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Computational Intelligence-Based Modeling, Control, Estimation, and Optimization in Electrical Motor/Drive, Renewable Energy, and Power Systems, Volume II

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: 30 December 2024 | Viewed by 5240

Special Issue Editors

Dr. Amirmehdi Yazdani

E-Mail Website
Guest Editor
Discipline of Engineering and Energy, College of Science, Health, Engineering and Education, Murdoch University, Perth, WA 6150, Australia
Interests: autonomous systems; intelligent control; optimization; AI in renewable systems
Special Issues, Collections and Topics in MDPI journals
Dr. Amin Mahmoudi

E-Mail Website
Guest Editor
College of Science and Engineering, Flinders University, Adelaide 5042, Australia
Interests: electrical machines and energy conversion; power electronics and electrical drives; renewable energy systems and energy storage; electric vehicles; power system analysis distributed generation
Special Issues, Collections and Topics in MDPI journals
Dr. GM Shafiullah

E-Mail Website
Guest Editor
School of Engineering and Information Technology, Murdoch University, Perth, Australia
Interests: power systems analysis; renewable energy and its enabling technologies; renewable energy integration; microgrid; smart gird; renewable hydrogen, and machine learning techniques
Special Issues, Collections and Topics in MDPI journals
Dr. Irfan Ahmad Khan

E-Mail Website
Guest Editor
Clean and Resilient Energy Systems (CARES) Lab, Electrical and Computer Engineering Department, Texas A & M University, Galveston, TX 77553, USA
Interests: energy storage systems; smart grids; V2G
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern electrical and renewable energy systems are currently experiencing significant changes with the recent advances in artificial intelligence (AI) techniques and the standards of industry 4.0.

The complex technical changes are urging modern electrical and renewable energy systems to exhibit more stable and excellent operating performance in terms of effectiveness, persistence, robustness and reliability, design simplicity, and smartness.

However, electrical and renewable energy systems are continuously facing technical challenges and difficulties under parametric and/or structural uncertainties, undesired external disturbances, faults and trips, fast-varying references, sensor noises, nonlinearities, component failures, and the restricted online computing time of control execution.

In order to further address the above concerns and improve the overall performance of electrical and renewable energy systems, many computational intelligence (CI) technologies, such as fuzzy logic, neural networks, reinforcement learning, and evolutionary algorithms, have been utilized for modeling, control, estimation, and optimization of electrical and renewable energy systems. Meanwhile, the recent advancements in microcontrollers and digital signal processing technologies such as DSP and FPGA have facilitated real-time and in-the-loop implementation of CI-based methods for electrical and renewable energy systems.

The main goal of this Special Issue is to highlight the recent advancements, developments, and challenges in CI-based modeling, control, estimation, and optimization of electrical motor/drive, renewable energy, and power systems with indications on practical and industry applications.

Topics of interest for publication include, but are not limited to, the following:

  • Fuzzy logic techniques for modeling, control, estimation, and optimization of electrical motor/drive, renewable energy, and power systems
  • CI-based fault detection and prognostics of electrical motor/drive, renewable energy, and power systems
  • Neural network techniques for modeling, control, estimation, and optimization of electrical motor/drive, renewable energy, and power systems
  • CI-based actuators and sensor/data fusion systems design for electrical motor/drive, renewable energy, and power systems
  • Evolutionary algorithms for modeling, control, estimation, and optimization of electrical motor/drive, renewable energy, and power systems
  • CI-based risk and reliability assessment of electrical motor/drive, renewable energy, and power systems
  • Neuro-fuzzy techniques for modeling, control, estimation, and optimization of electrical motor/drive, renewable energy, and power systems
  • CI-IoT-based integrated frameworks for control, estimation, and optimization of electrical motor/drive, renewable energy, and power systems
  • Deep learning and reinforcement learning for modeling, control, estimation, and optimization of electrical motor/drive, renewable energy, and power systems
  • Stochastic learning and statistical algorithms for modeling, control, estimation, and optimization of electrical motor/drive, renewable energy, and power systems

