Search Results (852)

In order to improve power quality in low-voltage distribution networks with nonlinear, distorted, and unbalanced loads, this research suggests an innovative design for Shunt Active Power Filters (SAPFs). Compared to conventional single-inverter SAPF structures, the improved method uses a combination of two T-Type three-level inverters operating in a parallel configuration to improve compensator performance, leading to a higher current-carrying capacity as well as better harmonic reduction and system scalability. The Synchronous Reference Frame (SRF) algorithm is used to extract reference currents in order to achieve the required accuracy of harmonic cancellation. In order to ensure both a quick response and a suitable switch state selection for compensatory current references, the Model Predictive Current Control (MPCC) technique is employed. To maintain the DC-link voltage at a steady level and guarantee its correct operation under rapidly fluctuating loading conditions, a Proportional Integral (PI) controller-based DC–DC converter is also utilized. Four real-time operational circumstances are used to verify the performance of the proposed method using MATLAB/Simulink R2023a (i) SAPF activation under nonlinear loading conditions, (ii) dynamic load variation, (iii) distorted and unbalanced operation, and (iv) grid voltage disturbances including sag and swell conditions. The simulation study’s results show that, in all of the previously indicated scenarios, the source current Total Harmonic Distortion (THD) is reduced and an almost unity power factor is maintained while maintaining a constant DC-link voltage. Furthermore, the obtained performance meets IEEE-519-2022 requirements, demonstrating the feasibility of the suggested SAPF with two inverters under high-load circumstances. Full article

Nonlinear and asymmetrical loads in microgrids cause severe current distortion and phase unbalance, presenting critical challenges for grid-connected energy storage inverters. Conventional dq-frame Linear Active Disturbance Rejection Control (LADRC) suffers from two key limitations: bandwidth coupling between the observer and controller, and insufficient AC disturbance attenuation. To address these issues, an improved Quasi-Proportional Resonant-LADRC (QPR-LADRC) strategy is proposed for the inner current loop. First, by reconfiguring the disturbance feedback structure within the Linear Extended State Observer (LESO), observer bandwidth is decoupled from controller bandwidth, eliminating parameter tuning conflicts. Second, parallel QPR units tuned at 2ω₀ and 6ω₀ are integrated into the LESO state-error feedback loop to construct a QPR-LESO. This provides high selective gain for dq-frame AC ripples, enabling zero steady-state error tracking for negative-sequence components and dominant 5th/7th harmonics. Validated via MATLAB/Simulink and StarSim Hardware-in-the-Loop (HIL) testing, the proposed strategy restricts grid-connected current Total Harmonic Distortion (THD) below 2.7% (2.61%, 2.54%, and 2.63% for phases A, B, and C) under severe nonlinear and unbalanced loads. Compared with conventional PI and conventional LADRC schemes, it achieves maximum THD reductions of 1.68% and 3.43%, respectively, while maintaining three-phase current unbalance below 1.1%. Full article

The rapid advancement of power electronic devices has accelerated the widespread adoption of Line-Commutated Converter (LCC) and Voltage Source Converter (VSC) technologies as leading solutions for high-voltage industrial applications. These technologies have become fundamental components of modern high-voltage direct current (HVDC) transmission systems and advanced Industrial Machine-Drive (IMD) systems. This review presents a comprehensive comparison between conventional LCC technology and the more recent VSC technology, highlighting the operational advantages, limitations, and application suitability of each approach. In addition, it provides an in-depth examination of hierarchical control architectures employed in both LCC-HVDC and VSC-HVDC systems. As these systems are increasingly required to operate closer to their performance limits, the implementation of robust and efficient control strategies has become essential for ensuring stability, reliability, and optimal performance. Consequently, a wide range of control techniques has been developed to address the inherent nonlinearities and parameter uncertainties present in power systems. This review evaluates and compares both conventional and advanced control methods, including Proportional–Integral (PI) control, Variable Coefficient PI (V-PI), Fuzzy PI, Self-Tuning Fuzzy PI (STF-PI), Fractional Order PI (FOPI), Variable Coefficient Fractional Order PI (V-FOPI), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Model Predictive Control (MPC). Their performance is assessed across a range of operating conditions, with emphasis on dynamic response, robustness, and overall control effectiveness. Full article

