Review on small-signal stability of multiple virtual synchronous generators

Renewable and Sustainable Energy Reviews, Journal Year: 2025, Volume and Issue: 215, P. 115543 - 115543

Published: March 10, 2025

Language: Английский

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Energetic Efficiency and Sustainability

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 355 - 376

Published: Feb. 28, 2025

The 21st century faces a growing energy crisis driven by rapid population growth, urbanization and industrialization, particularly in emerging economies. Fossil fuels account for 80% of global consumption, which has led to increased greenhouse gas (GHG) emissions intensified climate change, increasing the need transition energy-efficient systems. Artificial intelligence (AI) emerges as platform address these challenges improving efficiency sustainability. Smart public policies can help harness potential AI promote ensure socio-environmental equity. Collaboration between government stakeholders is essential develop regulatory frameworks that enable an efficient transition. This paper explores applications sustainability, its socio-economic benefits opportunities involved implementing AI-based solutions policies.

Language: Английский

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Advanced Deep Learning Based Predictive Maintenance of DC Microgrids: Correlative Analysis

Energies, Journal Year: 2025, Volume and Issue: 18(6), P. 1535 - 1535

Published: March 20, 2025

This paper presents advanced frameworks for microgrid predictive maintenance by performing a comprehensive correlative analysis of recurrent neural network (RNN) architectures, i.e., RNNs, Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs) photovoltaic (PV) based DC microgrids (MGs). Key contributions this are development architectures on RNN, GRU LSTM, their performance analysis, integrating adaptive threshold technique with the algorithms to detect faulty operations inverters which is indispensable ensuring reliability sustainability distributed energy resources (DERs) in modern MG systems. The proposed models trained evaluated dataset diverse real-world operational scenarios environmental conditions. Moreover, performances those have been compared conventional RNN-based techniques. achieved decremental MAE scores from 12.102 (advanced RNN) 10.182 GRU) 8.263 LSTM) incremental R2 0.941 0.958 GRU), finally 0.971 demonstrate strong capabilities all, while LSTM method outperforming other counterparts. study can contribute emerging technology MGs provide significant insights into modeling RNN improving fault detection findings noteworthy implications enhance efficiency resilience systems, thereby evolving renewable technologies power sector contributing sustainable greener landscape.

Language: Английский

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Advancements in Grid Resilience: Recent Innovations in AI-Driven Solutions

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 105042 - 105042

Published: April 1, 2025

Language: Английский

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