Search Results (529)

This article presents a neural network-based control method for maintaining the required output voltage of a synchronous buck converter. The goal was to replace a traditional PID controller with a neural network that calculates the duty cycle based on real-time data. Several versions of the neural network were tested. The final version, which included the input voltage, reference, and output current as inputs and compensated for dead time, was successfully validated on real hardware. It was able to respond to changes in load and input voltage within a limited operating range. Full article

When the speed of an induction motor (IM) exceeds its rated value, voltage saturation occurs, which degrades its performance. Traditional flux-weakening (FW) control suffers from delays due to cascaded PI regulators and sensitivity to rotor field orientation lag. Addressing these two issues, the proposed ancillary flux-weakening (AFW) method introduces two d-axis current compensation paths. One compensation path is from the reference value of the q-axis current, which simplifies the traditional three-PI cascade FW path into a single PI path in the transient process. The other compensation path is derived from the q-axis current tracking error to mitigate voltage saturation caused by orientation error. Comparative experiments show that during precise direction acceleration, the AFW method increases the current response time by 35% and reduces the peak voltage fluctuation by 38.98%. It also reduces low voltage ripple by 76.4% in inaccurate direction and burst load conditions. The results confirm a significant enhancement of dynamic performance and voltage anti-saturation capability in the FW region. Full article

Harmonic distortion from non-linear loads poses a significant challenge to power quality in residential nanogrids, often requiring complex control strategies and communication between distributed resources. This paper presents a parallel hybrid inverter system for an AC nanogrid that enhances power quality using only decentralized droop-based primary control, without the need for secondary control or communication links. The system features two inverters with strategic placement: one maintains voltage stability at the point of common coupling, while the other directly supplies the harmonic and reactive current demanded by non-linear loads. A compensation mechanism allows the second inverter to dynamically switch from supplying sinusoidal current to injecting targeted harmonic components, effectively isolating distortion from the main grid. Simulation results confirm that this approach significantly reduces voltage distortion at the PCC and ensures balanced power sharing, all while simplifying the control architecture. The proposed method offers a scalable, cost-effective solution for residential nanogrids seeking to integrate diverse loads and distributed energy resources while maintaining high power quality. Full article

This paper presents a novel literature survey on leveraging electrolysis for grid frequency stabilization in power systems with high penetration of renewable energy sources (RESs), uniquely integrating global research findings with specific insights into the Polish energy context—a region facing acute grid challenges due to rapid RES growth and infrastructure limitations. The intermittent nature of wind and solar power exacerbates frequency fluctuations, necessitating dynamic demand-side management solutions like hydrogen production via electrolysis. By synthesizing over 30 studies, the survey reveals key results: electrolysis systems, particularly PEM and alkaline electrolyzers, can reduce frequency deviations by up to 50% through fast frequency response (FFR) and primary reserve provision, as demonstrated in simulations and real-world pilots (e.g., in France and the Netherlands); however, economic viability requires enhanced compensation schemes, with current models showing unprofitability without subsidies. Technological advancements, such as transistor-based rectifiers, improve efficiency under partial loads, while integration with RES farms mitigates overproduction issues, as evidenced by Polish cases where 44 GWh of solar energy was curtailed in March 2024. The survey contributes actionable insights for policymakers and engineers, including recommendations for deploying electrolyzers to enhance grid resilience, support hydrogen-based transportation, and facilitate Poland’s target of 50.1% RESs by 2030, thereby advancing the green energy transition amid rising instability risks like blackouts in RES-heavy systems. Full article

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

In the context of the mining industry, the belt conveyor is a critical piece of equipment. The motor constitutes the primary component of the belt conveyor apparatus, and its stable and accurate operation can significantly influence the performance of the belt conveyor apparatus. This paper introduces an integrated control approach combining vector control methodology with active disturbance rejection control (ADRC) for velocity regulation and model predictive control (MPC) for current tracking. The ADRC framework actively compensates for load disturbances and parameter variations during speed control, while MPC achieves precise current regulation with minimal tracking error. Validation involved comprehensive MATLAB/Simulink R2024a simulations modeling PMSM behavior under mining-specific operating conditions. The results demonstrate substantial improvements in dynamic response characteristics and disturbance rejection capabilities compared to conventional control strategies. The proposed methodology effectively addresses critical challenges in mining conveyor applications, enhancing operational reliability and system longevity. Full article

