Sustainability

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14 pages, 2912 KiB

Open AccessArticle

Learning-Based Approaches for Voltage Regulation and Control in DC Microgrids with CPL

by Mustafa Güngör and Mehmet Emin Asker

Sustainability 2023, 15(21), 15501; https://doi.org/10.3390/su152115501 - 31 Oct 2023

Viewed by 867

This article introduces a novel approach to voltage regulation in a DC/DC boost converter. The approach leverages two advanced control techniques, including learning-based nonlinear control. By combining the backstepping (BSC) algorithm with artificial neural network (ANN)-based control techniques, the proposed approach aims to [...] Read more.

This article introduces a novel approach to voltage regulation in a DC/DC boost converter. The approach leverages two advanced control techniques, including learning-based nonlinear control. By combining the backstepping (BSC) algorithm with artificial neural network (ANN)-based control techniques, the proposed approach aims to achieve accurate voltage tracking. This is accomplished by employing the nonlinear distortion observer (NDO) technique, which enables a fast dynamic response through load power estimation. The process involves training a neural network using data from the BSC controller. The trained network is subsequently utilized in the voltage regulation controller. Extensive simulations are conducted to evaluate the performance of the proposed control strategy, and the results are compared to those obtained using conventional BSC and model predictive control (MPC) controllers. The simulation results clearly demonstrate the effectiveness and superiority of the suggested control strategy over BSC and MPC. Full article

(This article belongs to the Special Issue Recent Advances in the Design and Control of Modern Power Electronic Interfaces for Renewable Energy Integration)

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23 pages, 4866 KiB

Open AccessArticle

Developing an Integrated Soft-Switching Bidirectional DC/DC Converter for Solar-Powered LED Street Lighting

by Saeed Danyali, Mohammadamin Shirkhani, Jafar Tavoosi, Ali Ghazi Razi, Mostafa M. Salah and Ahmed Shaker

Sustainability 2023, 15(20), 15022; https://doi.org/10.3390/su152015022 - 18 Oct 2023

Cited by 5 | Viewed by 1101

Abstract

In the current era marked by the growing adoption of renewable energy sources, the use of photovoltaic-powered LED streetlights, known for their enhanced efficiency and extended lifespan, is on the rise. This lighting solution encompasses essential components such as a photovoltaic (PV) panel, [...] Read more.

In the current era marked by the growing adoption of renewable energy sources, the use of photovoltaic-powered LED streetlights, known for their enhanced efficiency and extended lifespan, is on the rise. This lighting solution encompasses essential components such as a photovoltaic (PV) panel, an energy storage system, LED luminaires, and a controller responsible for supervising power distribution and system operations. This research introduces a novel approach involving a ZVS (zero-voltage switching) bidirectional boost converter to manage the interaction among the PV panel, LED lights, and battery storage within the system. To elevate system efficiency, a modified version of the conventional bidirectional boost converter is employed, incorporating an auxiliary circuit encompassing a capacitor, inductor, and switch. This configuration enables soft switching in both operational modes. During daytime, the converter operates in the buck mode, accumulating solar energy in the battery. Subsequently, at night, the battery discharges energy to power the LED lights through the converter’s boost operation. In this study, the PET (photo-electro-thermal) theory is harnessed, coupled with insights into heatsink characteristics and the application of a soft-switching bidirectional boost converter. This integrated approach ensures optimal driving of the LED lights at their ideal operating voltage, resulting in the generation of optimal luminous flux. The proposed LED lighting system is thoroughly examined, and theoretical outcomes are validated through simulations using the PSCAD/EMTDC version 4.2.1 software platform. Full article

(This article belongs to the Special Issue Recent Advances in the Design and Control of Modern Power Electronic Interfaces for Renewable Energy Integration)

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16 pages, 7831 KiB

Open AccessArticle

Model Predictive Control of a PUC5-Based Dual-Output Electric Vehicle Battery Charger

by Hamza Makhamreh, Meryem Kanzari and Mohamed Trabelsi

Sustainability 2023, 15(19), 14483; https://doi.org/10.3390/su151914483 - 4 Oct 2023

Viewed by 767

Abstract

In this study, a model predictive control (MPC) technique is applied to a packed-u-cell (PUC)-based dual-output bidirectional electric vehicle (EV) battery charger. The investigated topology is a 5-level PUC-based power factor correction (PFC) rectifier allowing the generation of two levels of DC output [...] Read more.

