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

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

Deadline for manuscript submissions: closed (10 May 2022) | Viewed by 16715

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
Co-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
Co-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

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
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 (5 papers)

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Research

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13 pages, 9051 KiB  
Article
Magnetic Equivalent Circuit Modelling of Synchronous Reluctance Motors
by Rekha Jayarajan, Nuwantha Fernando, Amin Mahmoudi and Nutkani Ullah
Energies 2022, 15(12), 4422; https://0-doi-org.brum.beds.ac.uk/10.3390/en15124422 - 17 Jun 2022
Viewed by 2097
Abstract
This paper proposes a modelling technique for Synchronous Reluctance Motors (SynRMs) based on a generalized Magnetic Equivalent Circuit (MEC). The proposed model can be used in the design of any number of stator teeth, rotor poles, and rotor barrier combinations. This technique allows [...] Read more.
This paper proposes a modelling technique for Synchronous Reluctance Motors (SynRMs) based on a generalized Magnetic Equivalent Circuit (MEC). The proposed model can be used in the design of any number of stator teeth, rotor poles, and rotor barrier combinations. This technique allows elimination of infeasible machine solutions during the initial machine sizing stage, resulting in a lower cohort of feasible machine solutions that can be further optimized using finite element methods. Therefore, saturation effects, however, are not considered in the modelling. This paper focuses on modelling a generic structure of the SynRM in modular form and is then extended to a full SynRM model. The proposed model can be iteratively used for any symmetrical rotor pole and stator teeth combination. The developed technique is applied to model a 4-pole, 36 slot SynRM as an example, and the implemented model is executed following a time stepping strategy. The motor characteristics such as flux distribution and torque of the developed SynRM model is compared with finite elemental analysis (FEA) simulation results. 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)
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16 pages, 8407 KiB  
Article
Design of a Deflection Switched Reluctance Motor Control System Based on a Flexible Neural Network
by Zheng Li, Xiaopeng Wei, Jinsong Wang, Libo Liu, Shenhui Du, Xiaoqiang Guo and Hexu Sun
Energies 2022, 15(11), 4172; https://0-doi-org.brum.beds.ac.uk/10.3390/en15114172 - 06 Jun 2022
Cited by 3 | Viewed by 1541
Abstract
Deflection switched reluctance motors (DSRM) are prone to chattering at low speeds, which always affects the output efficiency of the DSRM and the mechanical loss of the motor. Combining the characteristics of a traditional reluctance motor with the strong nonlinear and high coupling [...] Read more.
Deflection switched reluctance motors (DSRM) are prone to chattering at low speeds, which always affects the output efficiency of the DSRM and the mechanical loss of the motor. Combining the characteristics of a traditional reluctance motor with the strong nonlinear and high coupling of the DSRM, a control system for a DSRM based on a flexible neural network (FNN) is proposed in this paper. Based on the better robustness and fault tolerance of fuzzy PI control, the given speed signal is adjusted and converted into a torque control signal. As a result, the FNN control module possesses the strong self-learning ability and adaptive adjustment ability necessary to obtain the control voltage signal. Through simulations and experiments, it was verified that the control system can run stably on DSRM and shows good dynamic performance and anti-interference ability. 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)
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19 pages, 2809 KiB  
Article
Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems
by Ramadoss Janarthanan, R. Uma Maheshwari, Prashant Kumar Shukla, Piyush Kumar Shukla, Seyedali Mirjalili and Manoj Kumar
Energies 2021, 14(20), 6584; https://0-doi-org.brum.beds.ac.uk/10.3390/en14206584 - 13 Oct 2021
Cited by 47 | Viewed by 2585
Abstract
The real-time application research on the Fuzzy Logic Systems (FLSs) and Artificial Neural Networks (ANN) is vast and, in this paper, a technique for a photovoltaic failure analysis using the type 2 FLS and ANN is proposed. The method is proposed to build [...] Read more.
The real-time application research on the Fuzzy Logic Systems (FLSs) and Artificial Neural Networks (ANN) is vast and, in this paper, a technique for a photovoltaic failure analysis using the type 2 FLS and ANN is proposed. The method is proposed to build T2 FLS with a guaranteed value equal to or lower than T2 and ANN. Several explanations are conducted to illustrate the effectiveness of the methodologies. It is found that both the type 2 Fuzzy and ANN can be configured for productive actions in applications for a PV fault analysis, and choice is typically applied. The methods discussed in this paper lay the groundwork for developing FLSs and ANNs with durable characteristics that will be extremely useful in many functional applications. The result demonstrates that specific fault categories can be detected using the fault identification method, such as damaged PV modules and partial PV unit shades. The average detection performance is similar in both ANN and fuzzy techniques. In comparison, both systems evaluated show approximately the same performance during experiments. The architecture of the type 2 fuzzy logic system and ANN with radial basic function, including the roles of the output port and the rules for identifying the type of defect in the PV structure is slightly different. 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)
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19 pages, 5865 KiB  
Article
Optimal Sizing of Rooftop PV and Battery Storage for Grid-Connected Houses Considering Flat and Time-of-Use Electricity Rates
by Iflah Javeed, Rahmat Khezri, Amin Mahmoudi, Amirmehdi Yazdani and G. M. Shafiullah
Energies 2021, 14(12), 3520; https://0-doi-org.brum.beds.ac.uk/10.3390/en14123520 - 13 Jun 2021
Cited by 27 | Viewed by 3300
Abstract
This paper investigates a comparative study for practical optimal sizing of rooftop solar photovoltaic (PV) and battery energy storage systems (BESSs) for grid-connected houses (GCHs) by considering flat and time-of-use (TOU) electricity rate options. Two system configurations, PV only and PV-BESS, were optimally [...] Read more.
This paper investigates a comparative study for practical optimal sizing of rooftop solar photovoltaic (PV) and battery energy storage systems (BESSs) for grid-connected houses (GCHs) by considering flat and time-of-use (TOU) electricity rate options. Two system configurations, PV only and PV-BESS, were optimally sized by minimizing the net present cost of electricity for four options of electricity rates. A practical model was developed by considering grid constraints, daily supply of charge of electricity, salvation value and degradation of PV and BESS, actual annual data of load and solar, and current market price of components. A rule-based energy management system was examined for GCHs to control the power flow among PV, BESS, load, and grid. Various sensitivity analyses are presented to examine the impacts of grid constraint and electricity rates on the cost of electricity and the sizes of the components. Although the capacity optimization model is generally developed for any case study, a grid-connected house in Australia is considered as the case system in this paper. It is found that the TOU-Flat option for the PV-BESS configuration achieved the lowest NPC compared to other configuration and options. The optimal capacities of rooftop PV and BESS were obtained as 9 kW and 6 kWh, respectively, for the PV-BESS configuration with TOU-Flat according to two performance metrices: net present cost and cost of electricity. 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)
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Review

