Hybrid Deep Networks for Early Detection of Power Quality Disturbances in Smart Grids: A Resilience Enhancement Approach
Hybrid Deep Networks for Early Detection of Power Quality Disturbances in Smart Grids: A Resilience Enhancement Approach |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-4 |
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| Year of Publication : 2024 | ||
| Author : Sabeena Beevi K, Neethu Mohan, Thasneem A, Krishnendhu Murali, Visakh S. |
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| DOI : 10.14445/22315381/IJETT-V72I4P112 | ||
How to Cite?
Sabeena Beevi K, Neethu Mohan, Thasneem A, Krishnendhu Murali, Visakh S. , "Hybrid Deep Networks for Early Detection of Power Quality Disturbances in Smart Grids: A Resilience Enhancement Approach," International Journal of Engineering Trends and Technology, vol. 72, no. 4, pp. 121-130, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I4P112
Abstract
A smart grid is an electrical power system that uses modern digital technologies and automation to improve reliability, efficiency, and sustainability. However, the integration of these technologies can increase the risk of power quality (PQ) disturbances, which can damage electrical devices and cause significant economic losses. Conventional protection schemes in smart grids typically provide a reactive approach to detecting PQ disturbances. This is not sufficient to address the root cause of these distortions, and thus, advanced protection schemes that incorporate predictive measures are needed. Hence it will be essential to proactively detect the occurrence of power quality disturbances and implement preventive measures to mitigate their impact. Traditional forecasting methods often rely on simple models and assumptions, which can lead to inaccuracies and limitations in the predictions. This paper proposes an advanced model for the early detection of PQ disturbances by utilizing the power of artificial intelligence and machine learning. This paper utilizes a state-of-the-art encoder-decoder model for forecasting Power Quality (PQ) disturbances, accompanied by the implementation of a hybrid Convolutional Neural Network-Long Short-Term Memory model to categorize these disorders effectively. By accurately detecting the disturbances in advance, appropriate mitigation measures can be considered to minimize their effect on the system. Several experiments are conducted to find the optimum model with proper network configurations that detect the PQ disorders. The effectiveness of the proposed model is confirmed through testing with over 18 different classes of simple and mixed distortions. The study further explores the potential of a unified model capable of detecting and classifying multiple disturbances based on forecasted data points.
Keywords
Power grid, Power quality disturbances, Forecasting, Convolutional Neural Network, Long-Short Term Memory, Early detection.
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