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Search Results (697)

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Keywords = proportional–integral (P-I) controller

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22 pages, 3730 KiB  
Article
Support-Vector-Regression-Based Intelligent Control Strategy for DFIG Wind Turbine Systems
by Farhat Nasim, Shahida Khatoon, Ibraheem Nasiruddin, Mohammad Shahid, Shabana Urooj and Basel Bilal
Machines 2025, 13(8), 687; https://doi.org/10.3390/machines13080687 - 5 Aug 2025
Abstract
Achieving sustainable energy goals requires efficient integration of renewables like wind energy. Doubly fed induction generator (DFIG)-based wind turbine systems (WTSs) operate efficiently across a range of speeds, making them well-suited for modern renewable energy systems. However, sudden wind speed variations can cause [...] Read more.
Achieving sustainable energy goals requires efficient integration of renewables like wind energy. Doubly fed induction generator (DFIG)-based wind turbine systems (WTSs) operate efficiently across a range of speeds, making them well-suited for modern renewable energy systems. However, sudden wind speed variations can cause power oscillations, rotor speed fluctuations, and voltage instability. Traditional proportional–integral (PI) controllers struggle with such nonlinear, rapidly changing scenarios. A control approach utilizing support vector regression (SVR) is proposed for the DFIG wind turbine system. The SVR controller manages both active and reactive power by simultaneously controlling the rotor- and grid-side converters (RSC and GSC). Simulations under a sudden wind speed variation from 10 to 12 m per second show the SVR approach reduces settling time significantly (up to 70.3%), suppresses oscillations in rotor speed, torque, and power output, and maintains over 97% DC-link voltage stability. These improvements enhance power quality, reliability, and system performance, demonstrating the SVR controller’s superiority over conventional PI methods for variable-speed wind energy systems. Full article
(This article belongs to the Special Issue Modelling, Design and Optimization of Wind Turbines)
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19 pages, 9300 KiB  
Article
Decoupling Control for the HVAC Port of Power Electronic Transformer
by Wusong Wen, Tianwen Zhan, Yingchao Zhang and Jintong Nie
Energies 2025, 18(15), 4131; https://doi.org/10.3390/en18154131 - 4 Aug 2025
Abstract
For the high-voltage AC port of power electronic transformer (HVAC-PET) with three-phase independent DC buses on the low-voltage side, a decoupling control strategy, concerning the influence of grid voltage imbalance, three-phase active-load imbalance, and high-order harmonic distortion, is proposed in this paper to [...] Read more.
For the high-voltage AC port of power electronic transformer (HVAC-PET) with three-phase independent DC buses on the low-voltage side, a decoupling control strategy, concerning the influence of grid voltage imbalance, three-phase active-load imbalance, and high-order harmonic distortion, is proposed in this paper to simultaneously realize the functions of active power control, reactive power compensation, and active power filtering. In the outer power control loop, according to the distribution rule of decoupled average active power components in three phases, stability control for the sum of cluster average active power flows is realized by injecting positive-sequence active current, so as to control the average cluster voltage (i.e., the average of all the DC-link capacitor voltages), and by injecting negative-sequence current, the cluster average active power flows can be controlled individually to balance the three cluster voltages (i.e., the average of the DC-link capacitor voltages in each cluster). The negative-sequence reactive power component is considered to realize the reactive power compensation. In the inner current control loop, the fundamental and high-order harmonic components are uniformly controlled in the positive-sequence dq frame using the PI + VPIs (vector proportional integral) controller, and the harmonic filtering function is realized while the fundamental positive-sequence current is adjusted. Experiments performed on the 380 V/50 kVA laboratory HVAC-PET verify the effectiveness of the proposed control strategy. Full article
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21 pages, 3802 KiB  
Article
Parameter Identification and Speed Control of a Small-Scale BLDC Motor: Experimental Validation and Real-Time PI Control with Low-Pass Filtering
by Ayman Ibrahim Abouseda, Resat Ozgur Doruk and Ali Amini
Machines 2025, 13(8), 656; https://doi.org/10.3390/machines13080656 - 27 Jul 2025
Viewed by 376
Abstract
This paper presents a structured and experimentally validated approach to the parameter identification, modeling, and real-time speed control of a brushless DC (BLDC) motor. Electrical parameters, including resistance and inductance, were measured through DC and AC testing under controlled conditions, respectively, while mechanical [...] Read more.
