Load Variations Research Articles

Modelling and Analysis of Adaptive ON-Time Controlled Boost Converter Using Verilog-A

ABSTRACT This paper presents a mixed modelling of adaptive ON-time controlled boost converter using Verilog-A. It is a mixture of Mathematical modelling, Behavioural modelling, and discrete components. While mathematical modelling has been used to model individual components, for circuits such as comparator and pulse generator behavioural modelling is used. Power MOSFETs in the power stage are the only discrete components used from the library. Each model was coded using Verilog-A in Cadence Virtuoso and its symbolic notation obtained. The symbols were then connected to obtain the complete schematic of the boost converter. The behavioural modelling of the boost converter is targeted for an output voltage of 1.2 V for an input voltage ranging from 0.5 to 0.7 V and switching frequency of 800 KHz. The modelling also captures the results for 40% variation in input and load, for which the output voltage is found to vary by about 5–6%. This work is an attempt at completely modelling a complex analog circuit. It also shows the successful integration of modelled components with discrete components. To validate the results obtained, the performance of the modelled converter is compared with that of circuit simulation, with both simulations being conducted at identical operating conditions. The results obtained with modelling simulation match that obtained with the circuit simulation.

A Final Study: The Role of Plastic-Modified Bitumen in Enhancing Durability and Sustainability in Road Construction

Abstract: This study systematically evaluates the influence of integrating varying percentages of plastic waste into bitumen on the mechanical properties and performance characteristics of bituminous pavements. Experimental results indicate that the incorporation of plastic waste enhances the performance of bitumen up to an optimal threshold of 8% by weight; exceeding this concentration results in negligible improvements or deterioration in performance. Rigorous laboratory assessments, including Dynamic Shear Rheometer (DSR) analyses, demonstrate that plastic-modified bitumen exhibits a significantly elevated complex modulus (G*) and a reduced phase angle (δ) in comparison to unmodified bitumen, with the 8% modification yielding the most favourable outcomes. Enhanced Marshall Stability, improved flow values, reduced penetration, and increased softening points further signify superior resistance to load and thermal variations. The elevated viscosity of plastic-modified bitumen contributes to enhanced pavement strength and durability. Future investigations should encompass additional evaluations, such as ductility, elastic recovery, and flash/fire point tests utilizing the wet process, alongside an exploration of the dry process involving the coating of aggregates with shredded plastic. The findings substantiate that the incorporation of plastic waste into bitumen not only augments pavement performance but also presents a viable eco-friendly solution for plastic waste management, thereby fostering sustainable construction methodologies. The findings confirm that using plastic waste in bitumen improves pavement quality and offers an eco-friendly solution for plastic waste management, promoting sustainable construction practices.

Adaptive Operation Strategy of a District Cooling System with Chilled Water Storage and Its Validations by OpenModelica Modeling and Simulations

Developing operation strategies for district cooling systems with chilled water storage is challenging due to uncertain fluctuations of cooling demand in actual operations. To address this issue, this paper developed an adaptive operation strategy and performed its validations by modeling and simulating a commercial cooling system in Shanghai using OpenModelica. Firstly, the originally designed operation strategy of the cooling system was evaluated by simulation but was found unable to meet the statistically averaged ideal cooling requirements due to the early exhaustion of stored chilled water at about 5:30 PM. Then, to build foundations for adaptive operation strategy development, a newly designed operation strategy was established by increasing the operation time of base load chillers in the valley and flat electricity price periods. The new strategy proved numerically sustainable in satisfying the ideal cooling demand. Moreover, to realize the strategy’s adaptability to actual cooling load fluctuations, an adaptive operation strategy was developed by tracking the target stored chilled water mass curve that was calculated by implementing the newly designed strategy. The simulation results verify that the adaptive operation strategy enables good adaptability to representative cooling load fluctuation cases by automatically and periodically adjusting the operation status of base load chillers. The adaptive operation strategy was then further widely numerically tested in hundreds of simulation cases with different cooling load variations. The time-lagging problem resulting in strategy failures was found in numerical tests and was addressed by slightly modifying the adaptive strategy. Results indicate that the adaptive operation strategy enables adaptability to deal with cooling demand fluctuations as well as allowing low cooling supply economic costs and power grid-friendly characteristics. This study provides theoretical support to strategy design and validations for district cooling system operations.

