Research

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17 pages, 1249 KiB

Open AccessArticle

Nonlinear Modelling and Control of a Power Smoothing System for a Novel Wave Energy Converter Prototype

by Simon Krüner and Christoph M. Hackl

Sustainability 2022, 14(21), 13708; https://0-doi-org.brum.beds.ac.uk/10.3390/su142113708 - 22 Oct 2022

Cited by 2 | Viewed by 1341

This contribution presents the control of the electrical system of a Wave Energy Converter (WEC) prototype developed by SINN Power. Due to the movement of the waves, the generated power has a very high fluctuation with a period of a few seconds. To [...] Read more.

This contribution presents the control of the electrical system of a Wave Energy Converter (WEC) prototype developed by SINN Power. Due to the movement of the waves, the generated power has a very high fluctuation with a period of a few seconds. To be able to use this power, it has to be smoothed. The used Energy Storage System (ESS) is a supercapacitor bank, which is directly connected to the DC-link. Therefore, the DC-link voltage has to fluctuate according to the generated power, to charge and discharge the capacitors. The smoothed power is used to charge batteries with a DC/DC converter, which is typically used for photovoltaic applications. The DC-link voltage can be controlled with the current through the DC/DC converter, yielding a nonlinear control system where a stability analysis is carried out to prove a safe and stable operation. Measurement results at the prototype under typical sea conditions are presented, which fit the simulation results. With the presented control system, smooth power output can be guaranteed. Full article

(This article belongs to the Special Issue Intelligent Mechatronic and Renewable Energy Systems)

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23 pages, 574 KiB

Open AccessArticle

Gaussian Process Regression Based Multi-Objective Bayesian Optimization for Power System Design

by Nicolai Palm, Markus Landerer and Herbert Palm

Sustainability 2022, 14(19), 12777; https://0-doi-org.brum.beds.ac.uk/10.3390/su141912777 - 07 Oct 2022

Cited by 4 | Viewed by 1547

Abstract

Within a disruptively changing environment, design of power systems becomes a complex task. Meeting multi-criteria requirements with increasing degrees of freedom in design and simultaneously decreasing technical expertise strengthens the need for multi-objective optimization (MOO) making use of algorithms and virtual prototyping. In [...] Read more.

Within a disruptively changing environment, design of power systems becomes a complex task. Meeting multi-criteria requirements with increasing degrees of freedom in design and simultaneously decreasing technical expertise strengthens the need for multi-objective optimization (MOO) making use of algorithms and virtual prototyping. In this context, we present Gaussian Process Regression based Multi-Objective Bayesian Optimization (GPR-MOBO) with special emphasis on its profound theoretical background. A detailed mathematical framework is provided to derive a GPR-MOBO computer implementable algorithm. We quantify GPR-MOBO effectiveness and efficiency by hypervolume and the number of required computationally expensive simulations to identify Pareto-optimal design solutions, respectively. For validation purposes, we benchmark our GPR-MOBO implementation based on a mathematical test function with analytically known Pareto front and compare results to those of well-known algorithms NSGA-II and pure Latin Hyper Cube Sampling. To rule out effects of randomness, we include statistical evaluations. GPR-MOBO turnes out as an effective and efficient approach with superior character versus state-of-the art approaches and increasing value-add when simulations are computationally expensive and the number of design degrees of freedom is high. Finally, we provide an example of GPR-MOBO based power system design and optimization that demonstrates both the methodology itself and its performance benefits. Full article

(This article belongs to the Special Issue Intelligent Mechatronic and Renewable Energy Systems)

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16 pages, 1749 KiB

Open AccessArticle

The Impact of Industrial Intelligence on Energy Intensity: Evidence from China

by Xiekui Zhang, Peiyao Liu and Hongfei Zhu

Sustainability 2022, 14(12), 7219; https://0-doi-org.brum.beds.ac.uk/10.3390/su14127219 - 13 Jun 2022

Cited by 9 | Viewed by 2244

Abstract

With the sustainable development of cyber-physical science and information technologies, artificial intelligence technology is becoming more and more mature and has been used widely in various walks of life. As one part of this development, industrial intelligence has been applied diffusely to improve [...] Read more.

