Pattern Analysis for Classification of Power Quality Disturbances

Aziz, Sumair; Khan, Muhammad Umar; Abdullah, .; Usman, Adil; Mobeen, Areeba

doi:10.1109/icetst49965.2020.9080722

Cited by 6 publications

(3 citation statements)

References 25 publications

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“…In this sense, Table 2 summarizes the results obtained by the previous literature and also shows the conditions evaluated, as well as the used classification technique. Results reported in [19,20] present general values with high accuracy (99.7%); however, a classification percentage around 79% has been reported when the sag disturbance appears. This situation may be explained by the fact that the behavior of a signal with a sag is very similar to one that presents a healthy signal.…”

Section: Comparative Analysismentioning

confidence: 85%

“…This situation may be explained by the fact that the behavior of a signal with a sag is very similar to one that presents a healthy signal. Another important aspect is that the signals analyzed in [19,20] are free of noise, which is an important issue that significantly affects the performance of classification when real signals are under evaluation. For the remaining works, such as in [22][23][24], the overall percentages show variations within the range of 80% to 96.67%, which are lower than the accuracy reached by the proposed approach.…”

Section: Comparative Analysismentioning

confidence: 99%

“…On the machine learning side, the work developed in [19] proposes an approach based on dimensionality reduction through a linear discriminant analysis (LDA); moreover, statistical features are used to characterize the behavior of different PQD, and finally, using a Neural Network as a classifier, it is possible to perform an automatic classification of the disturbance in the signal. Another example of this approach is presented in the work of [20]. Here, four main steps are defined: generation of PQD classes, feature extraction, selection of highly discriminating features, and then the classification of PQDs using selected features.…”

Section: Introductionmentioning

confidence: 99%

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Methodology for the Detection and Classification of Power Quality Disturbances Using CWT and CNN

Perez-Anaya,

Jaen-Cuellar,

Elvira-Ortiz

et al. 2024

Energies

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Energy generation through renewable processes has represented a suitable option for power supply; nevertheless, wind generators and photovoltaic systems can suddenly operate under undesired conditions, leading to power quality problems. In this regard, the development of condition-monitoring strategies applied to the detection of power quality disturbances becomes mandatory to ensure proper working conditions of electrical machinery. Therefore, in this work we propose a diagnosis methodology for detecting power quality disturbances by means of the continuous wavelet transform (CWT) and convolutional neural network (CNN). The novelty of this work lies in the image processing that allows us to precisely highlight the discriminant patterns through spectrograms into 2D images; the images are cropped and reduced to a standard size of 128x128 pixels to retain the most relevant information. Subsequently, the identification of six power quality disturbances is automatically performed by a convolutional neural network. The effectiveness of the proposed method is validated under a set of synthetic data as well as a real data set; the obtained results make the proposal suitable for being incorporated into the monitoring of power quality disturbances in renewable energy systems.

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Section: Comparative Analysismentioning

confidence: 85%