This paper presents a structured and experimentally validated approach to the parameter identification, modeling, and real-time speed control of a brushless DC (BLDC) motor. Electrical parameters, including resistance and inductance, were measured through DC and AC testing under controlled conditions, respectively, while mechanical and electromagnetic parameters such as the back electromotive force (EMF) constant and rotor inertia were determined experimentally using an AVL dynamometer. The back EMF was obtained by operating the motor as a generator under varying speeds, and inertia was identified using a deceleration method based on the relationship between angular acceleration and torque. The identified parameters were used to construct a transfer function model of the motor, which was implemented in MATLAB/Simulink R2024b and validated against real-time experimental data using sinusoidal and exponential input signals. The comparison between simulated and measured speed responses showed strong agreement, confirming the accuracy of the model. A proportional–integral (PI) controller was developed and implemented for speed regulation, using a low-cost National Instruments (NI) USB-6009 data acquisition (DAQ) and a Kelly controller. A first-order low-pass filter was integrated into the control loop to suppress high-frequency disturbances and improve transient performance. Experimental tests using a stepwise reference speed profile demonstrated accurate tracking, minimal overshoot, and robust operation. Although the modeling and control techniques applied are well known, the novelty of this work lies in its integration of experimental parameter identification, real-time validation, and practical hardware implementation within a unified and replicable framework. This approach provides a solid foundation for further studies involving more advanced or adaptive control strategies for BLDC motors. Full article
(This article belongs to the Section Electrical Machines and Drives)
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19 pages, 3865 KiB  
Article
The Voltage Regulation of Boost Converters via a Hybrid DQN-PI Control Strategy Under Large-Signal Disturbances
by Pengqiang Nie, Yanxia Wu, Zhenlin Wang, Song Xu, Seiji Hashimoto and Takahiro Kawaguchi
Processes 2025, 13(7), 2229; https://doi.org/10.3390/pr13072229 - 12 Jul 2025
Viewed by 351
Abstract
The DC-DC boost converter plays a crucial role in interfacing low-voltage sources with high-voltage DC buses in DC microgrid systems. To enhance the dynamic response and robustness of the system under large-signal disturbances and time-varying system parameters, this paper proposes a hybrid control [...] Read more.
The DC-DC boost converter plays a crucial role in interfacing low-voltage sources with high-voltage DC buses in DC microgrid systems. To enhance the dynamic response and robustness of the system under large-signal disturbances and time-varying system parameters, this paper proposes a hybrid control strategy that integrates proportional–integral (PI) control with a deep Q-network (DQN). The proposed framework leverages the advantages of PI control in terms of steady-state regulation and a fast transient response, while also exploiting the capabilities of the DQN agent to learn optimal control policies in dynamic and uncertain environments. To validate the effectiveness and robustness of the proposed hybrid control framework, a detailed boost converter model was developed in the MATLAB 2024/Simulink environment. The simulation results demonstrate that the proposed framework exhibits a significantly faster transient response and enhanced robustness against nonlinear disturbances compared to the conventional PI and fuzzy controllers. Moreover, by incorporating PI-based fine-tuning in the steady-state phase, the framework effectively compensates for the control precision limitations caused by the discrete action space of the DQN algorithm, thereby achieving high-accuracy voltage regulation without relying on an explicit system model. Full article
(This article belongs to the Special Issue Challenges and Advances of Process Control Systems)
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25 pages, 7875 KiB  
Article
A Comparative Study of Direct Power Control Strategies for STATCOM Using Three-Level and Five-Level Diode-Clamped Inverters
by Diyaa Mustaf Mohammed, Raaed Faleh Hassan, Naseer M. Yasin, Mohammed Alruwaili and Moustafa Ahmed Ibrahim
Energies 2025, 18(13), 3582; https://doi.org/10.3390/en18133582 - 7 Jul 2025
Viewed by 382
Abstract
For power electronic interfaces, Direct Power Control (DPC) has emerged as a leading control technique, especially in applications such as synchronous motors, induction motors, and other electric drives; renewable energy sources (such as photovoltaic inverters and wind turbines); and converters that are grid-connected, [...] Read more.
