Special Issue "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 "Electrical Power and Energy System".

Deadline for manuscript submissions: 10 May 2022.

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 marine vehicles; guidance , navigation, and control; trajectory optimization, task manipulation and and AI-based decision making frameworks
Special Issues and Collections in MDPI journals
Dr. Amin Mahmoudi
E-Mail Website
Co-Guest Editor
College of Science and Engineering, Flinders University, Adelaide 5042, Australia
Interests: renewable energy; energy storage system; power system analysis; power system control and protection; power electronics; electrical machines and drives
Special Issues and Collections 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 and Collections 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 papers will be 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 2000 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

Published Papers (2 papers)

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Research

Article
Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems
Energies 2021, 14(20), 6584; https://doi.org/10.3390/en14206584 - 13 Oct 2021
Viewed by 322
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
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Article
Optimal Sizing of Rooftop PV and Battery Storage for Grid-Connected Houses Considering Flat and Time-of-Use Electricity Rates
Energies 2021, 14(12), 3520; https://doi.org/10.3390/en14123520 - 13 Jun 2021
Cited by 4 | Viewed by 808
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
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