Special Issue "Intelligent Mechatronic and Renewable Energy Systems"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editor

Prof. Dr. Christoph M. Hackl
Website
Guest Editor
Department of Electrical Engineering and Information Technology, Munich University of Applied Sciences (MUAS), 80335 Munich, Germany
Interests: modeling; control; efficiency enhancements; fault detection and condition monitoring of mechatronic and renewable energy systems
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Special Issue Information

Dear Colleagues,

Mechatronic and renewable energy systems are the driver of our world, with electrical energy as their basis. Renewable energy systems such as photovoltaic (PV) systems, concentrated solar power (CSP) systems, wind turbines, geothermal power plants, wave converters, and bio gas power plants “produce” electrical energy. Mechatronic energy systems such as electric vehicles or aircrafts, traction systems, robots, industrial drives or domestic appliances consume and/or (partially) store electrical energy. Of utter importance is a reliable and efficient operation of these systems and their interconnection with the future power grid to ensure global welfare and sustainability.

Therefore, I cordially invite original manuscripts presenting recent advances in these important and interdisciplinary research fields and applications with particular (though not exclusive) focus on:

  • Nonlinear and hybrid modeling approaches (considering also the switching behavior of the power electronic actuators);
  • Nonlinear, optimal, and fault-tolerant control strategies;
  • Efficiency enhancements (by intelligent design and/or control);
  • Fault detection methods; and
  • Condition-monitoring approaches.

The Special Issue shall present cutting-edge research results in these emerging fields as a basis for a reliable and efficient operation of future mechatronic and renewable energy systems. It is key to present all research results in a mathematically thorough (e.g., in state space) but understandable manner to ease approachability and re-implementation by the readers. All results should be validated by both simulation and measurement results.

Prof. Dr.-Ing. Christoph M. Hackl
Guest Editor

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. Sustainability 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 1800 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

  • nonlinear and hybrid modeling (including switching behavior)
  • nonlinear, optimal, and fault-tolerant control
  • efficiency enhancements
  • fault detection and condition monitoring

Published Papers (4 papers)

