Optimized modal decomposition techniques for robust leakage detection in noisy environments: A comparative study

Measurement, Год журнала: 2025, Номер unknown, С. 117390 - 117390

Опубликована: Март 1, 2025

Classification of power quality disturbances in microgrids using a multi-level global convolutional neural network and SDTransformer approach

PLoS ONE, Год журнала: 2025, Номер 20(2), С. e0317050 - e0317050

Опубликована: Фев. 12, 2025

As the adoption of new energy sources like photovoltaic and wind power increases alongside influx advanced electronic devices, there has been a significant rise in quality disturbance events (PQDs) within systems. These disturbances, including harmonics voltage dips, severely impact stability microgrids efficiency equipment. To enhance accuracy identifying disturbances microgrids, this paper introduces Multi-level Global Convolutional Neural Network combined with Simplified double-layer Transformer model (MGCNN-SDTransformer). The processes input raw 1D time-series signals through multi-level convolutional 1D-Global Attention Mechanism (1D-GAM) operations MGCNN, which preliminarily extracts emphasizes key features dynamic changes; Subsequently, utilizes Multi-head Self Attention(MSA) Multi-Layer Perceptron(MLP) components enhanced SDTransformer to further explore transient local periodic global signals; classification outcomes are then determined using fully-connected layer Softmax classifier. effectively retains signal’s original one-dimensional temporal attributes while also delving into more complex features. This approach exhibits strong resistance noise generalization skills, markedly improving detection issues microgrids.

Язык: Английский

Recognition of Power Quality Disturbances Using Afd Based Features and Graph Convolutional Network

Опубликована: Янв. 1, 2025

Язык: Английский