In this study, a model predictive control (MPC) technique is applied to a packed-u-cell (PUC)-based dual-output bidirectional electric vehicle (EV) battery charger. The investigated topology is a 5-level PUC-based power factor correction (PFC) rectifier allowing the generation of two levels of DC output voltages. The optimization of the MPC cost function is performed by reducing the errors on the capacitors’ voltages (DC output voltages) and the grid (input) current. Moreover, the desired capacitors’ voltages and peak value of the input current are considered within the designed cost function to normalize the errors. In addition, an external PI controller is used to generate the amplitude of the grid current reference based on the computed errors on the capacitors’ voltages. The presented simulation and experimental results recorded using a 1 kW laboratory prototype demonstrate the high performance of the proposed approach in rectifying the AC source at different levels (dual rectifier), while drawing a sinusoidal current from the grid with low THD (around 4%) and ensuring a unity power factor operation. Full article

(This article belongs to the Special Issue Recent Advances in the Design and Control of Modern Power Electronic Interfaces for Renewable Energy Integration)

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29 pages, 5814 KiB

Open AccessArticle

The Optimal Design of a Hybrid Solar PV/Wind/Hydrogen/Lithium Battery for the Replacement of a Heavy Fuel Oil Thermal Power Plant

by Isaac Amoussou, Emmanuel Tanyi, Lajmi Fatma, Takele Ferede Agajie, Ilyes Boulkaibet, Nadhira Khezami, Ahmed Ali and Baseem Khan

Sustainability 2023, 15(15), 11510; https://doi.org/10.3390/su151511510 - 25 Jul 2023

Cited by 5 | Viewed by 2198

Abstract

Renewable energies are clean alternatives to the highly polluting fossil fuels that are still used in the power generation sector. The goal of this research was to look into replacing a Heavy Fuel Oil (HFO) thermal power plant in Limbe, southwest Cameroon, with [...] Read more.

Renewable energies are clean alternatives to the highly polluting fossil fuels that are still used in the power generation sector. The goal of this research was to look into replacing a Heavy Fuel Oil (HFO) thermal power plant in Limbe, southwest Cameroon, with a hybrid photovoltaic (PV) and wind power plant combined with a storage system. Lithium batteries and hydrogen associated with fuel cells make up this storage system. The total cost (TC) of the project over its lifetime was minimized in order to achieve the optimal sizing of the hybrid power plant components. To ensure the reliability of the new hybrid power plant, a criterion measuring the loss of power supply probability (LPSP) was implemented as a constraint. Moth Flame Optimization (MFO), Improved Grey Wolf Optimizer (I-GWO), Multi-Verse Optimizer (MVO), and African Vulture Optimization Algorithm (AVOA) were used to solve this single-objective optimization problem. The optimization techniques entailed the development of mathematical models of the components, with hourly weather data for the selected site and the output of the replaced thermal power plant serving as input data. All four algorithms produced acceptable and reasonably comparable results. However, in terms of proportion, the total cost obtained with the MFO algorithm was 0.32%, 0.40%, and 0.63% lower than the total costs obtained with the I-GWO, MVO, and AVOA algorithms, respectively. Finally, the effect of the type of storage coupled to the PV and wind systems on the overall project cost was assessed. The MFO meta-heuristic was used to compare the results for the PV–Wind–Hydrogen–Lithium Battery, PV–Wind–Hydrogen, and PV–Wind–Lithium Battery scenarios. The scenario of the PV–Wind–Hydrogen–Lithium Battery had the lowest total cost. This scenario’s total cost was 2.40% and 18% lower than the PV–Wind–Hydrogen and PV–Wind–Lithium Battery scenarios. Full article

