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文摘   2024-10-25 08:01   芬兰  

Neural Computing and Applications (Springer旗下SCI期刊)最新专题“AI Techniques for Optimal Control and Operation of Modern Power Systems”目前开始火热征稿啦!课题广泛+文章处理效率高,欢迎各位投稿!

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【征稿背景】
The control and operation of modern power systems can greatly benefit from the application of various cutting-edge artificial intelligence (AI) techniques. Reinforcement Learning (RL) offers an exciting opportunity to optimize control actions, such as load shedding and generation dispatch, by enabling systems to learn optimal strategies through interaction with the environment. Generative adversarial networks (GANs) have shown promise in generating synthetic power system data, facilitating improved system modeling accuracy and supporting decision-making processes. Deep neural networks (DNNs) are effective tools for tasks such as load forecasting, fault detection, generation control and power system stability analysis, thanks to their ability to extract complex patterns and relationships from large datasets. Long short-term memory (LSTM) networks, with their focus on time-series data analysis, can be employed for short-term load forecasting and real-time prediction of power system parameters. Convolutional neural networks (CNNs) excel in processing spatial data and can be utilized for fault detection and classification based on images captured by phasor measurement units (PMUs) or overhead line inspections. Hybrid models, which combine different AI techniques, offer the potential to enhance the accuracy and effectiveness of power system control and operation. Moreover, natural language processing (NLP) techniques can extract valuable insights from textual data, aiding decision-making in areas like maintenance and incident reports.
【征稿主题】

感兴趣的主题包括但不限于:
  1. AI-based control strategies for power system stability and reliability.

  2. Machine learning approaches for demand response and load forecasting.

  3. Deep learning techniques for fault detection, diagnosis, and self-healing in power systems.

  4. Reinforcement learning for optimal power dispatch and economic operation.

  5. AI-driven predictive maintenance and condition monitoring of power system assets.

  6. Data analytics and AI methods for grid integration of renewable energy sources.

  7. AI applications in smart grid operations and management.

  8. Cybersecurity and AI-based anomaly detection for power system protection.

  9. Evolutionary algorithms, swarm intelligence, nature and biologically inspired meta-heuristics based control strategies for AGC/LFC/AVR, etc.


【截止日期】

请注意,截稿日期为202411月01日如有延期请参见留言区)。

【期刊网址】(阅读原文,网址在维护打不开的话投搞时候系统选择这个专题即可)

https://link.springer.com/journal/521/updates/26047324


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