Machine learning for power systems
Machine Learning for Sustainable Power Systems: AIoT
This research investigates the transformative role of Machine Learning (ML) in optimizing smart-grid inverter systems, specifically emphasizing solar photovoltaics. A comprehensive literature review informed the development of a robust methodology, leveraging Artificial Intelligence of Things (AIoT) and ML algorithms.
Machine Learning Applications in Electric Power Systems:
The integration of machine learning techniques into electric power systems has revolutionized the way we generate, transmit, and distribute electrical energy. Machine learning algorithms have demonstrated their potential to enhance the efficiency, reliability, and sustainability of power systems by leveraging the vast amount of data available
[1911.03737] Physics-Informed Neural Networks for Power Systems
Exploiting the underlying physical laws governing power systems, and inspired by recent developments in the field of machine learning, this paper proposes a neural network training procedure that can make use of the wide range of mathematical models describing power system behavior, both in steady-state and in dynamics.
Applications of Machine Learning in Modern Power Systems: A
Therefore, this paper aims to provide an extensive review of recent ML techniques as well as their usage in modern power systems in terms of power quality, power stability, energy and load forecasting, protection and fault diagnosis, and cybersecurity.
Machine Learning-Based Intelligent Power Systems
Some of the key applications of machine learning in power systems include load forecasting, predictive maintenance, load scheduling, state estimation, optimization, fault detection, energy management, power quality monitoring, etc.
Deep learning in power systems research: A review
This study explores the theoretical advantages of deep representation learning in power systems research. We review deep learning methodologies presented and applied in a wide range of supervised, unsupervised, and semi-supervised applications as well as reinforcement learning tasks.
Machine Learning for Power Systems
Machine learning (ML) is one of the emerging technologies for implementing the next generation smart grid. In recent years, the PES community has witnessed significant efforts to explore the potential of machine learning for solving complex power system
A Review of Machine Learning Applications in Power System
Therefore, this paper aims to systematically review the existing application of machine learning methods on power system resilience enhancement, to expand the interest of researchers and scholars in this topic, and to jointly promote the application of artificial
Machine learning driven smart electric power systems: Current
The application of machine learning models in such energy systems may be useful in operational planning, managing the consumer demands, integration of renewable energy systems, and so forth.
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