Interpretable DWT-1DCNN-LSTM Network for Power Quality Disturbance Classification
The proportion of new energy sources, such as wind, photovoltaic and hydropower, in the power grid is increasing year by year. In addition, a large number of nonlinear loads are connected to the grid, resulting in frequent power quality disturbances (PQDs), which pose challenges to the stability and...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/18/2/231 |
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| Summary: | The proportion of new energy sources, such as wind, photovoltaic and hydropower, in the power grid is increasing year by year. In addition, a large number of nonlinear loads are connected to the grid, resulting in frequent power quality disturbances (PQDs), which pose challenges to the stability and reliability of the power system. Accurate identification of these disturbances is crucial for effective grid management and protection. Although deep learning methods have high accuracy, their lack of interpretability can limit their acceptance in engineering applications. Traditional signal analysis has a good physical foundation, but it is not integrated with deep learning to a sufficient degree. To address these issues, we propose the DWT-1DCNN-LSTM network as an interpretable model for PQD classification. This method effectively decomposes the time-domain signals into sub-signals in different frequency bands by employing the Discrete Wavelet Transform (DWT), which enhances the anti-interference capability of the classification model. This approach enhances the interference resilience of the classification model through the incorporation of the Discrete Wavelet Transform (DWT), which effectively decomposes time-domain signals into sub-signals across different frequency bands. The one-dimensional Convolutional Neural Network (1DCNN) then extracts local features, while the Long Short-Term Memory network (LSTM) analyzes temporal dependencies of the transformed sub-signals. Experimental validation with simulated datasets demonstrates that the DWT-1DCNN-LSTM model achieves an accuracy of 99.27%, outperforming the DWT-1DCNN, 1DCNN-LSTM, LSTM, and CNN models by 1.59%, 1.13%, 1.44%, and 6.48%, respectively. The robustness provided by the DWT module makes the model well suited for PQDs in environments with large disturbances, helping to detect and mitigate PQDs in a timely manner and ultimately contributing to improved power quality and system reliability. |
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| ISSN: | 1996-1073 |