Dr. Amirmehdi Yazdani
Dr. Amin Mahmoudi
Dr. GM Shafiullah
Dr. Irfan Ahmad Khan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fuzzy logic
  • neural networks
  • evolutionary algorithms
  • deep and reinforcement learning

Related Special Issue

Published Papers (6 papers)

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Research

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20 pages, 1571 KiB  
Article
Particle Swarm-Optimized Fuzzy Logic Energy Management of Hybrid Energy Storage in Electric Vehicles
by Joseph Omakor, Mohamad Alzayed and Hicham Chaoui
Energies 2024, 17(9), 2163; https://doi.org/10.3390/en17092163 - 30 Apr 2024
Viewed by 800
Abstract
A lithium-ion battery–ultracapacitor hybrid energy storage system (HESS) has been recognized as a viable solution to address the limitations of single battery energy sources in electric vehicles (EVs), especially in urban driving conditions, owing to its complementary energy features. However, an energy management [...] Read more.
A lithium-ion battery–ultracapacitor hybrid energy storage system (HESS) has been recognized as a viable solution to address the limitations of single battery energy sources in electric vehicles (EVs), especially in urban driving conditions, owing to its complementary energy features. However, an energy management strategy (EMS) is required for the optimal performance of the HESS. In this paper, an EMS based on the particle swarm optimization (PSO) of the fuzzy logic controller (FLC) is proposed. It aims to minimize battery current and power peak fluctuations, thereby enhancing its capacity and lifespan, by optimizing the weights of formulated FLC rules using the PSO algorithm. This paper utilizes the battery temperature as the cost function in the optimization problem of the PSO due to the sensitivity of lithium-ion batteries (LIBs) to operating temperature variations compared to ultracapacitors (UCs). An evaluation of optimized FLC using PSO and a developed EV model is conducted under the Urban Dynamometer Driving Schedule (UDDS) and compared with the unoptimized FLC. The result shows that 5.4% of the battery’s capacity was conserved at 25.5 °C, which is the highest operating temperature attained under the proposed strategy. Full article
(This article belongs to the Special Issue Computational Intelligence-Based Modeling, Control, Estimation, and Optimization in Electrical Motor/Drive, Renewable Energy, and Power Systems, Volume II)
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21 pages, 4682 KiB  
Article
Enhancing Energy Management Strategies for Extended-Range Electric Vehicles through Deep Q-Learning and Continuous State Representation
by Christian Montaleza, Paul Arévalo, Jimmy Gallegos and Francisco Jurado
Energies 2024, 17(2), 514; https://doi.org/10.3390/en17020514 - 20 Jan 2024
Viewed by 1014
Abstract
The efficiency and dynamics of hybrid electric vehicles are inherently linked to effective energy management strategies. However, complexity is heightened due to uncertainty and variations in real driving conditions. This article introduces an innovative strategy for extended-range electric vehicles, grounded in the optimization [...] Read more.
