Input Load Research Articles

PV Fed Grid System with PI Controller for Enhancing Power Quality Using CMBO-PCSANN Approach

This paper proposes a hybrid method for a photovoltaic fed grid system with a PI controller for improving power quality. The hybrid approach that is being proposed is the joint execution of both the cat and mouse-based optimizer (CMBO) and pyramidal convolution shuffle attention neural network (PCSANN). For this reason, it is called the CMBO-PCSANN approach. This method's proposed objective is to lessen the system's total harmonic distortion. The proposed technique CMBO is used to reduce the THD of system and the PCSANN approach is used to forecast the system's ideal solution. The proportional–integral (PI) controller (Kp, Ki) is adjusted by the CMBO-PCSANN logic controller in order to control the grid-fed inverter's voltage and current. This improves the system's power quality and allows for a quick response to a various load disturbances and inputs. By then, the proposed approach has been implemented on the MATLAB platform, and its performance has been compared using present techniques like MIWO-P&O (Modified Invasive Weed Optimization with Perturb and Observe), IBSMFO (Improved Bat Search Algorithm and Moth Flame Optimization), and HJSPSO (Hybrid Jellyfish Search Optimizer and Particle Swarm Optimizer). In the proposed strategy, the total harmonic distortion is 3.5% which is lower than other existing approaches.

Artificial Neural Network Based On a Predictive Current Control in A DC to DC Buck Converter

Artificial Neural Networks (ANNs) have emerged as a powerful tool for enhancing predictive control strategies in power electronics applications. This paper presents an ANN-based predictive current control approach for a DC-DC buck converter, aimed at improving dynamic performance and efficiency. The proposed method utilizes a trained neural network model to predict the optimal duty cycle for the converter, based on real-time input voltage, load conditions, and current feedback. By leveraging machine learning techniques, the system can achieve faster transient response, reduced steady-state error, and enhanced robustness against parameter variations compared to conventional control methods like PI controllers.

Modelling phosphorus inputs and dynamics in a large tropical semiarid basin.

This study proposed a simple process-based model to predict total phosphorus (TP) inputs and dynamics in a large tropical semiarid basin in Brazil (19,015 km2), where the Castanhão dam, the largest non-hydropower reservoir in Latin America, is located. The model solved on a monthly basis the water balance and TP dynamics along the river system, considering complete mixing at the cross-section, steady-state regime, and a piston-type transport with first-order decay. Both primary and secondary data were used to validate the model, with deviations of up to about 20%. A linear increase in the flow rate along the river was observed, indicating that transmission gains were dominant. TP concentration increased with river flow rate and a consistent decay along the system was observed. TP from point sources (PS) was assumed constant. TP from non-point sources (NPS) decreased with time (R2 = 0.90) due to the continuous wash-off process. NPS was the dominant load, decreasing respectively from 99 to 61% of the total load from high to low flows. TP load varied from 1 to 141 ton/month, with an average of 34 ton/month. This resulted in an average TP yield of 21.2kgkm-2year-1, which is significantly lower than the national average due to the peculiarities of the region: lower precipitation, intermittent rivers, and high-density reservoir network. All the simulated TP values were above the mandatory limit of 0.05mg/L. PS reduction was only effective for relatively low flows, while NPS reduction was the most relevant remediation measure for both high and low flows. The impact of climate change on TP concentration also yielded TP > 0.05mg/L for all the projected scenarios, with the input loads to the Castanhão reservoir spanning from 23 to 266 ton/month. The proposed model can also be applied to other regions with different hydroclimatic conditions and land uses.

A New Transformer‐Less Buck‐Boost Converter With Reliability Assessment Using the Improved Markov Model

ABSTRACTThis work proposes a new continuous input current cubic gain buck‐boost converter with a simple structure and reduced components. The converter has minimal components consisting of a twin‐switch configuration with a broad range of duty‐cycle operations. The converter attains unity gain at 24.51% duty cycle and can perform under both the continuous and discontinuous modes of conduction. Adding to the fundamental analysis of the converter, a study to examine the converter's volumetric distribution and cost factor calculation is also discussed. The averaged small‐signal model of the converter is formulated to assess the transfer function and the stability of the converter during its operation. The converter's reliability is also determined to assess its performance using the improved Markov model. The discussion of the variation of the reliability and the MTTF with parameters like the duty cycle of the active switch, input voltage, and output power is also highlighted in the paper. The converter performs at an efficiency of 95.20% while it delivers 100 W output power, making it feasible for low‐ to medium‐power applications. The converter's 200 W hardware prototype is presented, followed by a discussion of the corresponding results. Furthermore, the dynamic state survey of the converter is also presented, where step changes in input voltage, load, and duty cycle are being considered.