For power electronic interfaces, Direct Power Control (DPC) has emerged as a leading control technique, especially in applications such as synchronous motors, induction motors, and other electric drives; renewable energy sources (such as photovoltaic inverters and wind turbines); and converters that are grid-connected, such as Virtual Synchronous Generator (VSG) and Static Compensator (STATCOM) configurations. DPC accomplishes several significant goals by avoiding the inner current control loops and doing away with coordinating transformations. The application of STATCOM based on three- and five-level diode-clamped inverters is covered in this work. The study checks the abilities of DPC during power control adjustments during diverse grid operation scenarios while detailing how multilevel inverters affect system stability and power reliability. Proportional Integral (PI) controllers are used to control active and reactive power levels as part of the control approach. This study shows that combining DPC with Sinusoidal Pulse Width Modulation (SPWM) increases the system’s overall electromagnetic performance and control accuracy. The performance of STATCOM systems in power distribution and transient response under realistic operating conditions is assessed using simulation tools applied to three-level and five-level inverter topologies. In addition to providing improved voltage quality and accurate reactive power control, the five-level inverter structure surpasses other topologies by maintaining a total harmonic distortion (THD) below 5%, according to the main findings. The three-level inverter operates efficiently under typical grid conditions because of its straightforward design, which uses less processing power and computational complexity. Full article
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19 pages, 4714 KiB  
Article
Robust Model-Free Control for MMC Inverters in Cold Ironing Systems
by Cheikh Abdel Kader, Nadia Aït-Ahmed, Azeddine Houari, Mourad Aït-Ahmed, Gang Yao and Menny El-Bah
Appl. Sci. 2025, 15(13), 7343; https://doi.org/10.3390/app15137343 - 30 Jun 2025
Viewed by 245
Abstract
Power quality is a key issue in cold ironing (CI) systems, where a stable, clean power supply is essential to meet the needs of moored vessels. According to IEC/ISO/IEEE 80005-1, these systems must deliver high power at standardized voltages (6.6 kV or 11 [...] Read more.
Power quality is a key issue in cold ironing (CI) systems, where a stable, clean power supply is essential to meet the needs of moored vessels. According to IEC/ISO/IEEE 80005-1, these systems must deliver high power at standardized voltages (6.6 kV or 11 kV) with minimal harmonic distortion in the presence of vessel load variability. This study proposes a model-free control strategy based on an intelligent proportional–integral (iPI) corrector with adaptive gain, applied to a three-phase modular multilevel converter (MMC) equipped with an LC filter. This architecture, adapted to distributed infrastructures, reduces the number of transformers required while guaranteeing high output voltages. The iPI strategy improves system robustness, dynamically compensates for disturbances, and ensures better power quality. A comparative analysis of three control strategies, proportional–integral (PI), intelligent proportional–integral (iPI), and intelligent proportional–integral adaptive (iPIa), performed in MATLAB/Simulink and complemented by experimental tests on the OPAL-RT platform, revealed a significant THD reduction of 1.18%, in accordance with the IEC/ISO/IEEE 80005-1 standard. These results confirm the effectiveness of the proposed method in meeting the requirements of CI systems. Full article
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40 pages, 4643 KiB  
Article
An Innovative LFC System Using a Fuzzy FOPID-Enhanced via PI Controller Tuned by the Catch Fish Optimization Algorithm Under Nonlinear Conditions
by Saleh Almutairi, Fatih Anayi, Michael Packianather and Mokhtar Shouran
Sustainability 2025, 17(13), 5966; https://doi.org/10.3390/su17135966 - 28 Jun 2025
Viewed by 437
Abstract
Load frequency control (LFC) remains a critical challenge in ensuring the stability of modern power grids. The integration of nonlinear dynamics into LFC design is paramount to achieving robust performance, which directly underpins grid reliability. This study introduces a novel hybrid control strategy—a [...] Read more.