With the sustainable development of cyber-physical science and information technologies, artificial intelligence technology is becoming more and more mature and has been used widely in various walks of life. As one part of this development, industrial intelligence has been applied diffusely to improve the productivity and energy efficiency of factories and governments. Meanwhile, the social ecological environment change has also caused widespread social concern in recent years, and energy efficiency, which is related to climate change, has forced almost every country to reduce their carbon emissions for bettering environmental quality. However, there is little research that has studied this problem from the perspective of industrial robots, even though they are an indispensable part in modern industrial systems. In order to promote the development of artificial intelligence and its application in industrial fields effectively and raise the energy consumption efficiency of production, this paper investigates the impact of industrial intelligence on energy intensity in China, as it is the largest manufacturing and energy consumption country in the world, and we also hope that the experimental results in this study can guide relevant departments and governments to formulate reasonable policies to enhance the utilization efficiency of energy and improve the environmental quality synchronously. For the sake of the rigor of this research and the accuracy of the experimental results, this study explores the corresponding effect mechanisms of industrial intelligence on China’s energy intensity from 2008 to 2019 by using the classical linear regression model OLS (Ordinary Least Squares) and WLS (Weighted Least Squares) separately, which were applied in the previous studies. The results of this study reveal three major findings. The first is that it further proves that the application of artificial intelligence can indeed reduce energy intensity, and the wide applications of artificial intelligence can reduce energy intensity significantly by reducing energy consumption. Besides, the ownership structure of state-owned enterprises will have a positive impact on energy efficiency. The environmental performance of state-owned enterprises is better than that of foreign-funded and private enterprises. Finally, the models further verify the significant impact of the enterprise scale effect on energy intensity. It will bring about the improvement of economic efficiency, and the larger the enterprise, the more obvious the economies of scale effect and the lower the energy intensity. Full article

(This article belongs to the Special Issue Intelligent Mechatronic and Renewable Energy Systems)

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16 pages, 1140 KiB

Open AccessArticle

Dependence of IPMSM Motor Efficiency on Parameter Estimates

by Antonín Glac, Václav Šmídl, Zdeněk Peroutka and Christoph M. Hackl

Sustainability 2021, 13(16), 9299; https://0-doi-org.brum.beds.ac.uk/10.3390/su13169299 - 19 Aug 2021

Cited by 4 | Viewed by 2394

Abstract

The efficiency of an IPMSM motor is influenced by the operating point of the machine. Conventional approaches to generate measured efficiency maps may be too expensive to use in some situations, thus it often replaced by simpler variants based on parametric models. A [...] Read more.

The efficiency of an IPMSM motor is influenced by the operating point of the machine. Conventional approaches to generate measured efficiency maps may be too expensive to use in some situations, thus it often replaced by simpler variants based on parametric models. A promising approach is to combine model-based approaches with online parameter identification methods which would allow following changes of the parameters. However, such approaches may also result in deteriorated performance if the online parameter estimation is inaccurate. We present a systematic experimental study of the influence of the parameter estimates on the efficiency of a 4.5 kW IPMSM drive and analyze the sources of inaccuracy. The first outcome of this study is that none of the tested methods performs well when the machine is fully loaded, which deteriorates overall performance. The second outcome is that the conventional maximum torque per ampere/current (MTPA/MTPC) is not an accurate optimization criterion. The overall performance of the compared methods thus heavily depends on the testing profile. When a significant part of the profile is at full load, the methods based on online estimation are unsuitable and parameters estimated offline using frequency domain provides better efficiency under the maximum torque per current control strategy. Full article

(This article belongs to the Special Issue Intelligent Mechatronic and Renewable Energy Systems)