Investigating the Tribological Performance and Wear Mechanisms of Stainless Steel 316L in Cold Metal Transfer-Based Wire Arc Additive Manufacturing Under Varied Loads and Thermal Inputs

Abstract In recent years, cold metal transfer (CMT)-based wire arc additive manufacturing (WAAM) has gained significant attention in the manufacturing sector, particularly for its ability to produce components with low thermal input and high deposition rates. This study investigated the tribological behaviour of SS316L walls fabricated using CMT-based WAAM, employing a ball-on-plate linear reciprocating test with tungsten carbide (WC) counter body under varying thermal inputs and applied loads (15 N, 20 N, and 25 N). The tests were conducted for 10 minutes at a frequency of 15 Hz and a stroke length of 2 mm. Results indicate that the coefficient of friction (COF) increased slightly with applied loads, yielding an average COF of 0.22 across all loads. Wear rate analysis revealed that both increased applied load and heat input led to a higher wear rate, with the maximum wear rate (3.39 × 10−3 mm3/m) occurring at high heat input and 25 N, while the minimum wear rate (1.2 × 10−3 mm3/m) was observed at low heat input and 15 N. Vickers microhardness results demonstrated an inverse relationship between hardness and heat input, with hardness increasing by 11% as heat input decreased from high to low. FESEM analysis of wear tracks showed significant craters, abrasive grooves, delamination, surface cracks, and particle adhesion, identifying an abrasive-dominant wear mechanism with surface fatigue, partial adhesion, and oxidative wear. Wear debris analysis showed sharper angular particles and larger irregularly shaped flakes. X-ray diffraction (XRD) spectra confirmed δ-ferrite and γ-austenite phases pre- and post-wear, with post-wear analysis showing an α′-martensite peak, indicating phase transformation during wear.

Dynamic behavior and jet load of vapor bubbles driven by spherical shock waves

The dynamic behavior and loading characteristics of cavitation bubbles are predominantly determined by the system's input load and boundary conditions. This study employs numerical simulations to examine the response of a bubble driven by a spherical shock wave under varying boundary conditions, with a particular focus on the impact of the shock wave on bubble collapse. The findings reveal that boundary conditions critically influence bubble evolution and collapse-induced loading. Specifically, the reflection and transmission at the boundary surfaces substantially modify the bubble's dynamics. In the vicinity of an elastoplastic thin wall, the bubble's behavior and loading characteristics approximate those observed in a free-field environment, as the thin wall partially absorbs and transmits part of the impact energy, thereby attenuating the reflected shock wave. This attenuation primarily affects bubble evolution through the reflected wave and the Bjerknes effect. Moreover, a linear relationship is identified between the strength of the input shock wave and the peak collapse load, with the collapse load at varying distances exhibiting a strong linear correlation with distance. These results underscore the potential for controlling bubble collapse loads by modulating shock wave strength, offering a theoretical framework for understanding bubble dynamics under diverse boundary conditions and practical insights for cavitation protection and application strategies.

Research on seismic characteristics of supercharged boiler based on measured boundary

Research on seismic characteristics of supercharged boiler based on measured boundary

Non-minimum Phase Zeros of Multi-DoF Damped Flexible Systems

Abstract This paper investigates the non-minimum phase (NMP) zeros in the transfer function, between actuated load input and measured displacement output, of a multi-degree of freedom (DoF) flexible system in the presence of proportional viscous damping. NMP zeros have a negative impact on the dynamics and control of flexible systems and therefore are generally undesirable. Viscous damping is one potential means to guarantee that no NMP zeros exist in the system. However, the impact of viscous damping on NMP zeros of multi-DoF flexible systems is not adequately studied or understood in the literature. In order to address this gap, a change of variable method is used to first establish a simple mathematical relationship between the zeros of a multi-DoF undamped flexible system and its proportionally damped counterpart. The “proportional” viscous damping model is used due to its practical amenability, conceptual simplicity and ease of application. This mathematical relationship (between zeros of an undamped system and its damped counterpart) is used to derive the necessary and sufficient condition for the absence of NMP zeros in proportionally damped flexible systems. A graphical analysis of this necessary and sufficient condition is provided, which leads to the formulation of simple proportional damping strategies. A case study of a four-DoF flexible system is presented to demonstrate how a proportional viscous damping strategy can be used to simultaneously guarantee the absence of NMP zeros in multiple single-input single-output (SISO) transfer functions of a multi-DoF flexible system.