Research on the aerodynamic characteristics of electrically controlled rotor under Parallel Blade Vortex Interaction using Lattice Boltzmann Method

An electrically controlled rotor (ECR), also known as a swashplateless rotor, employs a trailing edge flap (TEF) system for primary rotor control instead of a swashplate, demonstrating the significant potential in rotor vibration and noise reduction. To investigate the aerodynamic characteristics of the blade flap segment of the ECR under parallel blade vortex interaction, an aerodynamic analysis model based on the lattice Boltzmann method (LBM) is established using the D3Q27 lattice model. The model is validated against experimental data of both the airfoil with trailing edge flap and conventional airfoil under vortex interaction, showing that the LBM can effectively predict variations in aerodynamic loads under both conditions. Based on this model, the effects of different flap deflection angles and miss distances on the aerodynamic characteristics of the ECR under parallel BVI are analyzed. The results indicate that under strong vortex interaction, a region of flow separation forms due to the entrainment effect of the vortex and adverse pressure gradient. The small-scale vortex structure upstream of the flap can also be observed and is believed to contribute to the unsteady flow phenomena such as vortex structure splitting, development, and separation on the upper surface of the flap. The different flap deflection angles mainly affect the scale and type of vortex structures developed on the flap upper surface. As the miss distance increases, the interaction effect is significantly weakened compared to strong vortex interaction. However, as the vortex moves downstream along the airfoil lower surface, it entrains vorticity from the lower surface, ultimately forming a negative pressure region on the lower surface of the flap. The different flap deflection angles will influence the structural characteristics during the downstream motion of the vortex, which changes the size of the negative pressure region, causing differences in the magnitude of the variations in aerodynamic parameters.

Real-Time Co-Simulation Implementation for Voltage and Frequency Regulation in Standalone AC Microgrid with Communication Network Performance Analysis across Traffic Variations

Effective communication networks are crucial for ensuring reliable and stable operation and control in smart microgrids (MGs). This paper proposes a comprehensive analysis of the interdependence between power and communication networks in the real-time control of a standalone AC microgrid to address this vital need. Thus, the role of communication network design is emphasized in facilitating an effective centralized secondary control to regulate the voltage and frequency of an MG. Consequently, voltage and frequency deviations from the droop-based primary control should be eliminated. This study employs a real-time co-simulation testbed setup that integrates OPAL-RT and network simulator (ns-3), supporting a rigorous evaluation of the interplay between the communication networks and control within the MG. Experiments have been conducted to demonstrate the effectiveness of the designed communication infrastructure in seamlessly enabling real-time data exchange among the primary and secondary control layers. Testing scenarios have been implemented, encompassing low-traffic patterns with minimal load variations and high traffic characterized by more frequent and severe load changes. The experimental results highlight the significant impact of traffic variations on communication network performance. Despite the increase in traffic, the effectiveness and reliability of the designed communication network have been validated, underscoring the vital role of communication in ensuring the resilient and stable operation of cyber–physical standalone AC microgrids.

Adaptive reactive power control for voltage rise mitigation on distribution network with high photovoltaic penetration

This research addresses the challenge of voltage rise on low voltage distribution networks with high photovoltaic penetration. The proliferation of distributed generators, particularly small-scale PV systems, has raised concerns about voltage stability and power quality in these networks. Existing reactive power control techniques, such as fixed power factor and voltage-based methods (Q(V)), have limitations in effectively mitigating voltage rise while considering load variations and network sensitivity. To overcome these limitations, an adaptive reactive power control technique is proposed in this research. The technique combines both PV active power injection and network voltage considerations in real-time to dynamically adjust reactive power output. Unlike traditional methods, which directly link reactive power reference to PV active power or voltage, the adaptive technique calculates the change in reactive power reference (ΔQ) based on both factors. This dynamic approach enables more responsive and accurate voltage regulation. The effectiveness of the adaptive technique is demonstrated through MATLAB simulations on a representative low voltage distribution network. The results show that the adaptive technique outperforms existing methods, providing better voltage regulation and reduced losses. The technique's adaptability to different scenarios and variations is also highlighted. However, it is noted that the adaptive technique may have a slightly slower response time compared to existing methods due to its dynamic nature.

Deep learning enabled in vitro predicting biological tissue thickness using force measurement device

Accurate perception of biological tissues (BT) thickness is essential for preliminary evaluation of medical diagnosis and animal nutrition. However, traditional thickness measuring approaches of BT require complex operation, high-cost, and trigger biological stress response. Herein this study, an novel in vitro BT thickness measuring approach integrated with force test system (FST) and the discrete multiwavelet transform convolutional neural network (DMWA-CNN) prediction model based on deep learning are proposed. Simultaneously, several comprehensive experiments and model comparisons are conducted to demonstrate the superiority of the proposed approach. By establishing a DMWA-CNN demonstrates higher estimation accuracy than other traditional algorithm, achieving 100 % accuracy for artificial BT. Moreover, the experimental results indicate that proposed approach is robust to elastic modulus variation (E), external load variation (F), and small thickness differences (Ts). In addition, four kinds of the pork’ thickness are experimentally measured, and the accuracy value is not less than 98.2 %. The thickness of BT determined using the FST and DMWA-CNN algorithm demonstrate potential application in the biomechanical parameter prediction.

