Recognition of Multiple Power Quality Disturbances Based on Discrete Wavelet Transform and Improved Long Short-Term Memory Networks
DOI:
https://doi.org/10.69650/rast.2024.255814Keywords:
Recognition, Multiple Power Quality Disturbances, Discrete Wavelet Transform, Improved Long Short-Term Memory NetworksAbstract
In the past few years, distributed electricity generation from renewable sources, or microgrid systems, has been connected to the grid to increase power supply stability. This responds to government policy regarding commitment to using 100% renewable energy in operations (RE100) project efforts. This results in the entry of power electronic or non-linear equipment into the electrical system, making it more sensitive. Moreover, multiple power quality disturbances (PQDs) consist of a variety of single disturbances. Analysis of complex multi-label patterns is a challenging task. In this paper, we propose a methodology to address this challenge by leveraging Discrete Wavelet Transform (DWT) and Improved Long Short-Term Memory Networks (LSTM). Firstly, multiple PQDs are synthesized utilizing a mathematical model based on IEEE standards 1159-2019. Secondly, the obtained PQDs are decomposed into nine feature classes, yielding detailed (cDs) and approximation (cAs) coefficients through Five-Level DWT Decomposition. Furthermore, we conducted a comparative analysis of each component across five different wavelet functions: haar1, db4, bior1.3, coif2, and sym4. Thirdly, the cDs and cAs coefficients derived from each wavelet type undergo statistical analysis before being inputted into the LSTM model for classification of each feature class. Our results highlight that cD5 components obtained from the db4 wavelet exhibit the highest accuracy rate of 93.86%. This finding elucidates the significance of selecting appropriate wavelet types and compositions for the successful classification of multiple PQDs.
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