(This article belongs to the Special Issue Recent Advances in the Design and Control of Modern Power Electronic Interfaces for Renewable Energy Integration)

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28 pages, 2612 KiB

Open AccessArticle

Transfer Learning for Renewable Energy Systems: A Survey

by Rami Al-Hajj, Ali Assi, Bilel Neji, Raymond Ghandour and Zaher Al Barakeh

Sustainability 2023, 15(11), 9131; https://doi.org/10.3390/su15119131 - 5 Jun 2023

Cited by 7 | Viewed by 1662

Abstract

Currently, numerous machine learning (ML) techniques are being applied in the field of renewable energy (RE). These techniques may not perform well if they do not have enough training data. Additionally, the main assumption in most of the ML algorithms is that the [...] Read more.

Currently, numerous machine learning (ML) techniques are being applied in the field of renewable energy (RE). These techniques may not perform well if they do not have enough training data. Additionally, the main assumption in most of the ML algorithms is that the training and testing data are from the same feature space and have similar distributions. However, in many practical applications, this assumption is false. Recently, transfer learning (TL) has been introduced as a promising machine-learning framework to mitigate these issues by preparing extra-domain data so that knowledge may be transferred across domains. This learning technique improves performance and avoids the resource expensive collection and labeling of domain-centric datasets; furthermore, it saves computing resources that are needed for re-training new ML models from scratch. Lately, TL has drawn the attention of researchers in the field of RE in terms of forecasting and fault diagnosis tasks. Owing to the rapid progress of this technique, a comprehensive survey of the related advances in RE is needed to show the critical issues that have been solved and the challenges that remain unsolved. To the best of our knowledge, few or no comprehensive surveys have reviewed the applications of TL in the RE field, especially those pertaining to forecasting solar and wind power, load forecasting, and predicting failures in power systems. This survey fills this gap in RE classification and forecasting problems, and helps researchers and practitioners better understand the state of the art technology in the field while identifying areas for more focused study. In addition, this survey identifies the main issues and challenges of using TL for REs, and concludes with a discussion of future perspectives. Full article

(This article belongs to the Special Issue Recent Advances in the Design and Control of Modern Power Electronic Interfaces for Renewable Energy Integration)

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20 pages, 6361 KiB

Open AccessArticle

Low Computational Burden Predictive Direct Power Control of Quasi Z-Source Inverter for Grid-Tied PV Applications

by Abderahmane Abid, Abualkasim Bakeer, Laid Zellouma, Mansour Bouzidi, Abderezak Lashab and Boualaga Rabhi

Sustainability 2023, 15(5), 4153; https://doi.org/10.3390/su15054153 - 24 Feb 2023

Cited by 2 | Viewed by 1501

Abstract

This paper proposes a simplified predictive direct power control for the grid-tied quasi Z-source inverter. The proposed control implements a model predictive control structure to achieve the maximum obtainable power from the collected PV source. The power delivered to the grid is managed [...] Read more.

This paper proposes a simplified predictive direct power control for the grid-tied quasi Z-source inverter. The proposed control implements a model predictive control structure to achieve the maximum obtainable power from the collected PV source. The power delivered to the grid is managed to compensate for the reactive power and, as needed, to ensure the grid’s stability. A predictive power model for a quasi Z-source inverter is developed in which the proposed control can operate with a fixed switching frequency without a weighting factor. The simplified space vector modulation uses the three appropriate switching vectors that are selected and applied using precalculated switching times during each switching period, in which the required switching vectors are determined only from one sector in the space vector diagram, taking all of the information of the other sectors, which leads to reducing the computational burden. Simulation results and comparative study are used to confirm the proposed control performance for the grid-tied quasi Z-source inverter capable of tracking and generating the maximum power from PV with fast-tracking dynamics, ensuring the ac voltage desired, and better tracking of the active and reactive power reference with the lowest power ripple. The grid current harmonics were tested and conformed to the IEEE-519 standard. Additionally, the proposed simplified PDPC is experimentally validated using the Hardware-in-the-Loop emulator and the C2000TM-microcontroller-LaunchPadXL TMS320F28379D kit, establishing the usability and good result of our proposed control approach in terms of requirements. Full article