The efficiency and dynamics of hybrid electric vehicles are inherently linked to effective energy management strategies. However, complexity is heightened due to uncertainty and variations in real driving conditions. This article introduces an innovative strategy for extended-range electric vehicles, grounded in the optimization of driving cycles, prediction of driving conditions, and predictive control through neural networks. First, the challenges of the energy management system are addressed by merging deep reinforcement learning with strongly convex objective optimization, giving rise to a pioneering method called DQL-AMSGrad. Subsequently, the DQL algorithm has been implemented, allowing temporal difference-based updates to adjust Q values to maximize the expected cumulative reward. The loss function is calculated as the mean squared error between the current estimate and the calculated target. The AMSGrad optimization method has been applied to efficiently adjust the weights of the artificial neural network. Hyperparameters such as the learning rate and discount factor have been tuned using data collected during real-world driving tests. This strategy tackles the “curse of dimensionality” and demonstrates a 30% improvement in adaptability to changing environmental conditions. With a 20%-faster convergence speed and a 15%-superior effectiveness in updating neural network weights compared to conventional approaches, it also highlights an 18% reduction in fuel consumption in a case study with the Nissan Xtrail e-POWER system, validating its practical applicability. Full article
(This article belongs to the Special Issue Computational Intelligence-Based Modeling, Control, Estimation, and Optimization in Electrical Motor/Drive, Renewable Energy, and Power Systems, Volume II)
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18 pages, 3631 KiB  
Article
Linear Quadratic Gaussian Control of a 6-DOF Aircraft Landing Gear
by Chimezirim Miracle Nkemdirim, Mohamad Alzayed and Hicham Chaoui
Energies 2023, 16(19), 6902; https://doi.org/10.3390/en16196902 - 30 Sep 2023
Viewed by 864
Abstract
The suspension system of the aircraft, provided by the landing gear, is a crucial part of landing, take-off, and taxiing. It is important that this suspension system not only adequately supports the airframe of the aircraft but also provides a comfortable, seamless ride [...] Read more.
The suspension system of the aircraft, provided by the landing gear, is a crucial part of landing, take-off, and taxiing. It is important that this suspension system not only adequately supports the airframe of the aircraft but also provides a comfortable, seamless ride for the passengers. However, the landing gear is usually riddled with issues, such as landing vibrations that affect passenger comfort and cause damage to the aircraft’s airframe. To reduce these vibrations, this paper proposes the use of a Linear Quadratic Gaussian (LQG) controller to control a 6-DOF aircraft landing gear. The LQG controller is an optimal controller that combines the Linear Quadratic Regulator (LQR) controller with the Kalman filter to compute the system’s control signals and estimate the system’s states. In this paper, the state space model of the 6-DOF landing gear is derived, and the mathematical model of the LQG controller is calculated. The controller’s performance is then tested via MATLAB/Simulink and compared with an equally simple control strategy, the PID controller. The results obtained from the testing process indicate that the LQG controller surpasses the PID controller in reducing landing vibrations, maintaining the aircraft’s airframe, and providing passenger comfort. Full article
(This article belongs to the Special Issue Computational Intelligence-Based Modeling, Control, Estimation, and Optimization in Electrical Motor/Drive, Renewable Energy, and Power Systems, Volume II)
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Review

