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Application of Advanced Control Theories to Power Electronics and Power Systems

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F3: Power Electronics".

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 15677

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Prof. Dr. Isabel Jesus

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Department of Electrical Engineering, Institute of Engineering-Polytechnic of Porto (ISEP/IPP), Rua Dr. António Bernardino de Almeida, 431, 4249-015 Porto, Portugal
Interests: photovoltaic systems; fractional order control systems; fuzzy control systems; evolutionary algorithms
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Special Issue Information

Dear Colleagues,

Power electronics and power systems involve many areas strongly important in the real world. Nowadays, these systems are related to the areas of energy markets and regulation, electric machines and drives, electronic devices, inverters, power converters, control, computational techniques or artificial intelligent algorithms, among many others.

The research in the area of power electronics and power systems focuses its attention on the management of electrical power and control, so that global energy consumption can be reduced. Improving the efficiency of power conversion systems is crucial to reducing energy waste, particularly during conversion.

In these fields, many works have aimed to improve the cost-effectiveness and efficiency of power electronics technologies and to guarantee the stability, reliability, and flexibility of power systems. To achieve that, several techniques have been implemented in industries in the fields of optimization converter topologies, design of advanced control algorithms, smart grid technologies, and exploring novel semiconductor devices. Nevertheless, some challenges related to power generation, the integration of renewable energy sources, the implementation of more efficient control algorithms, and the improvement of energetic distribution still need to be addressed.

In this perspective, the main topics of this Special Issue include, but are not limited to:

Advanced power semiconductor devices;

Medium voltage power electronics for applications in renewable energy;

Energy storage and smart grid systems;

High-frequency-power-electronic conversion systems;

DC power grids;

Power system analyses;

Modeling and simulation of power systems;

Energy system optimization;

Electricity markets;

Distributed generation;

Artificially intelligent algorithms for power systems.

Prof. Dr. Isabel Jesus
Guest Editor

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Keywords

advanced power semiconductor devices
medium voltage power electronics for applications in renewable energy
energy storage and smart grid systems
high-frequency-power-electronic conversion systems
DC power grids
power system analyses
modeling and simulation of power systems
energy system optimization
electricity markets
distributed generation
artificially intelligent algorithms for power systems

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Related Special Issue

Application of Advanced Control Theories to Power Electronics and Power Systems: 2nd Edition in Energies (1 article)

Published Papers (9 papers)

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Research

Jump to: Review

24 pages, 5848 KiB

Open AccessArticle

Transient Stability Analysis and Emergency Generator Tripping Control Based on Spatio-Temporal Graph Deep Learning

by Shuaibo Wang, Jie Zeng, Jie Zhang, Zhuohang Liang, Yihua Zhu and Shufang Li

Energies 2025, 18(4), 993; https://doi.org/10.3390/en18040993 - 19 Feb 2025

Viewed by 355

Abstract

This paper addresses the challenge of achieving fast and accurate transient stability analysis and emergency control in power systems, which are crucial for reliable grid operation under disturbances. To this end, we propose a spatio-temporal graph deep learning approach leveraging Diffusion Convolutional Gated Recurrent Units (DCGRUs) for transient stability assessment and coherent generator group prediction. Unlike traditional methods, our approach explicitly represents transient responses as spatio-temporal graph data, capturing both topological and dynamic dependencies. The DCGRU model effectively extracts these features, and the predicted coherent generator groups are incorporated into the single-machine infinite-bus equivalence method to design an emergency generator tripping scheme. Simulation analysis results on both benchmark and real-world power grids validate the proposed method’s feasibility and effectiveness in enhancing transient stability analysis and emergency control. Full article

(This article belongs to the Special Issue Application of Advanced Control Theories to Power Electronics and Power Systems)

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21 pages, 12122 KiB

Open AccessArticle

Adaptive Energy Management Control for Efficiency Improvement of Supercapacitor-Based Energy Recovery System

by Ģirts Staņa, Martin Makar and Kaspars Kroičs

Energies 2025, 18(2), 336; https://doi.org/10.3390/en18020336 - 14 Jan 2025

Viewed by 610

Abstract

The efficiency of the DC microgrid can be improved by adding additional energy storage to recover regenerative braking energy. Supercapacitors are well suited for such applications. To make such devices easily connectable to the existing grid, it is beneficial to measure the voltage of the DC bus. The paper proposes a method on how to estimate load current indirectly from this voltage measurement. Estimated current is used in the developed energy management algorithm that adapts current reference of an energy storage system to discharge stored energy in the most energy efficient way. The paper presents simulation and experimental results and shows in some cases efficiency improvement even by more than five percent. Full article