In battery energy storage systems (BESSs) in microgrids, traditional proportional–integral (PI) controllers remain a popular choice for management. The PI-based system often fails during sharp transients and under sudden shifts in solar irradiance or load demands. PI-based systems typically exhibit sluggish recovery times and pronounced voltage overshoots. To overcome these limitations, this article develops a flatness-based control (FBC) framework designed to optimize the dynamic response and stability of an islanded hybrid microgrid powered by photovoltaic (PV) arrays and wind turbines. The core mechanism directly regulates the battery charging and discharging currents. This mechanism ensures that the DC-bus voltage strictly tracks its reference command regardless of fluctuations in load or weather profiles. Crucially, the structural resilience of this control architecture was rigorously assessed, with the simulation model subjected to severe 20% mismatches in physical parameters, specifically the main DC-bus capacitance and battery inductance, alongside continuous high-frequency Gaussian white noise injected into the measurement feedback channels. Three scenarios have been implemented in MATLAB/Simulink: variable weather conditions, realistic weather conditions, and parameter uncertainties with measurement noise. The comparison shows that the new FBC controller cuts the settling time down from 0.47 s with the regular PI controller to just 0.02 s, which is a 95.7% decrease. In addition, the proposed controller substantially mitigates transient voltage deviations and eliminates the 2.6% voltage overshoot observed with the PI controller. The rise time is also reduced by approximately 35%. These results demonstrate that the proposed FBC provides faster, overshoot-free, and more stable DC-bus voltage regulation under the investigated operating conditions. Full article

Conventional proportional integral (PI) current controllers used in field-oriented control (FOC) have an acceptable performance under linear motor models, but their performance degrades when nonlinear machine characteristics are taken into account. This paper presents a comparative evaluation of PI and twin delayed deep deterministic policy gradient (TD3)-based current control for a permanent magnet synchronous motor (PMSM) drive using a high-fidelity nonlinear motor model. The first step involves modeling and validating a PMSM in JMAG using finite element analysis, assessing its electromagnetic properties. Subsequently, JMAG-RT is utilized to generate nonlinear flux, torque, and inductance maps, which are implemented as lookup tables (LUTs) in MATLAB/Simulink (R2026a) to accurately capture the motor’s nonlinear behavior. Then, the performance of both the PI and TD3 controllers is tested with both lumped-parameter and nonlinear motor models. The results show that the PI controller has strong speed oscillation and current fluctuation under nonlinear operating conditions, but the TD3-based controller has better tracking accuracy and current regulation performance. In terms of integral absolute error (IAE), compared to PI-based nonlinear motor parameters, the TD3 control framework provides a 17.6% reduction in speed IAE, and a minor 0.4% decrease in q-axis current IAE, resulting in accurate reference tracking under nonlinear operating conditions. These findings demonstrate the effectiveness of reinforcement learning for enhancing the performance of PMSM drives when high-fidelity nonlinear motor models are considered. Full article

48 V bus modules must stay regulated under wide input-voltage swings and fast load transients. A proportional-integral (PI) controller and a fuzzy logic controller (FLC) are tuned by the same Particle Swarm Optimization (PSO), a swarm metaheuristic modeled on bird-flock foraging, under an Integral of Time-weighted Absolute Error (ITAE) objective. The novelty is PSO-based membership-boundary optimization: the membership-function limits, analogues of biological decision thresholds, are shaped directly rather than only the scaling gains. A 153 W, 225 kHz synchronous boost converter is simulated with non-ideal parasitics. For input-voltage transitions the PSO-FLC shortens settling time by up to 70.5% (118 versus 400 ms), whereas the PSO-PI gives smaller peak deviation (26.2% versus 40.0%) and faster load-step recovery. Unconstrained, both controllers draw 58–62 A on the worst input-voltage step; a 10 A cycle-by-cycle limit removes these excursions. Under an identical budget and parameter count, boundary tuning attains 12.3% lower ITAE cost and roughly 40% shorter large-signal settling than scaling-gain tuning. Across ten runs the cost varies by under 1.5% of its mean, and the ranking holds at a realizable update rate (4.44 μs, zero-order hold) and under the current limit. The findings are simulation-only comparative design guidance. Full article