Interleaved quartic high gain DC–DC converter

This research paper presents a high-gain DC–DC converter with ultra-step-up voltage gain capability. The proposed converter is synthesized from a two-phase interleaved boost converter (IBC), and its voltage gain is doubled by adopting a voltage lift capacitor. To enhance its voltage gain capability, a floating capacitor-based gain extension cell is adopted subsequently. This cell yields a voltage gain that is cubed times the output voltage obtained from a classical boost converter (CBC). By cascading the two stages, the voltage gain of the proposed converter is enhanced to quartic times (4th power) that of the CBC. The proposed gain extension concept is validated by conducting practical experiments on a 16 V to 400 V, 150 W prototype version. Practically, the prototype converter delivers 150 W to the load and operates at a full-load efficiency of 92.7% when its switches are operated at safe duty ratio values. Under dynamic conditions, the proposed converter regulates the output voltage to 400 V quickly over a wide range of input voltage and load current variations; the overshoots and undershoots are also negligible. The maximum voltage gain of the proposed converter momentarily increases to 37 when the input voltage is drastically reduced to 10.8 V while the switches are still operated at safe duty ratio values. The voltage stress on the semiconductor devices is only a fraction of the output voltage due to the hybrid voltage gain extension technique. The input current is also ripple-free as the switches in the IBC structure are always operated at a duty ratio of 50%, and only the third switch is controlled to meet the required voltage gain. The salient features of the proposed converter are clearly highlighted by comparing it with several converters that possess quadratic, cubic, and quartic voltage gain functions. The common-ground connection between the source and the load in the proposed converter is an added preferable feature for PV applications.

Static load sharing performance of non-orthogonal and offset helical face gear dual-branch drivetrain

To research the static load sharing performance of non-orthogonal offset helical (NOH) face gear dual-branch drivetrain, the non-orthogonal offset layout forms of the system are explored, and the meshing phase differences of split-stage and converge-stage are analyzed. Based on the torque balance condition, deformation coordination condition, and static balance condition of the system, a static torque distribution model of the system was generated, and the static load sharing coefficients (SLSCs) were obtained results. The influence of assembly errors, support stiffness, torsional stiffness, meshing stiffness, and input load on the static load sharing performance was analyzed. The results indicate that assembly errors have a strong influence on the SLSCs, with the shaft angle errors exerting the strongest influence on the SLSCs; When there are assembly errors, the impact of input load and torsional stiffness on the static load sharing performance is greater than without assembly error. To increase the load sharing performance of the system, the meshing phase differences should be eliminated, and the offset layout form should be adopted in the dual-branch transmission system. In addition, reducing the support stiffness of the input helical gear and the fluctuation amplitude of meshing stiffness can also improve the load sharing performance.

Development and Efficiency Analysis of Dual Active Bridge Converter Employing SiC Devices Using TMS 320F280039

The utilization of on‐board chargers (OBCs) in electric vehicle (EV) has become increasing prevalent due to their configuration and cost‐efficiency in the context of installation. Furthermore, the automobile industry is increasingly showing interest in bidirectional power flow because of the EV’s ability to provide energy back to the network. The paper presents an analytical method for designing the crucial elements of dual active bridge (DAB) with single‐phase shift (SPS) modulation control. By comparing the estimated energy efficiency characteristics with measurements from a prototype 90‐W single‐phase DAB converter fitted with silicon carbide (SiC) MOSFETs, the robustness of this approach is exemplified and loss, and efficiency comparison analysis is carried out with conventional switching devices. Furthermore, this study presents a closed‐loop voltage controller that effectively regulates the output voltage in the presence of significant variations in both the input line and load conditions. Finally, a comparison analysis is undertaken to evaluate important factors such as overall efficiency and settling time across different load circumstances. This analysis provides substantial perspectives toward the actual functionality of these systems through contrasts of simulation results with hardware‐based results from the real world.

Empirical Neural Network Studies for Multi-Port Load Calculation by the Input Currents

A linear multi-port is considered a model of a wire communication line with physical quantities sensors or as a power loads supply line. The problems of known methods are shown to determine the multi-port parameters and the calculation of load resistances by specified or measured input currents. In the present work, the “loads‒currents” relationships are approximation tasks of feedforward neural networks. The corresponding input currents are calculated for a particular set of load values, using the multi-port models with one, two, and three loads. This is how the training or input vector (input currents) and the target vector (loads) are composed, the dimension is equal to the amount of input currents or loads, and the size corresponds to the load set. Numerical experiments by the Fit Data package of MATLAB Deep Learning toolbox demonstrate the accuracy of load calculation and capability to generalization. An introduced quantitative index of the quality of training allows us to identify the minimum size of the training vector and the optimal amount of hidden layers’ neurons. The obtained results provide purposeful and fast network training.