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Research

Open AccessArticle
Reduced-Complexity Model Predictive Control with Online Parameter Assessment for a Grid-Connected Single-Phase Multilevel Inverter
Sustainability 2020, 12(19), 7997; https://doi.org/10.3390/su12197997 - 27 Sep 2020
Abstract
This paper proposes a finite control set model predictive control (FCS-MPC) with a reduced computational burden for a single-phase grid-connected modified packed U-cell multilevel inverter (MPUC-MLI) with two control objectives: reference current tracking and switching frequency minimization. The considered competitive topology consists of [...] Read more.
This paper proposes a finite control set model predictive control (FCS-MPC) with a reduced computational burden for a single-phase grid-connected modified packed U-cell multilevel inverter (MPUC-MLI) with two control objectives: reference current tracking and switching frequency minimization. The considered competitive topology consists of two units with six active switches and two DC sources in each unit, allowing the generation of 49 levels in the output voltage, which is considered a significant reduction in the active and passive components compared to the conventional and recently developed topologies of multilevel inverters (MLIs). This topology has 49 different switching states, which means that 49 predictions of the future current and 49 calculations of the cost function are required for each evaluation of the conventional FCS-MPC. Accordingly, the computational load is heavy. Thus, this paper presents two reduced-complexity FCS-MPC methods to reduce the calculation burden. The first technique reduces the computational load almost to half by computing the reference voltage and dividing the states of the MLI into two sets. Based on the reference voltage polarity, one set is defined and evaluated to specify the optimal state, which has a minimal cost function. However, in the second proposed method, only three states of the 49 states are evaluated each iteration, achieving a significant reduction in the execution time and superior control performance compared to the conventional FCS-MPC. A mathematical analysis is conducted based on the reference voltage value to locate the three vectors under evaluation. In the second part of the paper, the sensitivity to parameter variations for the proposed simplified FCS-MPC is investigated and tackled by employing an extended Kalman filter (EKF). In addition, noise related to variable measurement is filtered in the proposed system with the EKF. The simulation investigation was performed using MATLAB/Simulink to validate the system under different operating conditions. Full article
(This article belongs to the Special Issue Intelligent Mechatronic and Renewable Energy Systems)
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Open AccessArticle
A Sustainable Distributed Building Integrated Photo-Voltaic System Architecture with a Single Radial Movement Optimization Based MPPT Controller
Sustainability 2020, 12(16), 6687; https://doi.org/10.3390/su12166687 - 18 Aug 2020
Abstract
The solar photo-voltaic systems control architecture has a substantial influence over the cost, efficiency, and accuracy of maximum power point tracking under partial shading conditions. In this paper, a novel distributed architecture of a building integrated photo-voltaic system equipped with a single maximum [...] Read more.
The solar photo-voltaic systems control architecture has a substantial influence over the cost, efficiency, and accuracy of maximum power point tracking under partial shading conditions. In this paper, a novel distributed architecture of a building integrated photo-voltaic system equipped with a single maximum power point tracking controller is presented in order to address the drawbacks associated with respect to cost, complexity and efficiency of the existing photo-voltaic system architectures. In addition, a radial movement optimization based maximum power point tracking control algorithm is designed, developed, and validated using the proposed system architecture under five different partial shading conditions. The inferences obtained from the validation results of the proposed distributed system architecture indicated that cost was reduced by 75% when compared to the commonly used decentralised systems. The proposed distributed building integrated photo-voltaic system architecture is also more efficient, robust, reliable, and accurate. Full article
(This article belongs to the Special Issue Intelligent Mechatronic and Renewable Energy Systems)
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Open AccessArticle
Highly Efficient and Robust Grid Connected Photovoltaic System Based Model Predictive Control with Kalman Filtering Capability
Sustainability 2020, 12(11), 4542; https://doi.org/10.3390/su12114542 - 03 Jun 2020
Cited by 1
Abstract
Renewable energy sources, especially photovoltaic (PV) ones, are gaining more and more interest due to the predicted lack of conventional sources over the coming years. That shortage is not the only concern, as environmental issues add to this concern also. Thus, this study [...] Read more.
Renewable energy sources, especially photovoltaic (PV) ones, are gaining more and more interest due to the predicted lack of conventional sources over the coming years. That shortage is not the only concern, as environmental issues add to this concern also. Thus, this study proposes two-stage PV grid connected system, which is supported with extended Kalman filter (EKF) for parameter estimation. In the first stage, maximum power point tracking (MPPT) for the boost converter is accomplished using new MPPT method in which the switching state of the converter is directly generated after the measurement stage, so it is called direct switching MPPT technique. This technique is compared with the conventional finite control set model predictive control (FCS-MPC) method, where the design of the cost function is based on minimizing the error between the reference and the actual current. The reference current is obtained by employing perturb and observe (P&O) method. In the second stage, the two-level inverter is controlled by means of model predictive control (MPC) with reduced computation burden. Further, to overcome the parameter variations, which is a very common problem in MPC applications, an extended Kalman filter is utilized to eliminate the control algorithm’s dependency on the parameters by providing an efficient estimation. After the inverter, an RL filter is inserted to guarantee the quality of the currents injected into the grid. Finally, the system is validated using Matlab under different operating conditions of atmospheric variation and parameter changes. Full article
(This article belongs to the Special Issue Intelligent Mechatronic and Renewable Energy Systems)
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Open AccessArticle
Smart Wifi Thermostat-Enabled Thermal Comfort Control in Residences
Sustainability 2020, 12(5), 1919; https://doi.org/10.3390/su12051919 - 03 Mar 2020
Cited by 1
Abstract
The present research leverages prior works to automatically estimate wall and ceiling R-values using a combination of a smart WiFi thermostat, building geometry, and historical energy consumption data to improve the calculation of the mean radiant temperature (MRT), which is integral to the [...] Read more.
The present research leverages prior works to automatically estimate wall and ceiling R-values using a combination of a smart WiFi thermostat, building geometry, and historical energy consumption data to improve the calculation of the mean radiant temperature (MRT), which is integral to the determination of thermal comfort in buildings. To assess the potential of this approach for realizing energy savings in any residence, machine learning predictive models of indoor temperature and humidity, based upon a nonlinear autoregressive exogenous model (NARX), were developed. The developed models were used to calculate the temperature and humidity set-points needed to achieve minimum thermal comfort at all times. The initial results showed cooling energy savings in excess of 83% and 95%, respectively, for high- and low-efficiency residences. The significance of this research is that thermal comfort control can be employed to realize significant heating, ventilation, and air conditioning (HVAC) savings using readily available data and systems. Full article
(This article belongs to the Special Issue Intelligent Mechatronic and Renewable Energy Systems)
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