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Neural Computing and Applications
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Hybrid

Neural Computing and Applications - Topical Collection on AI Techniques for Optimal Control and Operation of Modern Power Systems

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.

Other commonly employed AI approaches in power system applications includes expert systems, fuzzy logic systems, adaptive fuzzy logic systems, artificial neural networks (ANN), adaptive-neuro fuzzy inference systems (ANFIS), support vector machines (SVMs), decision trees, and evolutionary computing, among others. These techniques have proven effective in decision-making and control actions, supporting secure and stable operation of power systems. However, there remains a vast array of emerging AI techniques that have not been thoroughly explored or applied in power system operation and control applications. These include deep learning, reinforcement learning, Blockchain, cloud computing, cognitive AI, explainable AI, transfer learning, convolutional neural networks (CNNs), global optimization, meta-heuristics based control strategies and hybrid algorithms techniques. These promising avenues hold potential for further advancements in power system operation and control, awaiting in-depth exploration and practical application. By leveraging these advanced AI techniques, power systems can achieve improved efficiency, reliability, and sustainability. This issue targets to encourage the latest advances in the field of utilization of AI techniques for the control and operation of modern power systems, enabling control engineers to maximize the use of engineering assets in close alignment with available infrastructure and technical operating conditions.

Scope

This topical collection will cover a wide range of topics related to the application of AI techniques in power systems, including but not limited to:

  • AI-based control strategies for power system stability and reliability.
  • Machine learning approaches for demand response and load forecasting.
  • Deep learning techniques for fault detection, diagnosis, and self-healing in power systems.
  • Reinforcement learning for optimal power dispatch and economic operation.
  • AI-driven predictive maintenance and condition monitoring of power system assets.
  • Data analytics and AI methods for grid integration of renewable energy sources.
  • AI applications in smart grid operations and management.
  • Cybersecurity and AI-based anomaly detection for power system protection.
  • Evolutionary algorithms, swarm intelligence, nature and biologically inspired meta-heuristics based control strategies for AGC/LFC/AVR, etc.

Guest Editors

Dr. Yogendra Arya (Lead Guest Editor), Department of Electrical Engineering, J.C. Bose University of Science & Technology, YMCA, Faridabad, India, yarya@jcboseust.ac.in
Dr.  Sandeep Dhundhara, Department of Basic Engineering, COAE&T, CCS Haryana Agricultural University, Hisar, Haryana, India, sandeep08@hau.ac.in
Dr. Yajvender Pal Verma, Department of Electrical & Electronics Engineering, University Institute of Engineering and Technology, Panjab University, Chandigarh, India, yp_verma@pu.ac.in

Manuscript submission deadline: 1st November 2024

Peer Review Process

All the papers will go through peer review,  and will be reviewed by at least two reviewers. A thorough check will be completed, and the guest editor will check any significant similarity between the manuscript under consideration and any published paper or submitted manuscripts of which they are aware. In such case, the article will be directly rejected without proceeding further. Guest editors will make all reasonable effort to receive the reviewer’s comments and recommendation on time. The final decision will be taken by the EiC.

The submitted papers must provide original research that has not been published nor currently under review by other venues. Previously published conference papers should be clearly identified by the authors at the submission stage and an explanation should be provided about how such papers have been extended to be considered for this special issue (with at least 30% difference from the original works).

Submission Guidelines

Paper submissions for the collection should strictly follow the submission format and guidelines (https://www.springer.com/journal/521/submission-guidelines (this opens in a new tab)). Each manuscript should not exceed 16 pages in length (inclusive of figures and tables).

Manuscripts must be submitted to the journal online system at https://www.editorialmanager.com/ncaa/default.aspx (this opens in a new tab) or via the 'Submit manuscript' button on the journal homepage.
Authors should select “TC: AI Techniques for Optimal Control and Operation of Modern Power Systems” during the submission step ‘Additional Information’.

Author Resources

Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by other journals.  
Springer provides a host of information about publishing in a Springer Journal on our Journal Author Resources page, including  FAQs (this opens in a new tab),  Tutorials (this opens in a new tab)  along with  Help and Support (this opens in a new tab).
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