Load frequency control (LFC) remains a critical challenge in ensuring the stability of modern power grids. The integration of nonlinear dynamics into LFC design is paramount to achieving robust performance, which directly underpins grid reliability. This study introduces a novel hybrid control strategy—a fuzzy fractional-order proportional–integral–derivative (Fuzzy FOPID) controller augmented with a proportional–integral (PI) compensator—for LFC applications in two distinct dual-area interconnected power systems. To optimize the controller’s parameters, the recently developed Catch Fish Optimization Algorithm (CFOA) is employed, leveraging the Integral Time Absolute Error (ITAE) as the primary cost function for precision tuning. A comprehensive comparative analysis is conducted to benchmark the proposed controller against the existing methodologies documented in the literature. Nonlinear elements’ impact on the system stability is also investigated. The investigation evaluates the impact of critical nonlinearities, including governor dead band (GDB) and generation rate constraints (GRCs), on system performance. The simulation results demonstrate that the CFOA-tuned Fuzzy FOPID + PI controller exhibits superior robustness and dynamic response compared to conventional approaches, effectively mitigating frequency deviations and maintaining grid stability under nonlinear operating conditions. Furthermore, the CFOA demonstrates marginally superior convergence and tuning accuracy relative to the widely adopted Particle Swarm Optimization (PSO) algorithm. These findings underscore the proposed controller’s potential as a high-performance solution for real-world LFC systems, particularly in scenarios characterized by nonlinearities and interconnected grid complexities. This study advances the field by bridging the gap between fractional-order fuzzy control theory and practical power system applications, offering a validated strategy for enhancing grid resilience in dynamic environments. Full article
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25 pages, 4087 KiB  
Article
Symmetry-Inspired Friction Compensation and GPI Observer-Based Nonlinear Predictive Control for Enhanced Speed Regulation in IPMSM Servo Systems
by Chao Wu, Xiaohong Wang, Yao Ren and Yuying Zhou
Symmetry 2025, 17(7), 1012; https://doi.org/10.3390/sym17071012 - 27 Jun 2025
Cited by 1 | Viewed by 270
Abstract
In integrated permanent magnet synchronous motors (IPMSMs) coupled with mechanical devices such as ball screws and reducers, complex nonlinear friction characteristics often arise, leading to asymmetrical distortions such as position “flat-top” and speed “ramp-up”. These phenomena significantly degrade the system’s positioning accuracy. To [...] Read more.
In integrated permanent magnet synchronous motors (IPMSMs) coupled with mechanical devices such as ball screws and reducers, complex nonlinear friction characteristics often arise, leading to asymmetrical distortions such as position “flat-top” and speed “ramp-up”. These phenomena significantly degrade the system’s positioning accuracy. To address this issue, this paper introduces a symmetry-inspired nonlinear predictive speed control approach based on the Stribeck piecewise linearized friction compensation and a generalized proportional integral (GPI) observer. The proposed method leverages the inherent symmetry in the Stribeck friction model to describe the nonlinear behavior, employing online piecewise linearization via the least squares method. A GPI observer was designed to estimate the lumped disturbance, including time-varying components in the speed dynamics, friction model deviations, and external loads. By incorporating these estimates, a nonlinear predictive controller was developed, employing a quadratic cost function to derive the optimal control law. The experimental results demonstrate that, compared to traditional integral NPC and PI controllers, the proposed method effectively restores system symmetry by eliminating the “flat-top” and “ramp-up” distortions while maintaining computational efficiency. Full article
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32 pages, 8258 KiB  
Article
Optimal Incremental Conductance-Based MPPT Control Methodology for a 100 KW Grid-Connected PV System Employing the RUNge Kutta Optimizer
by Kareem M. AboRas, Abdullah Hameed Alhazmi and Ashraf Ibrahim Megahed
Sustainability 2025, 17(13), 5841; https://doi.org/10.3390/su17135841 - 25 Jun 2025
Viewed by 481
Abstract
Solar energy is a promising and sustainable green energy source, showing significant advancements in photovoltaic (PV) system deployment. To maximize PV efficiency, robust maximum power point tracking (MPPT) methods are essential, as the maximum power point (MPP) shifts with changing irradiance and temperature. [...] Read more.
