Computational Intelligence Techniques for Future Power Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: 15 September 2024 | Viewed by 1891

Special Issue Editors

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Guest Editor
Center for Research on Microgrids (CROM), AAU Energy, Aalborg University, 9220 Aalborg, Denmark
Interests: particle swarm optimization, model predictive control; stochastic optimization; energy management systems; microgrid clusters; system dynamics

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Guest Editor
Department of Mechanical and Production Engineering, Aarhus University, 8200 Aarhus, Denmark
Interests: digital twins; support vector machine; relevance vector machine; system identification; Bayesian analysis; robotics; hybrid systems

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Guest Editor
Center for Research on Microgrids, AAU Energy, 9220 Aalborg, Denmark
Interests: microgrids; space power systems; psychobiology; brain networks
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Special Issue Information

Dear Colleagues,

Learning and adaptation are two essential requirements for solving real-world problems. The increasing complexity of systems and their interaction with the dynamic environment make it necessary to develop new efficient computational tools. Computational intelligence (CI) encompasses models and methods inspired by Nature, biological evolution, and human brain and language to address complex non-linear problems. Neural networks, fuzzy systems, swarm intelligence, and evolutionary algorithms are some of the widely used CI techniques. CI plays an important role in power systems. The ever-increasing complexity of power systems, and the presence of uncertainty make it impracticable to use traditional computing methods.

Prospective authors are invited to submit their original research and review papers to this Special Issue on Computational Intelligence Techniques for Future Power Systems in Electronics.

Topics of interest include, but are not limited to, the recent advances of CI applied to the following areas:

  • Planning, design, control, and operation management;
  • Renewable energy integration;
  • Resilience and cybersecurity;
  • Power quality;
  • Load shedding and load forecasting;
  • Demand-side management;
  • Battery management systems;
  • Fault detection;
  • Power system protection;
  • Prognostics and health management;
  • Electricity markets;
  • Power-to-X technologies;
  • Digital shadow and digital twins;
  • State of health monitoring and predictive maintenance;
  • Decision-support systems;
  • Electric vehicles;
  • Planning of electric vehicle charging stations.

Dr. Najmeh Bazmohammadi
Dr. Ahmad Madary
Prof. Dr. Juan C. Vasquez
Prof. Dr. Josep M. Guerrero
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

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  • computational intelligence
  • evolutionary algorithms
  • swarm intelligence
  • fuzzy systems
  • neural networks
  • heuristic and metaheuristic
  • microgrids
  • power systems
  • data-driven
  • big data
  • data analytics

Published Papers (1 paper)

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22 pages, 6084 KiB  
Battery State-of-Health Estimation: A Step towards Battery Digital Twins
by Vahid Safavi, Najmeh Bazmohammadi, Juan C. Vasquez and Josep M. Guerrero
Electronics 2024, 13(3), 587; - 31 Jan 2024
Cited by 2 | Viewed by 1323
For a lithium-ion (Li-ion) battery to operate safely and reliably, an accurate state of health (SOH) estimation is crucial. Data-driven models with manual feature extraction are commonly used for battery SOH estimation, requiring extensive expert knowledge to extract features. In this regard, a [...] Read more.
For a lithium-ion (Li-ion) battery to operate safely and reliably, an accurate state of health (SOH) estimation is crucial. Data-driven models with manual feature extraction are commonly used for battery SOH estimation, requiring extensive expert knowledge to extract features. In this regard, a novel data pre-processing model is proposed in this paper to extract health-related features automatically from battery-discharging data for SOH estimation. In the proposed method, one-dimensional (1D) voltage data are converted to two-dimensional (2D) data, and a new data set is created using a 2D sliding window. Then, features are automatically extracted in the machine learning (ML) training process. Finally, the estimation of the SOH is achieved by forecasting the battery voltage in the subsequent cycle. The performance of the proposed technique is evaluated on the NASA public data set for a Li-ion battery degradation analysis in four different scenarios. The simulation results show a considerable reduction in the RMSE of battery SOH estimation. The proposed method eliminates the need for the manual extraction and evaluation of features, which is an important step toward automating the SOH estimation process and developing battery digital twins. Full article
(This article belongs to the Special Issue Computational Intelligence Techniques for Future Power Systems)
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