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Editorial Board Members’ Collection Series: "Artificial Intelligence for Energy Systems"
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Editorial Board Members’ Collection Series: "Artificial Intelligence for Energy Systems"

A topical collection in Energies (ISSN 1996-1073). This collection belongs to the section "F5: Artificial Intelligence and Smart Energy".

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Editors

Prof. Dr. Paul Stewart

E-Mail Website
Collection Editor
Institute for Engineering, Computing and Advanced Manufacturing, University of Cumbria, Lancaster LA1 3JD, UK
Interests: complex system simulation, design and optimisation; artificial intelligence and advanced control systems; power and energy architectures and electrical machines, drives and systems; energy conversion and storage; remote monitoring and sensing; prognostics and diagnostics; low carbon and low emissions operations
Special Issues, Collections and Topics in MDPI journals
Dr. Marcin Kaminski

E-Mail Website
Collection Editor
Department of Electrical Machines, Drives and Measurements, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
Interests: neural networks; adaptive control; fuzzy logic; optimization of control structures using nature-inspired techniques; hardware implementations (FPGA, DSP, microcontrollers) of algorithms based on artificial intelligence; electrical drives; machine learning; digital image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Mohsin Jamil

E-Mail Website
Collection Editor
Department of Electrical and Computer Engineering (EN-3031), Faculty of Engineering & Applied Science, Memorial University of Newfoundland, St. John's, NL A1B 3X5, Canada
Interests: power electronic converters for smart grids; power and energy; instrumentation, controls and automation; mechatronics and intelligent systems; robotics; signal processing
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear Colleagues,

As our global complex energy systems are transformed away from fossil fuels to clean, sustainable sources, the change will require heightened levels of sophistication in terms of complex system control and optimization. Challenging invention and adaptation will be required, particularly in energy generation (electrical, hydrogen, etc.), transmission and distribution. Coupled to this are the development of tools and technology for the delivery of sustainable energy production, distribution and consumption in the context of societal, economic and environmental pressures and requirements. Traditional system and design modelling, optimization, control theory and applications are of limited effectiveness when applied to problems of this complexity. It is likely that Artificial Intelligence supported by big data analytics and machine learning will be the main tools to solve these problems.

This research collection focuses on the application of AI-based techniques across the spectrum of energy system applications from renewable, sustainable generation through distribution to consumption via complex systems and optimisation. Including smart grid, electrical drives, power electronics and electric vehicles. The goal of this collection is to cover recent advances in energy system planning and operation.

The collection Topics include but are not limited to:

  • Energy system planning, including smart grid;
  • Energy system optimisation;
  • Control, identification and optimisation of electrical drives applied in energy systems;
  • Electric vehicle energy systems integration;
  • Load forecasting and system adaptation;
  • Condition monitoring of energy systems (including electrical drives);
  • Renewable energy resource allocation;
  • System planning and scheduling;
  • Systems economics and societal impact.

Prof. Dr. Paul Stewart
Dr. Marcin Kaminski
Dr. Mohsin Jamil
Collection 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 collection 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

  • artificial intelligence, expert systems, machine learning
  • complex modelling and prediction
  • smart cities and smart grids
  • sustainable energy systems
  • renewable energy sources
  • energy storage
  • hydrogen and electricity energy vectors
  • wind, tidal and solar generation
  • biomass
  • neural networks
  • adaptive control
  • deep learning
  • fuzzy logic
  • optimisation techniques (e.g., swarm inspired methods)
  • heuristics and hyperheuristics

Published Papers (1 paper)

2022

16 pages, 6696 KiB  
Article
A Robust Nonlinear Sliding Mode Controller for a Three-Phase Grid-Connected Inverter with an LCL Filter
by Abu Sufyan, Mohsin Jamil, Salman Ghafoor, Qasim Awais, Hafiz Ali Ahmad, Ashraf Ali Khan and Hassan Abouobaida
Energies 2022, 15(24), 9428; https://doi.org/10.3390/en15249428 - 13 Dec 2022
Cited by 9 | Viewed by 1953
Abstract
In distributed power generation systems, grid-connected inverters are becoming an attractive means of delivering the energy generated from renewable sources into the grid. However, the performance of the current controller drastically decreases in the presence of model uncertainty, grid harmonics, filter parametric, and [...] Read more.
In distributed power generation systems, grid-connected inverters are becoming an attractive means of delivering the energy generated from renewable sources into the grid. However, the performance of the current controller drastically decreases in the presence of model uncertainty, grid harmonics, filter parametric, and grid impedance variations, which can jeopardize the entire system’s stability. This paper presents a novel design of a super-twisting integral sliding mode control (ST-ISMC) strategy for the first time in the application of a three-phase voltage source grid-connected inverter. The designed controller has shown robustness and maintains a low total harmonic distortion (THD) in the presence of filter parameters drift, grid impedance variation, and grid harmonics distortion. The super-twisting action is added to remove the chattering problem associated with the conventional SMC strategy, and integral action is adopted to improve the grid’s current steady-state error. The modeling and simulation of a complete system are carried out using MATLAB/SIMULINK. Finally, a real-world hardware prototype system is fabricated to demonstrate the performance and effectiveness of the proposed controller. Full article
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