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<p>A study on power system disturbance (PSD) classification of a distributed generator (DG) based grid-connected network has been carried out in this article. A total of 24 different PSDs commonly occurring in the DG-based grid-connected network have been considered. The three signal processing tools, Discrete wavelet transform (DWT), Detrended Fluctuation Analysis (DFA) and Recurrence Quantification Analysis (RQA) have been applied to construct a feature matrix. A category-wise visual representation on a 2D plane has been provided using the Uniform Manifold Approximation and Projection (UMAP) technique, and the coefficients of the feature matrix have been processed using this UMAP technique. The non-linear model, obtained from UMAP, helps to reduce the dimension of the classification algorithm and provides a categorical projection in a 2D plane. The category-wise visual 2D representation clearly proves the effectiveness of the model in identifying and classifying each class of PSDs. This huge variety of disturbance classification by visual 2D representation using UMAP is a novel approach in PSD study.</p> Graphical abstract <p></p>

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Sensing of power system disturbances using DWT-DFA-RQA features employing UMAP analysis

  • 期刊论文

  • 发表日期：

    2026年6月15日

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  • Chandan Jana,
  • Sannistha Banerjee,
  • Subhajit Maur,
  • Mousumi Jana Bala,
  • Sovan Dalai

摘要

A study on power system disturbance (PSD) classification of a distributed generator (DG) based grid-connected network has been carried out in this article. A total of 24 different PSDs commonly occurring in the DG-based grid-connected network have been considered. The three signal processing tools, Discrete wavelet transform (DWT), Detrended Fluctuation Analysis (DFA) and Recurrence Quantification Analysis (RQA) have been applied to construct a feature matrix. A category-wise visual representation on a 2D plane has been provided using the Uniform Manifold Approximation and Projection (UMAP) technique, and the coefficients of the feature matrix have been processed using this UMAP technique. The non-linear model, obtained from UMAP, helps to reduce the dimension of the classification algorithm and provides a categorical projection in a 2D plane. The category-wise visual 2D representation clearly proves the effectiveness of the model in identifying and classifying each class of PSDs. This huge variety of disturbance classification by visual 2D representation using UMAP is a novel approach in PSD study.

Graphical abstract