A Potential Technology for Improving the Quality of Lake Water Environments in Cold Zones: A Case Study from Lake Hulun, China

Lakes in cold zones have common characteristics of long frozen periods and fragile water ecosystems. More and more lakes in cold zones have been experiencing water quality deterioration due to eutrophication with climate change and human activities. Lake Hulun is located in the cold zone of northern China, in which Cyanobacterial blooms frequently occur. The excessive nutrient input with inflowing river water and the change in lake hydrodynamic condition might be the main factors affecting this. To obtain a better understanding of the effects, the input loads of nutrients from the inflowing rivers were analyzed. A field test of freezing concentration combined with microbial activity regulation was carried out at a river–lake confluence. The results showed the following: (1) Lake Hulun receives a large amount of nitrogen and phosphorus pollutants from river runoff every year, and the water quality of these river is inferior Grade V, which greatly increases the difficulty in ecological purification of cold zone lakes. (2) The microbial activity control technology has a high purification rate for water during the unfrozen period. The order of purification rates for each hydrochemical index was TP > TN > COD > NH4+-N, and the purification rates of TN and COD were between 35% and 36%. Compared with the water before purification, the water quality grade improved from Grade V to Grade III. (3) The composite technology of freezing concentration–microbial activity regulation has a general water purification rate during the frozen period. Under the low-temperature condition, the TN and COD nutrient fluxes in the water were reduced by 9.38% and 9.36%, respectively. After purification, the water quality grade of the ice body was above Grade II, and the water under the ice layer was above Grade IV, which was one grade higher than the water quality grade of the original lake water. This water treatment and purification process is a green, low-energy consumption, and low-cost technology. This study can provide reference for the integration and demonstration of lake water ecological governance and water resources security technology in cold and arid regions. It is beneficial to the sustainable development of the lake.

Optimizing power sharing accuracy in low voltage DC microgrids considering mismatched line resistances

The main difficulties facing the operation of parallel converters in DC microgrids (DCMGs) are load sharing, circulation current, and bus voltage regulation. A droop controller is commonly used to control current sharing among parallel DC–DC converters due to its simplicity. However, the values of droop parameters impact both bus voltage regulation and the error in current sharing among converters. This study introduces an adaptive and straightforward droop control mechanism designed to estimate droop parameters. The algorithm aims to enhance both bus voltage regulation and load sharing performance within DCMGs. Additionally, to mitigate bus voltage deviation in DCMGs, the second loop shifts the droop lines to maintain voltage regulation at rated values. The proposed method has been compared with the conventional droop controller under variable input voltage and load resistance conditions. Simulation results demonstrate that the presented method outperforms the traditional control approach, achieving superior performance.

A Dynamic Impedance Matcher to Broaden Input Power and Load Ranges for High-Power Rectifier in Microwave Wireless Power Transmission

A Dynamic Impedance Matcher to Broaden Input Power and Load Ranges for High-Power Rectifier in Microwave Wireless Power Transmission

Application of RBF neural network PID control on buck DC-DC converter

Abstract A structure to solve low control accuracy and poor tracking performance in traditional PID controlled DC-DC converters when input voltages and load currents fluctuate has been proposed: combining PID control with Radial Basis Function (RBF) neural networks. To optimize the PID controller’s parameters, this approach makes use of the RBF neural network’s approximation capability for nonlinear functions. Under different test conditions, the neural network takes the output voltage and its deviation from the reference value as inputs. The hidden layer employs Gaussian activation and adjusts by gradient descent. The output layer computes the PID controller’s Proportional (Kp), Integral (Ki), and Derivative (Kd) by the linear combination of hidden layer node outputs. Simulation results using MATLAB/Simulink demonstrate that, under different test conditions, the system employing RBF neural network-PID control exhibits a smaller overshoot compared to traditional PID control systems. Furthermore, in steady-state circumstances, the settling time is shortened by around 1/2 and is decreased by roughly 1/4 in the presence of disturbance signals. These results unequivocally demonstrate that, in the presence of fluctuating test conditions, the new structure of this combination greatly improves the control precision and tracking performance of DC-DC converters.

Coordinated intelligent control strategy and power management for marine gas turbine under pulsed load using optimized neural network

Coordinated intelligent control strategy and power management for marine gas turbine under pulsed load using optimized neural network

Recursive Bayesian estimation of wind load on a monopile-supported offshore wind turbine using output-only measurements

Recursive Bayesian estimation of wind load on a monopile-supported offshore wind turbine using output-only measurements

Characterization of aircraft landing impact loads: Effects on vertical tire-pavement contact characteristics and pavement performance

Characterization of aircraft landing impact loads: Effects on vertical tire-pavement contact characteristics and pavement performance

On investigation of capillary and pressure involved working constrains for water-driven and sintered-powder heat pipes using a semi-empirical model

On investigation of capillary and pressure involved working constrains for water-driven and sintered-powder heat pipes using a semi-empirical model