(This article belongs to the Special Issue Application of Advanced Control Theories to Power Electronics and Power Systems)

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15 pages, 3897 KiB

Open AccessArticle

Proposal of Low-Speed Sensorless Control of IPMSM Using a Two-Interval Six-Segment High-Frequency Injection Method with DC-Link Current Sensing

by Daniel Konvicny, Pavol Makys and Alex Franko

Energies 2024, 17(22), 5789; https://doi.org/10.3390/en17225789 - 20 Nov 2024

Viewed by 686

Abstract

This paper proposes a modification to existing saliency-based, sensorless control strategy for interior permanent magnet synchronous motors. The proposed approach leverages a two-interval, six-segment high-frequency voltage signal injection technique. It aims to improve rotor position and speed estimation accuracy when utilizing a single current sensor positioned in the inverter’s DC-bus circuit. The key innovation lies in modifying both the high-frequency signal injection and demodulation processes to address challenges in accurate phase current reconstruction and rotor position estimation, at low and zero speeds. A significant modification to the traditional high-frequency voltage signal injection method is introduced, which involves splitting the signal injection and the field-oriented control algorithm into two distinct sampling and switching periods. This approach ensures that no portion of the injected voltage space vector falls into the immeasurable region of space vector modulation, which could otherwise compromise current measurements. The dual-period structure, termed the two-interval six-segment high-frequency injection, allows for more precise current measurement during the signal injection period while maintaining optimal motor control during the field-oriented control period. Furthermore, this paper explores a different demodulation technique that improves the estimation of rotor position and speed. By employing a synchronous filter in combination with a phase-locked loop, the proposed method enhances the robustness of the system against noise and inaccuracies typically encountered in phase current reconstruction. The effectiveness of the proposed modifications is demonstrated through comprehensive simulation results. These results confirm that the enhanced method offers more reliable rotor position and speed estimates compared to the existing sensorless technique, making it particularly suitable for applications requiring high precision in motor control. Full article

(This article belongs to the Special Issue Application of Advanced Control Theories to Power Electronics and Power Systems)

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13 pages, 9091 KiB

Open AccessArticle

Influence of Ti Layers on the Efficiency of Solar Cells and the Reduction of Heat Transfer in Building-Integrated Photovoltaics

by Paweł Kwaśnicki, Dariusz Augustowski, Agnieszka Generowicz and Anna Kochanek

Energies 2024, 17(21), 5327; https://doi.org/10.3390/en17215327 - 25 Oct 2024

Cited by 1 | Viewed by 932

Abstract

This study examined the potential application of metallic coatings to mitigate the adverse effects of ultraviolet (UV) and infrared (IR) light on photovoltaic modules. Titanium coatings were applied on low-iron glass surfaces using magnetron sputtering at powers of 1000, 1250, 1500, 1750, 2000, and 2500 W. The module with uncoated glass served as a reference. The Ti layer thickness varied from 7 nm to 20 nm. Transmittance and reflectance spectra were used to calculate visible light transmittance L_t, UV light transmittance L_tuv, solar transmittance g, and visible light reflectance L_r. The obtained parameters indicated that the thinnest Ti layer (1000 W) coating did not significantly affect light transmittance, but thicker layers did, altering the L_t, g, and L_r factors. However, every sample noticeably changed L_tuv, probably due to the natural formation of a UV-reflective thin TiO₂ layer. The differences in fill factor (FF) were minimal, but thicker coatings resulted in lower open-circuit voltages (U_oc) and short-circuit currents (I_sc), leading to a reduction in power conversion efficiency (PCE). Notably, a Ti coating deposited at 2500 W reduced the power of the photovoltaic module by 78% compared to the uncoated sample but may protect modules against the unwanted effects of overheating. Full article

(This article belongs to the Special Issue Application of Advanced Control Theories to Power Electronics and Power Systems)

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27 pages, 1868 KiB

Open AccessArticle

A New Stochastic Controller for Efficient Power Extraction from Small-Scale Wind Energy Conversion Systems under Random Load Consumption

by Abdelhakim Tighirt, Mohamed Aatabe, Fatima El Guezar, Hassane Bouzahir, Alessandro N. Vargas and Gabriele Neretti

