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

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.

Initial condition based real time classification of power quality disturbance using deep convolution neural network with bidirectional long short‐term memory

“…Wilson L. Rodrigues Jr et al. [61] presented a deep‐learning strategy using multiple voltage signals, without feature extraction, pre‐processing of signal, non‐manual feature detection to identify and categorize PQ disturbances without human intervention. The developed strategy consists of three stage of convolution layers, a long short‐term memory layer, a pooling layer with batch normalization.…”

Identification of Oscillatory Modes in Power System Using Deep Learning Approach

“…In recent years deep learning techniques have become one of the most efficient tools in many research areas. It is successfully implemented in power systems, particularly short-term load forecasting problems and disturbance classification [28]. Other works include the deep learning approach of cost loss function for transient stability assessment proposed by Zhou et al [29] and Cheng et al presented prediction of load demand in the smart grid using the LSTM network [30].…”