Solar energy is a promising and sustainable green energy source, showing significant advancements in photovoltaic (PV) system deployment. To maximize PV efficiency, robust maximum power point tracking (MPPT) methods are essential, as the maximum power point (MPP) shifts with changing irradiance and temperature. This paper proposes a novel MPPT control strategy for a 100 kW grid-connected PV system, based on the incremental conductance (IC) method and enhanced by a cascaded Fractional Order Proportional–Integral (FOPI) and conventional Proportional–Integral (PI) controller. The controller parameters are optimally tuned using the recently introduced RUNge Kutta optimizer (RUN). MATLAB/Simulink simulations have been conducted on the 100 kW benchmark PV model integrated into a medium-voltage grid, with the objective of minimizing the integral square error (ISE) to improve efficiency. The performance of the proposed IC-MPPT-(FOPI-PI) controller has been benchmarked against standalone PI and FOPI controllers, and the RUN optimizer is here compared with recent metaheuristic algorithms, including the Gorilla Troops Optimizer (GTO) and the African Vultures Optimizer (AVO). The evaluation covers five different environmental scenarios, including step, ramp, and realistic irradiance and temperature profiles. The RUN-optimized controller achieved exceptional performance with 99.984% tracking efficiency, sub-millisecond rise time (0.0012 s), rapid settling (0.015 s), and minimal error (ISE: 16.781), demonstrating outstanding accuracy, speed, and robustness. Full article
(This article belongs to the Special Issue Sustainable Electrical Engineering and PV Microgrids)
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36 pages, 6279 KiB  
Article
Eel and Grouper Optimization-Based Fuzzy FOPI-TIDμ-PIDA Controller for Frequency Management of Smart Microgrids Under the Impact of Communication Delays and Cyberattacks
by Kareem M. AboRas, Mohammed Hamdan Alshehri and Ashraf Ibrahim Megahed
Mathematics 2025, 13(13), 2040; https://doi.org/10.3390/math13132040 - 20 Jun 2025
Cited by 1 | Viewed by 488
Abstract
In a smart microgrid (SMG) system that deals with unpredictable loads and incorporates fluctuating solar and wind energy, it is crucial to have an efficient method for controlling frequency in order to balance the power between generation and load. In the last decade, [...] Read more.
In a smart microgrid (SMG) system that deals with unpredictable loads and incorporates fluctuating solar and wind energy, it is crucial to have an efficient method for controlling frequency in order to balance the power between generation and load. In the last decade, cyberattacks have become a growing menace, and SMG systems are commonly targeted by such attacks. This study proposes a framework for the frequency management of an SMG system using an innovative combination of a smart controller (i.e., the Fuzzy Logic Controller (FLC)) with three conventional cascaded controllers, including Fractional-Order PI (FOPI), Tilt Integral Fractional Derivative (TIDμ), and Proportional Integral Derivative Acceleration (PIDA). The recently released Eel and Grouper Optimization (EGO) algorithm is used to fine-tune the parameters of the proposed controller. This algorithm was inspired by how eels and groupers work together and find food in marine ecosystems. The Integral Time Squared Error (ITSE) of the frequency fluctuation (ΔF) around the nominal value is used as an objective function for the optimization process. A diesel engine generator (DEG), renewable sources such as wind turbine generators (WTGs), solar photovoltaics (PVs), and storage components such as flywheel energy storage systems (FESSs) and battery energy storage systems (BESSs) are all included in the SMG system. Additionally, electric vehicles (EVs) are also installed. In the beginning, the supremacy of the adopted EGO over the Gradient-Based Optimizer (GBO) and the Smell Agent Optimizer (SAO) can be witnessed by taking into consideration