To combat the catastrophic effects of climate change, the usage of renewable energy sources (RESs) has increased dramatically in recent years. The main drivers of the increase in solar photovoltaic (PV) system grid integrations in recent years have been lowering energy costs and pollution. Active and reactive powers are controlled by a proportional–integral controller, whereas energy storage batteries improve the quality of energy by storing both current and voltage, which have an impact on steady-state error. Since traditional controllers are unable to maximize the energy output of solar systems, artificial intelligence (AI) is essential for enhancing the energy generation of PV systems under a variety of climatic conditions. Nevertheless, variations in the weather can have an impact on how well photovoltaic systems function. This paper presents an intelligent power management controller (IPMC) for obtaining power management with load and electric-vehicle applications. The architecture combines the solar PV, battery with electric-vehicle load, and grid system. Initially, the PV architecture is utilized to generate power from the irradiance. The generated power is utilized to compensate for the required load demand on the grid side. The remaining PV power generated is utilized to charge the batteries of electric vehicles. The power management of the PV is obtained by considering the proposed control strategy. The power management controller is a combination of the twisting sliding-mode controller (TSMC) and Modified Pufferfish Optimization Algorithm (MPOA). The proposed method is implemented, and the application results are matched with the Mountain Gazelle Optimizer (MSO) and Beluga Whale Optimization (BWO) Algorithm by evaluating the PV power output, EV power, battery-power and battery-energy utilization, grid power, and grid price to show the merits of the proposed work. Full article

Focusing on the common problems of phase-locked loop dependence, multiple current sensor requirements, a large number of controllers, and complex settings in traditional parallel active power filter (APF) control methods, this paper proposes a harmonic compensation control strategy based on an improved proportional resonant (PR) controller. The proposed method introduces an instantaneous power theory to construct a reference current model, which relies solely on grid voltage and current signals, does not require load-side current detection and phase-locked loop modules, and effectively simplifies the sensor configuration and system structure. At the same time, compared with the traditional solution that requires PR modules to be configured for each order of harmonics, this study only uses one set of PR controllers for fundamental current tracking, which has advantages in terms of compactness and computing resource occupation. To guide the controller parameter setting, this paper systematically discusses the influence of changes in K_p and K_r on pole distribution and dynamic performance based on discrete domain modeling and root locus analysis methods. The results were verified on the MATLAB/Simulink simulation platform and the 1 kVA experimental platform and compared with the traditional control method that requires the use of phase-locked loops (PLLs), load current sensors, and multiple PR controllers. The simulation and experimental results show that the proposed method has achieved a certain degree of optimization in terms of harmonic suppression effect, dynamic response performance, and system structure complexity. Full article

Remaining Useful Life (RUL) prediction is a crucial task in predictive maintenance. Currently, gated recurrent networks, hybrid models, and attention-enhanced models used for predictive maintenance face the challenge of balancing prediction accuracy and model lightweighting when extracting complex degradation features. This limitation hinders their deployment on resource-constrained edge devices. To address this issue, we propose TBiGNet, a lightweight Transformer-based classification network model for RUL prediction. TBiGNet features an encoder–decoder architecture that outperforms traditional Transformer models by achieving over 15% higher accuracy while reducing computational load, memory access, and parameter size by more than 98%. In the encoder, we optimize the attention mechanism by integrating the individual linear mappings of queries, keys, and values into an efficient operation, reducing memory access overhead by 60%. Additionally, an adaptive feature pruning module is introduced to dynamically select critical features based on their importance, reducing redundancy and enhancing model accuracy by 6%. The decoder innovatively fuses two different types of features and leverages BiGRU to compensate for the limitations of the attention mechanism in capturing degradation features, resulting in a 7% accuracy improvement. Extensive experiments on the C-MAPSS dataset demonstrate that TBiGNet surpasses existing methods in terms of computational accuracy, model size, and memory access, showcasing significant technical advantages and application potential. Experiments on the C-MPASS dataset show that TBiGNet is superior to the existing methods in terms of calculation accuracy, model size and throughput, showing significant technical advantages and application potential. Full article

Aiming to solve optimization scheduling difficulties caused by the double uncertainty of source-side photovoltaic (PV) output and load-side demand response in active distribution networks, this paper proposes a two-stage distribution robust optimization method. First, the first-stage model with the objective of minimizing power purchase cost and the second-stage model with the co-optimization of active loss, distributed power generation cost, PV abandonment penalty, and load compensation cost under the worst probability distribution are constructed, and multiple constraints such as distribution network currents, node voltages, equipment outputs, and demand responses are comprehensively considered. Secondly, the second-order cone relaxation and linearization technique is adopted to deal with the nonlinear constraints, and the inexact column and constraint generation (iCCG) algorithm is designed to accelerate the solution process. The solution efficiency and accuracy are balanced by dynamically adjusting the convergence gap of the main problem. The simulation results based on the improved IEEE33 bus system show that the proposed method reduces the operation cost by 5.7% compared with the traditional robust optimization, and the cut-load capacity is significantly reduced at a confidence level of 0.95. The iCCG algorithm improves the computational efficiency by 35.2% compared with the traditional CCG algorithm, which verifies the effectiveness of the model in coping with the uncertainties and improving the economy and robustness. Full article

