to identify the disturbances signals. All in all, these real signals have been used to validate the approaches presented in PQ studies.…”

“…The signals of such database are sampled at 15,360 Hz and contain six cycles at 60 Hz operation frequency, an amplitude in pu, and an SNR estimated at 45 dB [24]. Although most of the related studies consider less than 10 samples of such database, in this particular endeavor, which pursues to validate the generalization capabilities and performance of the proposed methodology, a total of 48 signals have been considered.…”

“…The work presented in [23] submits data-driven approaches on the grounds of such deep learning techniques. Although the outcoming performances keep being promising, the lack of a common procedure to configure and tune the algorithms still represents a shortcoming that avoids its consideration over real industrial applications [24]. The work presented in [23] looks into the capabilities of deep learning in PQD classification using a convolutional neural network alongside eleven statistical indicators calculated over the four principal components of the electrical signals by the use of a modified version of PCA.…”

Smart grid security enhancement by detection and classification of non‐technical losses employing deep learning algorithm

“…This massive data collected help us to design a secured smart gird, using advanced intelligent computing techniques. Moreover, data collected in the AMI of the smart grid help in energy management, 5 short‐term load forecast, 6 and optimal scheduling 7…”