the optimization process of the recommended regulator’s parameters, in addition to the optimum design of the membership functions of the fuzzy logic controller by each of these distinct algorithms. The subsequent phase showcases the superiority of the proposed EGO-based FFOPI-TIDμ-PIDA structure compared to EGO-based conventional structures like PID and EGO-based intelligent structures such as Fuzzy PID (FPID) and Fuzzy PD-(1 + PI) (FPD-(1 + PI)); this is across diverse symmetry operating conditions and in the presence of various cyberattacks that result in a denial of service (DoS) and signal transmission delays. Based on the simulation results from the MATLAB/Simulink R2024b environment, the presented control methodology improves the dynamics of the SMG system by about 99.6% when compared to the other three control methodologies. The fitness function dropped to 0.00069 for the FFOPI-TIDμ-PIDA controller, which is about 200 times lower than the other controllers that were compared. Full article
(This article belongs to the Special Issue Mathematical Methods Applied in Power Systems, 2nd Edition)
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14 pages, 2404 KiB  
Article
The Development of a 1 kW Mid-Range Wireless Power Transfer Platform for Autonomous Guided Vehicle Applications Using an LCC-S Resonant Compensator
by Worapong Pairindra, Suwaphit Phongsawat, Teeraphon Phophongviwat and Surin Khomfoi
World Electr. Veh. J. 2025, 16(6), 322; https://doi.org/10.3390/wevj16060322 - 9 Jun 2025
Cited by 1 | Viewed by 694
Abstract
This study presents the development, simulation, and hardware implementation of a 48 V, 1 kW mid-range wireless power transfer (WPT) platform for autonomous guided vehicle (AGV) charging in industrial applications. The system uses an LCC-S compensation topology, selected for its ability to maintain [...] Read more.
This study presents the development, simulation, and hardware implementation of a 48 V, 1 kW mid-range wireless power transfer (WPT) platform for autonomous guided vehicle (AGV) charging in industrial applications. The system uses an LCC-S compensation topology, selected for its ability to maintain a constant output voltage and deliver high efficiency even under load variations at a typical coil distance of 15 cm. It can also operate at different distances by adjusting the compensator circuit. A proportional–integral (PI) controller is implemented for current regulation, offering a practical, low-cost solution well suited to industrial embedded systems. Compared to advanced control strategies, the PI controller provides sufficient accuracy with minimal computational demand, enabling reliable operation in real-world environments. Current adjustment can be dynamically carried out in response to real-time changes and continuously monitored based on the AGV battery’s state of charge (SOC). Simulation and experimental results validate the system’s performance, achieving over 80% efficiency and demonstrating its feasibility for scalable, robust AGV charging in Industry 4.0 Manufacturing Settings. Full article
(This article belongs to the Special Issue Wireless Power Transfer Technology for Electric Vehicles)
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22 pages, 4847 KiB  
Article
Design and Implementation of a Comparative Study of Fractional-Order Fuzzy Logic and Conventional PI Controller for Optimizing Stand-Alone DFIG Performance in Wind Energy Systems
by Fella Boucetta, Mohamed Toufik Benchouia, Amel Benmouna, Mohamed Chebani, Amar Golea, Mohamed Becherif and Mohammed Saci Chabani
Sci 2025, 7(2), 80; https://doi.org/10.3390/sci7020080 - 5 Jun 2025
Viewed by 599
Abstract
This paper introduces a novel fractional-order fuzzy logic controller (FOFLC) designed for stator voltage control in standalone doubly fed induction generator (DFIG) systems used in wind energy applications. Unlike traditional fuzzy logic controllers (FLCs), which are limited by integer-order dynamics, the FOFLC leverages [...] Read more.