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27 pages, 7551 KiB  
Review
Losses in Efficiency Maps of Electric Vehicles: An Overview
by Emad Roshandel, Amin Mahmoudi, Solmaz Kahourzade, Amirmehdi Yazdani and GM Shafiullah
Energies 2021, 14(22), 7805; https://0-doi-org.brum.beds.ac.uk/10.3390/en14227805 - 22 Nov 2021
Cited by 14 | Viewed by 5799
Abstract
In some applications such as electric vehicles, electric motors should operate in a wide torque and speed ranges. An efficiency map is the contour plot of the maximum efficiency of an electric machine in torque-speed plane. It is used to provide an overview [...] Read more.
In some applications such as electric vehicles, electric motors should operate in a wide torque and speed ranges. An efficiency map is the contour plot of the maximum efficiency of an electric machine in torque-speed plane. It is used to provide an overview on the performance of an electric machine when operates in different operating points. The electric machine losses in different torque and speed operating points play a prominent role in the efficiency of the machines. In this paper, an overview about the change of various loss components in torque-speed envelope of the electric machines is rendered to show the role and significance of each loss component in a wide range of torque and speeds. The research gaps and future research subjects based on the conducted review are reported. The role and possibility of the utilization of the computational intelligence-based modeling of the losses in improvement of the loss estimation is discussed. 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)
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