(This article belongs to the Special Issue Recent Advances in the Design and Control of Modern Power Electronic Interfaces for Renewable Energy Integration)

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21 pages, 3918 KiB

Open AccessArticle

An Efficacious Modulation Gambit Using Fewer Switches in a Multilevel Inverter

by Sathyavani Bandela, Tara Kalyani Sandipamu, Hari Priya Vemuganti, Shriram S. Rangarajan, E. Randolph Collins and Tomonobu Senjyu

Sustainability 2023, 15(4), 3326; https://doi.org/10.3390/su15043326 - 11 Feb 2023

Viewed by 1016

Abstract

Since multicarrier based modulation techniques are simple to implement and can be used to control inverters at any level, they are frequently employed in modern multilevel inverters in high or medium power applications. When considering the many multi-carrier modulation techniques available, level-shifted pulse-width [...] Read more.

Since multicarrier based modulation techniques are simple to implement and can be used to control inverters at any level, they are frequently employed in modern multilevel inverters in high or medium power applications. When considering the many multi-carrier modulation techniques available, level-shifted pulse-width modulation (LSPWM) is often chosen for its superior harmonic performance. However, this traditional LSPWM method is not suitable for controlling newly proposed reduced switch count (RSC) MLI topologies. The research work in this paper seeks to elucidate the reasons why conventional LSPWM is ineffective in controlling RSC MLI topologies, and proposes a generalized LSPWM system based on logical expressions. The proposed method can be utilized with symmetrical and asymmetrical RSC MLIs, and can be extended to an arbitrary number of levels. The merit of the proposed method for controlling any RSC configuration with satisfactory line-voltage THD (≈1.8%) performance (identical to conventional LSPWM) was evaluated using multiple 13-level asymmetrical RSC-MLI topologies. A MATLAB model was developed and then subjected to simulation and real-world testing to prove the effectiveness of the proposed modulation strategy. Full article

(This article belongs to the Special Issue Recent Advances in the Design and Control of Modern Power Electronic Interfaces for Renewable Energy Integration)

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12 pages, 6283 KiB

Open AccessArticle

An Effective Transformerless PUC7-Based Dynamic Voltage Restorer Using Model Predictive Control

by Mohamed Trabelsi, Hasan Komurcugil, Sertac Bayhan and Haitham Abu-Rub

Sustainability 2023, 15(4), 3041; https://doi.org/10.3390/su15043041 - 7 Feb 2023

Cited by 3 | Viewed by 1225

Abstract

This paper investigates the performance of a seven-level Packed U Cell (PUC7) inverter-based transformerless Dynamic Voltage Restorer (DVR) topology under short-time voltage variations (voltage sags and swells). Unlike the existing multilevel inverter (MLI)-based DVR solutions, the proposed structure requires only one DC voltage [...] Read more.

This paper investigates the performance of a seven-level Packed U Cell (PUC7) inverter-based transformerless Dynamic Voltage Restorer (DVR) topology under short-time voltage variations (voltage sags and swells). Unlike the existing multilevel inverter (MLI)-based DVR solutions, the proposed structure requires only one DC voltage source (suitable for PV-based DVR systems), one capacitor, and six switching devices to generate seven-level output voltage. Moreover, the studied topology is characterized by reduced cost and size due to the elimination of the injection transformer. The PUC7 inverter is controlled using a multi-objective (filtering current, compensating voltage, and PUC capacitor voltage controls) Model Predictive Control (MPC) strategy. Simulation and implementation tests are carried out to demonstrate the high performance of the proposed DVR at steady-state and during transient conditions, while keeping the load voltage unaffected by the disturbances in the grid voltage. Full article

(This article belongs to the Special Issue Recent Advances in the Design and Control of Modern Power Electronic Interfaces for Renewable Energy Integration)

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