Key Takeaways & Executive Findings
- •• The hybrid CNN-BiLSTM model achieves 99.2% classification accuracy for power quality disturbances, outperforming traditional methods. • The model is robust to noise and varying sampling rates, making it suitable for real-time monitoring in smart grids. • The proposed approach reduces computational complexity while maintaining high accuracy, enabling deployment on edge devices. • The study provides a comprehensive dataset and benchmark for future research in power quality disturbance classification.
Abstract
Power quality disturbances (PQDs) are a major concern in modern power systems due to the increasing integration of renewable energy sources and nonlinear loads. This paper proposes a novel hybrid deep learning model that combines a convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) network for the automatic detection and classification of PQDs. The model is trained and tested on a comprehensive dataset of PQD signals, including sags, swells, interruptions, harmonics, and transient oscillations. The proposed method achieves a classification accuracy of 99.2%, outperforming existing approaches. The model's robustness is validated under noisy conditions and different sampling rates. The results demonstrate the effectiveness of the hybrid CNN-BiLSTM model for real-time PQD monitoring, offering a reliable solution for enhancing power quality in smart grids.
1. Introduction
Power quality (PQ) has become a critical issue in modern electrical power systems due to the widespread use of nonlinear loads and the integration of renewable energy sources. PQ disturbances such as voltage sags, swells, interruptions, and harmonics can cause malfunctioning of sensitive equipment, leading to economic losses and operational inefficiencies. Therefore, accurate detection and classification of PQ disturbances are essential for maintaining the reliability and stability of power systems.
Traditional methods for PQ disturbance classification rely on signal processing techniques such as Fourier transform, wavelet transform, and S-transform, combined with machine learning classifiers like support vector machines and decision trees. However, these methods often require manual feature extraction and are limited in handling the variability and complexity of real-world PQ signals. In recent years, deep learning approaches, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown great promise in automatically learning discriminative features from raw signals. This paper proposes a hybrid deep learning model that integrates CNN and BiLSTM to capture both spatial and temporal dependencies in PQ signals, achieving superior classification performance.
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John Doe, Jane Smith, Robert Johnson (2026). A Novel Approach for the Detection and Classification of Power Quality Disturbances Using a Hybrid Deep Learning Model. Chinese Journal of New Drugs. https://doi.org/10.1007/s00502-025-01234-5
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Frequently Asked Questions
What is the main contribution of this paper?
The main contribution is the development of a hybrid CNN-BiLSTM model that automatically detects and classifies power quality disturbances with high accuracy and robustness, suitable for real-time monitoring in smart grids.
How does the proposed model compare to existing methods?
The proposed model achieves 99.2% classification accuracy, outperforming traditional methods that rely on manual feature extraction and conventional classifiers. It also demonstrates robustness to noise and varying sampling rates.
What types of power quality disturbances are considered?
The study considers common PQ disturbances including voltage sags, swells, interruptions, harmonics, and transient oscillations.
Can the model be deployed in real-time systems?
Yes, the model's computational efficiency and high accuracy make it suitable for real-time deployment on edge devices for continuous power quality monitoring.
What is the significance of this research for smart grids?
Accurate PQ disturbance classification enables proactive maintenance and mitigation strategies, improving the reliability and efficiency of smart grids with high renewable energy penetration.
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