This paper introduces a novel fractional-order fuzzy logic controller (FOFLC) designed for stator voltage control in standalone doubly fed induction generator (DFIG) systems used in wind energy applications. Unlike traditional fuzzy logic controllers (FLCs), which are limited by integer-order dynamics, the FOFLC leverages the advanced principles of fractional-order (FO) calculus. By integrating fuzzy logic with fractional-order operators, the FOFLC enhances system precision, adaptability, and interpretability while addressing the inherent limitations of conventional proportional-integral (PI) controllers and integer-order FLCs. A key innovation of the FOFLC is its dual-mode architecture, enabling it to operate seamlessly as either a traditional FLC or a fractional-order FOFLC controller. This versatility allows for independent tuning of fractional parameters, optimizing the system’s response to transients, steady-state errors, and disturbances. The controller’s flexibility makes it particularly well-suited for nonlinear and dynamically complex stand-alone renewable energy systems. The FOFLC is experimentally validated on a 3-kW DFIG test bench using the dSPACE-1104 platform under various operating conditions. Compared to a conventional PI controller, the FOFLC demonstrated superior performance, achieving 80% reduction in response time, eliminating voltage overshoot and undershoot, reducing stator power and torque ripples by over 46%, and decreasing total harmonic distortion (THD) of both stator voltage and current by more than 50%. These results confirm the FOFLC’s potential as a robust and adaptive control solution for stand-alone renewable energy systems, ensuring high-quality power output and reliable operation. Full article
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19 pages, 5820 KiB  
Article
Angle-Based RGN-Enhanced ADRC for PMSM Compressor Speed Regulation Considering Aperiodic and Periodic Disturbances
by Chenchen Zhang, Yang Yang, Yimin Gong, Yibo Guo, Hongda Song and Jiannan Zhang
Actuators 2025, 14(6), 276; https://doi.org/10.3390/act14060276 - 4 Jun 2025
Viewed by 873
Abstract
Achieving excellent speed control in permanent magnet synchronous motors (PMSMs) relies on the simultaneous suppression of both aperiodic and periodic disturbances. This paper presents an enhanced Active Disturbance Rejection Control (ADRC) strategy specifically designed to address these disturbances in single-rotor compressors (SRCs). To [...] Read more.
Achieving excellent speed control in permanent magnet synchronous motors (PMSMs) relies on the simultaneous suppression of both aperiodic and periodic disturbances. This paper presents an enhanced Active Disturbance Rejection Control (ADRC) strategy specifically designed to address these disturbances in single-rotor compressors (SRCs). To achieve simultaneous suppression, a Recursive Gauss–Newton (RGN) algorithm is implemented in parallel with the conventional extended state observer (ESO) to enhance the ADRC framework. The RGN algorithm iteratively estimates the amplitude and phase information of periodic disturbances, while the ESO primarily observes the system’s aperiodic disturbances. In contrast to existing methods, the proposed angle-based approach demonstrates superior performance during speed transients. Detailed convergence and decoupling analyses are provided to facilitate parameter tuning. The effectiveness of the proposed method is validated through simulations and experiments conducted on a 650 W SRC, demonstrating its superiority over proportional–integral (PI) control, conventional ADRC, and quasi-resonant controller-based ADRC (QRC-ADRC) under both steady-state and dynamic conditions. Full article
(This article belongs to the Section Control Systems)
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19 pages, 5332 KiB  
Article
Adaptive Control Strategy for the PI Parameters of Modular Multilevel Converters Based on Dual-Agent Deep Reinforcement Learning
by Jiale Liu, Weide Guan, Yongshuai Lu and Yang Zhou
Electronics 2025, 14(11), 2270; https://doi.org/10.3390/electronics14112270 - 31 May 2025
Viewed by 473
Abstract
As renewable energy sources are integrated into power grids on a large scale, modular multilevel converter-high voltage direct current (MMC-HVDC) systems face two significant challenges: traditional PI (proportional integral) controllers have limited dynamic regulation capabilities due to their fixed parameters, while improved PI [...] Read more.