In photovoltaic-based hybrid energy storage systems (PV–HESS), rapid power transients accelerate battery degradation, directly reducing the operating lifetime and sustainability of renewable power resources. To address this issue, the current study proposes an optimal coordinated framework for the simultaneous and coordinated tuning of battery and supercapacitor current-loop proportional–integral (PI) controllers. The proposed framework treats the four PI gains of the battery and supercapacitor controllers as a unified optimization problem, applying five metaheuristic algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gazelle Optimization Algorithm (GOA), Artificial Protozoa Optimizer (APO), and White Shark Optimization (WSO). The optimization problem is directly coupled with a full nonlinear MATLAB 2022b/Simulink PV–HESS model, capturing the dynamic interactions among the PV array, bidirectional converters, DC-link capacitor, storage units, and load. A combined Integral of Time-weighted Absolute Error (ITAE) objective function is used to minimize current tracking errors, ensuring the supercapacitor absorbs fast power fluctuations while shielding the battery from high-frequency thermal and electrical stress. The controllers are evaluated across four operating scenarios involving steady irradiance shifts, rapid irradiance fluctuations, load disturbances, and a simultaneous irradiance drop from 1000 W/m² to 400 W/m² with a 33% load increase. The results confirm stable DC-link regulation and effective power sharing. Specifically, APO delivers superior performance in the high-stress scenario, GOA minimizes transient-error indices, and GA achieves the lowest DC-link voltage RMSE. These findings demonstrate that coordinated tuning effectively balances high-frequency dynamics, extending battery service life and enhancing the long-term operational sustainability of solar microgrid storage. Full article

This work presents the experimental validation of proportional–integral (PI) controller gains for a DC–DC boost converter obtained using two metaheuristic optimization algorithms: Particle Swarm Optimization (PSO) and Lungs Performance Optimization (LPO). The controller gains were previously determined through a simulation-based optimization procedure in which the proportional and integral gains were selected by minimizing a performance-based objective function incorporating settling time and overshoot. In the present study, the optimized gains are implemented in a physical boost converter prototype and experimentally evaluated under varying operating conditions. Their performance is compared with that of a conventional PI controller tuned using the Root Locus method. The experimental results demonstrate stable output-voltage regulation for all evaluated controllers. Under the investigated conditions, the controllers tuned using the metaheuristic approaches exhibit dynamic responses comparable to those obtained with the conventional tuning method. These results provide experimental evidence supporting the applicability of metaheuristic optimization for PI controller tuning in power electronic converters. Full article

This work presents a dual-core embedded architecture for inner–outer loop control of a brushed DC servomechanism based on a dsPIC33CH128MP202 digital signal controller. The secondary core executes the PWM-synchronized current loop at 20 kHz and computes online performance and timing statistics, while the main core handles the incremental encoder, host communication, and inter-core data exchange. Three control schemes are evaluated on the same plant over five runs: direct PWM, a floating-point proportional-integral (PI) current controller, and the same discrete PI law in Q15 fixed-point arithmetic. The maximum observed loop execution times were 9.17 µs, 31.74 µs, and 10.50 µs, respectively, within the 50 µs sampling period. Independent oscilloscope measurements produced 9.19, 30.45, and 10.50 µs. Q15 reduced the maximum execution time by 66.9% and increased the timing margin from 18.26 to 39.50 µs. The two PI implementations showed no observable difference in position response and nearly identical steady-state current-loop RMSE values. The measurements show that Q15 implementation recovers substantial current-loop timing margin without measurable degradation in closed-loop performance, while also confirming the expected reduction in computational cost of fixed-point arithmetic. Current-reference tests from ±0.25 to ±1.00 A and emulated additional inertias were added for both PI implementations to examine current-loop accuracy and load sensitivity. Full article

This study investigates how the insertion point and parameterization of reinforcement-learning (RL) compensation affect a low-voltage dual-active-bridge (DAB) converter operated with triple-phase-shift (TPS) modulation. A proportional–integral (PI)-TPS controller is used as the common baseline. Two deep deterministic policy gradient (DDPG) compensation structures are compared: one directly corrects the three TPS variables, whereas the other corrects the high-level phase-shift command before the deterministic TPS mapping. The controllers are evaluated at the rated point and under fixed and stepwise input-voltage and load variations. Direct three-variable correction provides no consistent improvement in the investigated simulations, while phase-shift-level compensation provides a more favorable voltage-current tradeoff and preserves TPS coordination. At the rated point, the phase-shift-level scheme reduces the steady-state voltage error by 29.34%, and the full-transient peak inductor current from 69.82 to 36.84 A. Variable-condition results represent robustness after retraining rather than zero-shot generalization. Because the two RL formulations also differ in observations and rewards, the comparison concerns the complete compensation structures rather than action-space dimension alone. Full article