Energies 2024, 17(19), 4927; https://doi.org/10.3390/en17194927 - 1 Oct 2024

Cited by 1 | Viewed by 1373

Abstract

This paper presents an innovative scheme to enhance the efficiency of power extraction from wind energy conversion systems (WECSs) under random loads. The study investigates how stochastic load consumption, modeled and predicted using a Markov chain process, impacts WECS efficiency. The suggested approach regulates the rectifier voltage rather than the rotor speed, making it a sensorless and reliable method for small-scale WECSs. Nonlinear WECS dynamics are represented using Takagi–Sugeno (TS) fuzzy modeling. Furthermore, the closed-loop system’s stochastic stability and recursive feasibility are guaranteed regardless of random load changes. The performance of the suggested controller is compared with the traditional perturb-and-observe (P&O) algorithm under varying wind speeds and random load variations. Simulation results show that the proposed approach outperforms the traditional P&O algorithm, demonstrating higher tracking efficiency, rapid convergence to the maximum power point (MPP), reduced steady-state oscillations, and lower error indices. Enhancing WECS efficiency under unpredictable load conditions is the primary contribution, with simulation results indicating that the tracking efficiency increases to

99.93 %

. Full article

(This article belongs to the Special Issue Application of Advanced Control Theories to Power Electronics and Power Systems)

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23 pages, 7606 KiB

Open AccessArticle

Electrification of Agricultural Machinery: One Design Case of a 4 kW Air Compressor

by Hsin-Chang Chen, Yulian Fatkur Rohman, Muhammmad Bilhaq Ashlah, Hao-Ting Lin and Wu-Yang Sean

Energies 2024, 17(15), 3647; https://doi.org/10.3390/en17153647 - 24 Jul 2024

Viewed by 1228

Abstract

In response to the global pursuit of net-zero carbon emissions, the electrification of agricultural machinery is becoming a significant research and development trend. This study introduces the overall design of a 4 kW air compressor aimed at achieving a green vision for agricultural machinery. The design focuses on providing continuous and stable power and air output using a lithium-ion battery. Durability and cost-effectiveness are prioritized, with a particular emphasis on the Arduino system for integrating battery and motor systems to withstand harsh conditions and ensure ease of maintenance. A permanent magnet brushless motor was selected as the power source, paired with an optimized pulley to supply the proper torque to the air compressor. The system employs an Arduino-based feedback control sensor for air pressure regulation, ensuring energy efficiency. The primary energy source is a 48 V lithium iron phosphate battery, known for its high energy density and safety. The battery design focuses on system integration, addressing specific environmental discharge requirements. The embedded battery management system provides thermal and lifecycle parameter estimation, guaranteeing long-duration power supply and safe operation under various conditions. Unlike traditional fuel-driven systems, lithium iron phosphate batteries do not emit harmful gases, aligning with environmental standards. System integration testing demonstrated that the air pressure feedback control effectively meets the energy-saving requirements by digitally reducing power output as air accumulates in the chamber. Bench testing confirmed that the system performs as designed, achieving the desired results and advancing the goal of sustainable agricultural machinery. Full article

(This article belongs to the Special Issue Application of Advanced Control Theories to Power Electronics and Power Systems)

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23 pages, 5817 KiB

Open AccessArticle

Investigation of Load, Solar and Wind Generation as Target Variables in LSTM Time Series Forecasting, Using Exogenous Weather Variables