As renewable energy sources are integrated into power grids on a large scale, modular multilevel converter-high voltage direct current (MMC-HVDC) systems face two significant challenges: traditional PI (proportional integral) controllers have limited dynamic regulation capabilities due to their fixed parameters, while improved PI controllers encounter implementation difficulties stemming from the complexity of their control strategies. This article proposes a dual-agent adaptive control framework based on the twin delayed deep deterministic policy gradient (TD3) algorithm. This framework facilitates the dynamic adjustment of PI parameters for both voltage and current dual-loop control and capacitor voltage balancing, utilizing a collaboratively optimized agent architecture without reliance on complex control logic or precise mathematical models. Simulation results demonstrate that, compared with fixed-parameter PI controllers, the proposed method significantly reduces DC voltage regulation time while achieving precise dynamic balance control of capacitor voltage and effective suppression of circulating current, thereby notably enhancing system stability and dynamic response characteristics. This approach offers new solutions for dynamic optimization control in MMC-HVDC systems. Full article
(This article belongs to the Section Power Electronics)
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30 pages, 6372 KiB  
Article
Integrating Metabolomics and Gut Microbiota to Identify Key Biomarkers and Regulatory Pathways Underlying Metabolic Heterogeneity in Childhood Obesity
by Zhiwei Xia, Yan Li, Jiyong Yin, Zhaolong Gong, Jing Sun, Shi Shen, Yi Yang, Tingting Liu, Liyuan Wang and Junsheng Huo
Nutrients 2025, 17(11), 1876; https://doi.org/10.3390/nu17111876 - 30 May 2025
Viewed by 780
Abstract
Background/Objectives: Individuals with childhood obesity exhibit significant metabolic heterogeneity, necessitating precise biomarkers for risk stratification and assessment. This multi-omics investigation characterizes metabolic and microbial signatures underlying divergent metabolic phenotypes in the context of pediatric obesity. Methods: We analyzed 285 Chinese children (5–7 years) [...] Read more.
Background/Objectives: Individuals with childhood obesity exhibit significant metabolic heterogeneity, necessitating precise biomarkers for risk stratification and assessment. This multi-omics investigation characterizes metabolic and microbial signatures underlying divergent metabolic phenotypes in the context of pediatric obesity. Methods: We analyzed 285 Chinese children (5–7 years) stratified into five groups: wasting (WAS, n = 55), metabolically healthy/unhealthy and normal weight (MHWH, n = 54; MUWH, n = 67), and metabolically healthy/unhealthy obesity (MHO, n = 36; MUO, n = 73). Untargeted metabolomics (Orbitrap ID-X Tribrid™) and 16S rRNA sequencing were integrated with multivariate analyses (OPLS-DA with VIP > 1, FDR < 0.05; Maaslin 2 with TSS normalization and BH correction, FDR < 0.10). Results: Analysis identified 225 differential metabolites and 12 bacterial genera. The proportion of steroids and their derivatives among differential metabolites in the MUO/MHO group was significantly lower than that in the OVOB/NOR and OVOB/WAS groups (2.12% vs. 7.9–14.1%). MUO displayed elevated C17 sphinganine and LysoPC (O-18:0) levels but reduced PI (16:0/14:1) levels. In contrast, OVOB showed upregulated glycerol phospholipids (LPCs and PSs) and downregulated PE species (e.g., PE(16:0/16:0)) as well as gut microbiota dysbiosis characterized by a higher Firmicutes/Bacteroidetes (F/B) ratio (2.07 vs. 1.24 in controls, p = 0.009) and reduced α diversity (Ace index, Chao1 index, and Shannon index values were lower in the OVOB group, Shannon index: 2.96 vs. 3.45, p = 0.03). SCFA-producing genera were negatively correlated with the OVOB group, while positively associated with PE(16:0/16:0). Internal validation showed differential metabolites had potential predictive efficacy for MUO/MHO (AUC = 0.967) and OVOB/NOR (AUC = 0.888). Conclusions: We identified distinct lipid disruptions characterizing obesity subtypes, including steroid/terpene deficits and sphingolipid/ether lipid dysregulation in the MUO/MHO groups as well as phospholipid imbalance (↑LPC/PS↓PE) in the OVOB/NOR groups. The gut microbiota exhibited a profile characterized by low diversity, an increased F/B ratio, and a reduced abundance of SCFA-producing genera. These findings suggest potential biomarkers for childhood obesity stratification, though further validation is warranted. Full article
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