The dq-axis coupling effect significantly influences the vector control performance of permanent magnet synchronous motors (PMSMs), and traditional proportional-integral (PI) control cannot control the system as desired. Extensive studies have addressed this coupling effect. However, traditional evaluation methods based on pole–zero distribution are not intuitive, which is unfavorable for engineers to select and apply decoupling strategies. This paper employed singular value analysis to assess coupling trends and magnitude–frequency characteristics of the system. In consideration of the effects of rotor speed, parameter estimation error, and digital delay, a comprehensive evaluation of controllers was conducted. This evaluation included feedforward, feedback, and internal model control (IMC) decoupling, with the analysis facilitated by singular value plots. Under the parameters chosen in this paper, the feedforward decoupling controller improves slightly in decoupling under time delay, but higher rotor speed degrades its performance more. The feedback decoupling controller worsens as the system’s overshoot rate surges from 19.4% to 33.4% under the effect of time delay, though it can decouple completely under ideal conditions. It is evident that the efficacy of all strategies is diminished in the presence of parameter estimation deviation. In contrast, the internal model decoupling controller shows better decoupling capability and stronger robustness, while its overshoot remains under 24%. The effectiveness of the proposed method was confirmed through a combination of simulations and experiments. Full article

Efficient maximum power extraction in variable-speed wind energy conversion systems (WECSs) remains challenging because of nonlinear turbine dynamics, continuously varying wind conditions, measurement disturbances, and mechanical-parameter uncertainties. This study presents a robust nonlinear integral backstepping control (BC) strategy for generator-speed regulation within a Tip-Speed Ratio (TSR)-based Maximum Power Point Tracking (MPPT) framework. The proposed controller combines nonlinear backstepping stabilization with integral compensation to improve reference tracking and reduce persistent tracking errors. The turbine-generator mechanical inertia is explicitly incorporated into the control formulation, providing a physically consistent representation of the mechanical dynamics and enabling systematic evaluation of parameter uncertainty. A comprehensive comparative assessment is conducted in MATLAB/Simulink using four control strategies: proportional-integral (PI), integral-proportional (IP), sliding-mode control (SMC), and the proposed integral BC. The controllers are evaluated under five complementary scenarios: variable wind speed, measurement noise, abrupt stepwise wind-speed variations, ±20% mechanical-inertia uncertainty, and a 10-ms rotor-speed measurement delay. Performance is assessed using the Integral of Squared Error (ISE), Integral of Absolute Error (IAE), and Integral of Time-weighted Absolute Error (ITAE), together with statistical measures across the five scenarios. Under the baseline variable-wind condition, BC achieves ISE = 24.4164, IAE = 1.539, and ITAE = 0.716, outperforming PI, IP, and SMC in all three indices. Under abrupt stepwise wind-speed variations, BC further achieves ISE = 0.00110, IAE = 0.0056, and ITAE = 0.0529, demonstrating rapid transient error suppression. The proposed controller remains stable under ±20% mechanical-inertia variations and a 10-ms measurement delay. Across the five scenarios, BC achieves the lowest mean ISE, IAE, and ITAE values of 19.353, 1.231, and 2.642, respectively, as well as the lowest standard deviations for ISE and IAE. SMC exhibits particularly consistent performance under measurement noise and the lowest standard deviation for ITAE. Overall, the results demonstrate that the proposed integral BC provides the most favorable balance of tracking accuracy, transient performance, and robustness among the investigated strategies. The improved rotor-speed regulation supports operation near the optimal TSR and effective aerodynamic power extraction. The findings highlight the potential of the proposed approach for robust MPPT control of variable-speed WECSs. Full article

