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A Deep Learning Approach for the Detection and Classification of Power Quality Disturbances with Windowed Signals

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Abstract

The modernization of Power Systems (PSs) to smart grids, the expansion of microgrids, the ever-increasing presence of distributed power generation, the more frequent use of non-linear and voltage-sensitive loads by the consumers have caused problems to the Power Quality (PQ). The studies in PQ are commonly related to disturbances that alter the sinusoidal characteristics of the voltage waveforms and/or current. The first step to analyzing the PQ is to detect and then classify the disturbances, since by identifying the disturbance, it is possible to know its causes and deliberate over strategies to mitigate it. Thus, this paper proposes a deep-learning approach using voltage signals, without pre-processing, extraction, nor manual selection of features in order to detect and classify PQ disturbances automatically. The proposed approach is composed of convolution layers, a pooling layer, a long short-term memory layer, and batch normalization. A 1D convolution was used to adapt the data from the voltage signals. Overlapping windowed signals with different Signal–Noise Ratio (SNR) (40 dB, 30 dB, 20 dB and 0 dB) and with different sampling rates (16, 32, and 64 samples/cycle) were used. For a more in-depth view of the results, the proposed approach was evaluated for its accuracy, precision, recall, and F1-Score in different scenarios. An analysis of the obtained results shows that even for the worst case scenario (SNR of 20 dB and sampling rates of 16 samples/cycle), the approach performs satisfactorily with values above 0.97 for the analyzed metrics, allowing, thus, consumer action in a demand-side management scenario.

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Authors and Affiliations

  1. Department of Computer Science, Federal University of Piauí, Teresina, PI, Brazil

    Wilson L. Rodrigues Jr & Ricardo de A. L. Rabelo

  2. Department of Computer Science, State University of Piauí, Piripiri, PI, Brazil

    Fabbio A. S. Borges

  3. Department of Computer Science, Federal University of Piauí, Picos, PI, Brazil

    Antonio O. de Carvalho Filho

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  1. Wilson L. Rodrigues Jr
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  2. Fabbio A. S. Borges
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  3. Antonio O. de Carvalho Filho
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  4. Ricardo de A. L. Rabelo
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Correspondence to Ricardo de A. L. Rabelo.

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Rodrigues, W.L., Borges, F.A.S., de Carvalho Filho, A.O. et al. A Deep Learning Approach for the Detection and Classification of Power Quality Disturbances with Windowed Signals. SN COMPUT. SCI. 2, 64 (2021). https://doi.org/10.1007/s42979-020-00435-1

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