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23 pages, 654 KiB  
Review
Strengthening Power Systems for Net Zero: A Review of the Role of Synchronous Condensers and Emerging Challenges
by Hamid Soleimani, Daryoush Habibi, Mehrdad Ghahramani and Asma Aziz
Energies 2024, 17(13), 3291; https://doi.org/10.3390/en17133291 - 4 Jul 2024
Viewed by 452
Abstract
System strength is both supplied and demanded in a power system during normal operations and in the presence of disturbances. This is characterised by stable voltage and frequency, supporting renewable generation such as wind and solar. Because the retirement of synchronous generators reduces [...] Read more.
System strength is both supplied and demanded in a power system during normal operations and in the presence of disturbances. This is characterised by stable voltage and frequency, supporting renewable generation such as wind and solar. Because the retirement of synchronous generators reduces system strength supply, and the connection of new inverter-based resource (IBR) generators increases demand, there is an urgent need for new sources of system strength. This paper provides an overview of the challenges brought about by grid modernisation. It highlights tangible solutions provided by synchronous condensers (SCs) to bolster grid strength, stability, and reliability while accommodating the rising influx of renewable energy sources (RESs). Furthermore, this paper examines the role of SCs in improving weak grids, voltage control, power quality, short-circuit levels, and inertia management. It introduces the role of innovative hybrid synchronous condenser (HSC) systems to boost grid reliability and resilience. It also elaborates on the optimisation strategies for SC sizing, placement, and control and outlines economic aspects of their deployment. The review also highlights future directions and challenges in SC technology, emphasising the need for ongoing research and development to enhance system design and operation. Full article
(This article belongs to the Special Issue Computational Intelligence-Based Modeling, Control, Estimation, and Optimization in Electrical Motor/Drive, Renewable Energy, and Power Systems, Volume II)
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39 pages, 5000 KiB  
Review
A Review of Control Techniques for Inverter-Based Distributed Energy Resources Applications
by Seyedmohammad Hasheminasab, Mohamad Alzayed and Hicham Chaoui
Energies 2024, 17(12), 2940; https://doi.org/10.3390/en17122940 - 14 Jun 2024
Viewed by 511
Abstract
The escalating adoption of low-carbon energy technologies underscores the imperative to transition from conventional fossil fuel-dependent sources to sustainable alternatives. The expansion of Distributed Energy Resources (DERs) signifies an essential shift towards a more resilient and environmentally friendly energy landscape. However, integrating inverter-based [...] Read more.
The escalating adoption of low-carbon energy technologies underscores the imperative to transition from conventional fossil fuel-dependent sources to sustainable alternatives. The expansion of Distributed Energy Resources (DERs) signifies an essential shift towards a more resilient and environmentally friendly energy landscape. However, integrating inverter-based DERs introduces challenges, particularly in system inertia and grid instability. This review delves into the critical area of inverter-based grid control strategies, focusing on the primary and secondary control mechanisms. Primary controls are investigated, including traditional droop control and low-voltage ride-through (LVRT) capability. The secondary control strategies, involving virtual impedance (VI) and load frequency control (LFC), are vital in maintaining grid stability and reliability are reviewed. The aim is to offer a comprehensive understanding of the principles, advancements, and challenges associated with inverter-based grid controls, contributing valuable insights for the seamless integration of DERs into modern power grids. Full article
(This article belongs to the Special Issue Computational Intelligence-Based Modeling, Control, Estimation, and Optimization in Electrical Motor/Drive, Renewable Energy, and Power Systems, Volume II)
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22 pages, 2417 KiB  
Review
Review of the Integration of Hybrid Electric Turbochargers for Mass-Produced Road Vehicles
by Cosmin Constantin Suciu, Sorin Vlad Igret, Ion Vetres and Ioana Ionel
Energies 2024, 17(6), 1484; https://doi.org/10.3390/en17061484 - 20 Mar 2024
Viewed by 882
Abstract
This study presents the findings of a comprehensive SWOT analysis on the integration of hybrid electric turbochargers (HETs) in mass-produced road vehicles. Through a synthesis of multiple research findings, this study compared the performance of HETs on thermal engines versus traditional turbochargers and [...] Read more.
This study presents the findings of a comprehensive SWOT analysis on the integration of hybrid electric turbochargers (HETs) in mass-produced road vehicles. Through a synthesis of multiple research findings, this study compared the performance of HETs on thermal engines versus traditional turbochargers and HETs on thermal engines versus HETs on hybrid engines. The analysis highlights key strengths, weaknesses, opportunities, and threats associated with the adoption of HET technology in the automotive industry. The results of the SWOT analysis provide valuable insights for both manufacturers and consumers regarding the feasibility and benefits of adopting HET technology in modern vehicles. By elucidating the fundamental mechanics of turbochargers and demonstrating the potential of hybrid electric turbocharging, this study contributes to a deeper understanding of the role of HETs in shaping the future of automotive engineering. In conclusion, this study underscores the potential of HETs to substantially mitigate the environmental impact of the transportation sector by reducing emissions and conserving energy. The novelty of this study is reflected in its comprehensive synthesis of multiple research findings, offering insights into the feasibility and benefits of adopting HET technology in modern vehicles, thereby contributing to a deeper understanding of the role of HETs in shaping the future of automotive engineering and highlighting their continued significance, as evidenced by the systematic SWOT analysis presented. Their ability to optimize fuel efficiency and power output, coupled with the feasibility of downsized engines, positions HETs as an attractive option for sustainable mobility solutions. Further research is warranted to comprehensively understand the environmental and economic implications of widespread HET adoption. Full article
(This article belongs to the Special Issue Computational Intelligence-Based Modeling, Control, Estimation, and Optimization in Electrical Motor/Drive, Renewable Energy, and Power Systems, Volume II)
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