by Thomas Shering, Eduardo Alonso and Dimitra Apostolopoulou

Energies 2024, 17(8), 1827; https://doi.org/10.3390/en17081827 - 11 Apr 2024

Cited by 5 | Viewed by 1406

Abstract

Accurately forecasting energy metrics is essential for efficiently managing renewable energy generation. Given the high variability in load and renewable energy power output, this represents a crucial area of research in order to pave the way for increased adoption of low-carbon energy solutions. Whilst the impact of different neural network architectures and algorithmic approaches has been researched extensively, the impact of utilising additional weather variables in forecasts have received far less attention. This article demonstrates that weather variables can have a significant influence on energy forecasting and presents methodologies for using these variables within a long short-term memory (LSTM) architecture to achieve improvements in forecasting accuracy. Moreover, we introduce the use of the seasonal components of the target time series, as exogenous variables, that are also observed to increase accuracy. Load, solar and wind generation time series were forecast one hour ahead using an LSTM architecture. Time series data were collected in five Spanish cities and aggregated for analysis, alongside five exogenous weather variables, also recorded in Spain. A variety of LSTM architectures and hyperparameters were investigated. By tuning exogenous weather variables, a 33% decrease in mean squared error was observed for solar generation forecasting. A 22% decrease in mean absolute squared error (MASE), compared to 24-h ahead forecasts made by the Transmission Service Operator (TSO) in Spain, was also observed for solar generation. Compared to using the target variable in isolation, utilising exogenous weather variables decreased MASE by approximately 10%, 15% and 12% for load, solar and wind generation, respectively. By using the seasonal component of the target variables as an exogenous variable itself, we demonstrated decreases in MASE of 19%, 12% and 8% for load, solar and wind generation, respectively. These results emphasise the significant benefits of incorporating weather and seasonal components into energy-related time series forecasts. Full article

(This article belongs to the Special Issue Application of Advanced Control Theories to Power Electronics and Power Systems)

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22 pages, 6820 KiB

Open AccessArticle

Sizing PV and BESS for Grid-Connected Microgrid Resilience: A Data-Driven Hybrid Optimization Approach

by Mahtab Murshed, Manohar Chamana, Konrad Erich Kork Schmitt, Suhas Pol, Olatunji Adeyanju and Stephen Bayne

Energies 2023, 16(21), 7300; https://doi.org/10.3390/en16217300 - 27 Oct 2023

Cited by 6 | Viewed by 3814

Abstract

This article presents a comprehensive data-driven approach on enhancing grid-connected microgrid grid resilience through advanced forecasting and optimization techniques in the context of power outages. Power outages pose significant challenges to modern societies, affecting various sectors such as industries, households, and critical infrastructures. The research combines statistical analysis, machine-learning algorithms, and optimization methods to address this issue to develop a holistic approach for predicting and mitigating power outage events. The proposed methodology involves the use of Monte Carlo simulations in MATLAB for future outage prediction, training a Long Short-Term Memory (LSTM) network for forecasting solar irradiance and load profiles with a dataset spanning from 2009 to 2018, and a hybrid LSTM-Particle Swarm Optimization (PSO) model to improve accuracy. Furthermore, the role of battery state of charge (SoC) in enhancing system resilience is explored. The study also assesses the techno-economic advantages of a grid-tied microgrid integrated with solar panels and batteries over conventional grid systems. The proposed methodology and optimization process demonstrate their versatility and applicability to a wide range of microgrid design scenarios comprising solar PV and battery energy storage systems (BESS), making them a valuable resource for enhancing grid resilience and economic efficiency across diverse settings. The results highlight the potential of the proposed approach in strengthening grid resilience by improving autonomy, reducing downtime by 25%, and fostering sustainable energy utilization by 82%. Full article

(This article belongs to the Special Issue Application of Advanced Control Theories to Power Electronics and Power Systems)

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Review

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29 pages, 1866 KiB

Open AccessReview

A Review of Smart Energy Management in Residential Buildings for Smart Cities

by Faiza Qayyum, Harun Jamil and Faiyaz Ali

Energies 2024, 17(1), 83; https://doi.org/10.3390/en17010083 - 22 Dec 2023

Cited by 18 | Viewed by 3685

Abstract

This survey critically examines the integration of energy management systems within smart residential buildings, serving as key nodes in the smart city network. It systematically maps out the intricate relationships between smart grid technologies, energy storage capabilities, infrastructure development, and their confluence in residential settings. From the evolution of power generation methods, incorporating both traditional and renewable sources, to the cutting-edge progress in energy-efficient transport systems, we assess their cumulative impact on the smart urban environment. While our approach is rooted in theoretical exploration rather than mathematical modeling, we provide a comprehensive review of the prevailing frameworks and methodologies that drive energy management in smart urban ecosystems. We also discuss the implications of these systems on urban sustainability and the critical importance of integrating various energy domains to facilitate effective energy governance. By bringing together a diverse array of scholarly insights, our paper aspires to enhance the understanding of energy interdependencies in smart cities and to catalyze the development of innovative, sustainable policies and practices that will define the future of urban energy management. Through this expanded perspective, we underscore the necessity of cross-disciplinary research and the adoption of holistic strategies to optimize energy usage, reduce carbon footprints, and promote resilient urban living in the era of smart cities. Full article

(This article belongs to the Special Issue Application of Advanced Control Theories to Power Electronics and Power Systems)

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