This paper proposes a supervisory soft disagreement framework for adaptive uncertainty regulation in Interval Type-2 (IT2) fuzzy servo control and validates its performance through embedded implementation on an industrial servo platform. The proposed framework introduces a supervisory learning layer that combines supervised classification and unsupervised fuzzy clustering to characterize servo operating conditions using experimentally extracted performance indicators, including rise time, settling time, overshoot, steady-state error, Integral Absolute Error (IAE), control-effort energy, tracking-error standard deviation, and maximum control effort. Operating condition confidence is quantified by measuring the soft disagreement between the posterior class probabilities of a Support Vector Machine (SVM) classifier and the normalized membership degrees of a Fuzzy C-Means (FCM) clustering algorithm using the Bhattacharyya coefficient. The resulting disagreement index adaptively regulates the Footprint of Uncertainty (FOU) of the antecedent membership functions in IT2 fuzzy controller. A closed-form Uncertainty Avoider Defuzzification (UAD) strategy enables computationally efficient uncertainty-aware type reduction for real-time embedded implementation without iterative procedures. The framework was trained using experimental data collected from a Delta ASDA-B2 400 W industrial servo drive under diverse operating conditions. The complete controller was implemented on a Raspberry Pi and experimentally compared with conventional Proportional–Integral–Derivative (PID), Type-1, and fixed-FOU IT2 fuzzy controllers. Experimental results show that the proposed controller achieved an average IAE of 1.08, representing improvements of 55.6% and 27.5% over the PID and fixed-FOU IT2 controllers, respectively. Overshoot was reduced to 2.2% and settling time to 0.24 s, while the supervisory computation required only 4.55 ms, confirming real-time feasibility. The scientific significance of this work lies in introducing a new disagreement-driven supervisory paradigm that links probabilistic machine learning confidence with adaptive fuzzy uncertainty regulation. By establishing a principled connection among supervised learning, unsupervised learning, and Interval Type-2 fuzzy control, the proposed framework provides a general foundation for confidence-aware adaptive uncertainty management in intelligent control systems operating under uncertain and time-varying conditions. Full article

Oxidative damage is closely associated with the gradual decline in boar sperm quality during liquid storage at 17 °C. To determine whether L-histidine (L-His) could limit this deterioration, semen was diluted with an extender containing 0, 1, 10, 100, or 1000 μM L-His. Motility and acrosomal integrity were measured throughout storage. On day 7, mitochondrial membrane potential (MMP) and ATP content were determined together with intracellular probe oxidation, membrane lipid oxidation, and Annexin V-FITC/PI staining patterns. The stored sperm were subsequently exposed to capacitating conditions and tested for their ability to bind to oviductal explants. The most favorable responses were observed with 100 μM L-His. Compared with untreated semen, this group retained higher motility and acrosomal integrity and showed higher MMP and ATP content (p < 0.05). It also exhibited less intracellular probe oxidation and membrane lipid oxidation, together with lower proportions of Annexin V-positive and membrane-compromised sperm. Following capacitation induction, sperm stored with 100 μM L-His showed increased tyrosine phosphorylation and higher proportions of capacitated sperm. Their binding index to oviductal explants was also higher than that of the control group. These findings indicate that supplementation with 100 μM L-His can improve the preservation quality of boar sperm during extended liquid storage at 17 °C and maintain functional characteristics of stored sperm, providing new insights into the potential use of L-His as a semen extender supplement for prolonged storage. Full article

A unified power quality conditioner (UPQC) is developed for the mitigation of grid-voltage and -current distortions under steady-state and dynamic conditions. The proposed configuration integrates series and shunt inverters through a common DC-link capacitor. The capacitor functions as an energy buffer by accommodating the power exchanged between the two inverters. Following an abrupt grid-voltage disturbance or load transition, the DC-link capacitor must instantaneously deliver or absorb power to maintain the power balance between the inverters while sustaining the required compensation. The resulting transient energy exchange can produce pronounced DC-link voltage excursions, with adverse consequences for system stability and compensation accuracy. Rapid and accurate regulation of the DC-link voltage is therefore essential for maintaining the dynamic compensation performance of the UPQC. To improve this regulation, a compensatory wavelet fuzzy neural network incorporating asymmetric membership functions (CWFNN-AMFs) is introduced in place of the conventional proportional–integral (PI) controller. The network architecture and its online learning algorithm are derived in detail. Finally, experimental results under steady-state and dynamic conditions demonstrate that the CWFNN-AMF-controlled UPQC improves both power quality compensation and DC-link voltage regulation, thereby verifying the feasibility and effectiveness of the proposed control framework. Full article