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23 pages, 2861 KiB

Open AccessArticle

Reduced-Complexity Model Predictive Control with Online Parameter Assessment for a Grid-Connected Single-Phase Multilevel Inverter

by Ibrahim Harbi, Mohamed Abdelrahem, Mostafa Ahmed and Ralph Kennel

Sustainability 2020, 12(19), 7997; https://0-doi-org.brum.beds.ac.uk/10.3390/su12197997 - 27 Sep 2020

Cited by 14 | Viewed by 2303

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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21 pages, 8537 KiB

Open AccessArticle

A Sustainable Distributed Building Integrated Photo-Voltaic System Architecture with a Single Radial Movement Optimization Based MPPT Controller

by Mehdi Seyedmahmoudian, Gokul Sidarth Thirunavukkarasu, Elmira Jamei, Tey Kok Soon, Ben Horan, Saad Mekhilef and Alex Stojcevski

Sustainability 2020, 12(16), 6687; https://0-doi-org.brum.beds.ac.uk/10.3390/su12166687 - 18 Aug 2020

Cited by 4 | Viewed by 2437

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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22 pages, 3644 KiB

Open AccessEditor’s ChoiceArticle

Highly Efficient and Robust Grid Connected Photovoltaic System Based Model Predictive Control with Kalman Filtering Capability

by Mostafa Ahmed, Mohamed Abdelrahem and Ralph Kennel

Sustainability 2020, 12(11), 4542; https://0-doi-org.brum.beds.ac.uk/10.3390/su12114542 - 03 Jun 2020

Cited by 33 | Viewed by 4090

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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15 pages, 3931 KiB

Open AccessArticle

Smart Wifi Thermostat-Enabled Thermal Comfort Control in Residences

by Robert Lou, Kevin P. Hallinan, Kefan Huang and Timothy Reissman

Sustainability 2020, 12(5), 1919; https://0-doi-org.brum.beds.ac.uk/10.3390/su12051919 - 03 Mar 2020

Cited by 22 | Viewed by 7833

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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Review

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29 pages, 1852 KiB

Open AccessReview

Digital Twins for the Future Power System: An Overview and a Future Perspective

by Zhao Song, Christoph M. Hackl, Abhinav Anand, Andre Thommessen, Jonas Petzschmann, Omar Kamel, Robert Braunbehrens, Anton Kaifel, Christian Roos and Stefan Hauptmann

Sustainability 2023, 15(6), 5259; https://0-doi-org.brum.beds.ac.uk/10.3390/su15065259 - 16 Mar 2023

Cited by 6 | Viewed by 5173

Abstract

The inevitable transition of the power system toward a sustainable and renewable-energy centered power system is accompanied by huge versatility and significant challenges. A corresponding shift in operation strategies, embracing more intelligence and digitization, e.g., a Cyber-Physical System (CPS), is needed to achieve [...] Read more.

The inevitable transition of the power system toward a sustainable and renewable-energy centered power system is accompanied by huge versatility and significant challenges. A corresponding shift in operation strategies, embracing more intelligence and digitization, e.g., a Cyber-Physical System (CPS), is needed to achieve an optimal, reliable and secure operation across all system levels (components, units, plants, grids) and by the use of big data. Digital twins (DTs) are a promising approach to realize CPS. In this paper, their applications in power systems are reviewed comprehensively. The review reveals that there exists a gap between available DT definitions and the requirements for DTs utilized in future power systems. Therefore, by adapting the current definitions to these requirements, a generic definition of a “Digital Twin System (DTS)” is introduced which finally allows proposing a multi-level and arbitrarily extendable “System of Digital Twin Systems (SDTSs)” idea. The SDTSs can be realized with an open-source framework that serves as a central data and communication interface between different DTSs which can interact by “Reporting Modules” and are regulated by “Control Modules” (CMs). Exemplary application scenarios involving multiple system levels are discussed to illustrate the capabilities of the proposed SDTS concept. Full article

(This article belongs to the Special Issue Intelligent Mechatronic and Renewable Energy Systems)

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