LiDAR-assisted wind turbine control holds strong potential for reducing structural loads and improving rotor speed regulation, thereby contributing to more sustainable wind energy generation. However, key research gaps remain: (i) the practical limitations of commercially available fixed beam LiDARs for large turbines, and (ii) the performance assessment of commonly used LiDAR assisted feedforward control methods. This study addresses these gaps by (i) analysing how the coherence of LiDAR estimated rotor effective wind speed is influenced by the number of beams, measurement locations, and turbulence box resolution, and (ii) comparing two established control strategies. Numerical simulations show that applying a low cut-off frequency in the low-pass filter can impair preview time compensation. This is particularly critical for large turbines, where reduced coherence due to fewer beams undermines the effectiveness of LiDAR assisted control compared to the smaller turbines. The subsequent evaluation of control strategies shows that the Schlipf method offers greater robustness and consistent load reduction, regardless of the feedback control design. In contrast, the Bossanyi method, which uses the current blade pitch measurements, performs well when paired with carefully tuned baseline controllers. However, using the actual pitch angle in the feedforward pitch rate calculation can lead to increased excitation at certain frequencies, particularly if the feedback controller is not well tuned to avoid dynamics in those ranges. Full article

Large-scale scene rendering faces challenges in managing massive scene data and mitigating rendering latency caused by suboptimal loading sequences. Although current approaches utilize Level of Detail (LOD) for dynamic resource loading, two limitations remain. One is loading priority, which does not adequately consider the factors affecting visual effects such as LOD selection and visible area. The other is the insufficient trade-off between rendering quality and loading latency. To this end, we propose a loading prioritization metric called Vision Degree (VD), derived from LOD selection, loading time, and the trade-off between rendering quality and loading latency. During rendering, VDs are sorted in descending order to achieve an optimized loading and unloading sequence. At the same time, a compensation factor is proposed to further compensate for the visual loss caused by the reduced LOD level and to optimize the rendering effect. Finally, we optimize the initial viewpoint selection by minimizing the average model-to-viewpoint distance, thereby reducing the initial scene loading time. Experimental results demonstrate that our method reduces the rendering latency by 24–29% compared with the existing Area-of-Interest (AOI)-based loading strategy, while maintaining comparable visual quality. Full article

This paper addresses the challenge of poor dynamic performance in Modular Multilevel Converter-based High-Voltage Direct Current (MMC-HVDC) systems within weak power grids when conventional control strategies are applied. To enhance system performance, a novel grid-connected power control method integrating Virtual Synchronous Generators (VSGs) and Passivity-Based Control (PBC) is proposed. The passivity characteristics of the MMC and the roles of virtual inertia and damping in VSG control are thoroughly examined. Based on the passivity property of the MMC, PBC is implemented in the current inner loop, while VSG control, leveraging its unique working characteristics, is incorporated into the power outer loop. To further optimize performance, adaptive virtual inertia and damping compensation mechanisms, utilizing sigmoid functions, are introduced within the VSG framework. The synergistic operation of PBC and adaptive VSGs significantly improves the dynamic response and robustness of the MMC-HVDC system. The effectiveness and feasibility of the proposed method are validated through simulation experiments in MATLAB/Simulink, conducted under power variations, grid voltage variations, and load changes. Full article

To enhance the speed control performance of the permanent magnet synchronous motor (PMSM) servo system, an improved sliding mode control method integrating a torque observer is presented. The current loop uses current feedback decoupling PID control, and the speed loop applies sliding mode control. In comparison to previous work in hybrid SMC using fuzzy logic and torque observers, this p proposes a hyperbolic tangent function in replacement of the signum function to solve the conflict between rapidity and chattering in the traditional exponential reaching law, and fuzzy and segmental self-tuning rules adjust relevant switching terms to reduce chattering and improve the sliding mode arrival process. A load torque observer is designed to enhance the system’s anti-interference ability by compensating the observed load torque to the current loop input. Simulation results show that compared with traditional sliding mode control with a load torque observer (SMC + LO), PID control with a load torque observer (PID + LO), and Active Disturbance Rejection Control (ADRC), the proposed strategy can track the desired speed in 0.032 s, has a dynamic deceleration of 2.7 r/min during sudden load increases, and has a recovery time of 0.011 s, while the others have relatively inferior performance. Finally, the model experiment is carried out, and the results of the experiment are basically consistent with the simulation results. Simulation and experimental results confirm the superiority of the proposed control strategy in improving the system’s comprehensive performance. Full article

This study addresses the problem of DC-link current pulsations in four-wire AC/DC converters with energy storage operating under unbalanced load conditions. A sensorless compensation algorithm based on AC-side voltage and current measurements is proposed, eliminating the need for additional sensors. The algorithm incorporates a Second Order Generalized Integrator (SOGI) filter for accurate detection and compensation of the pulsating component. Experimental validation under severe asymmetry confirmed the method’s effectiveness. In case 1, the AC component of the DC-link current was reduced from 7 A to 1.4 A and, in case 2, from 3 A to 0.5 A. Corresponding FFT analysis showed a reduction in relative amplitude from 240% to 21.5% and from 264% to 22%, respectively. In an asymmetrical charging scenario (case 3), the AC component was reduced from 2.5 A to nearly 0 A, corresponding to a decrease from 42% to 4.9% in the FFT spectrum. These results demonstrate that the proposed method enables stable converter operation even under deep phase current imbalances, significantly improving energy storage reliability and utility grid performance. Full article