Research on bridge spatial deformation monitoring using light poles and displacement-relay theory

The growing traffic on municipal bridges demands effective methods for monitoring spatial deformations to ensure structural safety. Traditional measurement techniques often fall short in terms of efficiency and precision. This study presents an innovative bridge deformation measurement system that employs light poles and displacement-relay theory, integrated with high-resolution optical cameras, sub-pixel image recognition algorithms, and IMU data. Laboratory experiments involved chain camera units and multiple data processing methods, including sub-pixel recognition and wavelet denoising. Results showed that sub-pixel recognition with four-layer wavelet denoising achieved the highest accuracy, aligning closely matching the performance of commercial equipment. Field tests on the Xinwuyi and Dinghuaimen Bridges under varying traffic conditions confirmed the system’s reliability. The study concludes that the system effectively captures dynamic load variations, enhancing the accuracy of spatial deformation measurements. Future work will refine error analysis and data processing methods to further enhance the system’s practical applicability.

Heating Performance and Energy Efficiency Analysis of Air-Source Heat Pumps in Public Buildings Across Different Climate Zonings

Article Heating Performance and Energy Efficiency Analysis of Air-Source Heat Pumps in Public Buildings Across Different Climate Zonings Junbao Fan 1, Yilin Liu 1,*, Jing Ma 2, Ying Cao 1,2, Zhibin Zhang 1, Xin Cui 1 and Liwen Jin 1,* 1 School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, No. 28 Xianning West Road, Xi’an 710049, China 2 China Architecture Design and Research Group, Beijing 100044, China * Correspondence: ylliu@xjtu.edu.cn (Y.L.); lwjin@xjtu.edu.cn (L.J.) Received: 11 July 2024; Revised: 3 September 2024; Accepted: 5 September 2024; Published: 20 September 2024 Abstract: Public buildings exhibit the highest operational energy consumption and contribute the most to carbon emissions compared to other types of building. The electrification of energy terminals in public buildings is crucial for the energy conservation and emission reduction, especially the energy-saving retrofitting of heating systems. Given the significant impact of climate on the performance of air-source heat pumps, this study explored the performance and energy efficiency of air-source heat pump systems in public buildings across different climate zonings. Using Design Builder software, the physical models of three types of public buildings (commercial, hotel, and office) were constructed, and the annual variations in the building load was analyzed. Considering the effects of defrosting and low-temperature conditions, an air-source heat pump heating system models were developed using TRNSYS software. The simulation results showed that the average COP of the heat pump system on the coldest day in Harbin, Beijing, and Shanghai were 1.7, 2.46 and 2.49, respectively. Moreover, the analysis factor correlation analysis reveals that the COP of the heat pump system is positively correlated with the dry-bulb temperature, negatively correlated with the building load. Surprisingly, the COP is not affected by the types of public buildings. The findings of this study are expected to provide valuable guidance for the application and regulation of air-source heat pumps in the public buildings.

DUAL LOOP VOLTAGE DROOP REGULATED CONTROLLER FOR DC MICROGRID USING HYBRID PSO AND GGO ALGORITHMS

Abstract DC microgrids are effectively employed in distribution network integration with renewable energy sources. The droop control technique is commonly utilized for DC microgrid regulation during load sharing. It minimizes the potential instability caused by disturbances such as input voltage changes, uncertainty parameters, and constant power load (CPL). Nevertheless, conventional droop control is inadequate in achieving precise current and satisfactory voltage regulation distribution for load sharing. The proposed approach comprises two separate loops for the DC bus voltage regulation and load current distribution, delivering a CVL and CPL to overcome the single optimization voltage controller technique. The primary loop uses the PSO algorithm to precisely manage the current load sharing among two parallel converters. Due to this the instability problems arising from disturbances, and it mitigated by the proposed topology. The multiobjective optimization technique provides notable resilience, rapid dynamic response, and robust stability over considerable variations in load. The secondary loop utilises the GGO algorithm to optimize the parameter to minimize the DC bus voltage regulation, voltage management, reasonable distribution of load power, and suitable reliability. The performance of the voltage regulation enhance & settling time for output voltage has been mitigated more than 31ms times through the proposed controller compared to conventional controller under the variation of CVL, CPL, and input voltage disturbance demonstrated in simulation results.

Predictive Voltage Control in Multi-Modular Matrix Converters under Load Variation and Fault Scenario

This paper presents a model predictive control (MPC) strategy to regulate output voltages in a multi-modular matrix converter topology for isolated loads. The converter system harnesses power from a six-phase permanent magnet synchronous generator (PMSG) to deliver sinusoidal voltages to a three-phase load, with LC filters positioned at the output of each MC module within the multi-modular scheme. The proposed MPC approach ensures that the output voltages remain within acceptable ranges of magnitude, phase, and frequency, even under load variations and system faults. This control strategy is particularly suitable for uninterruptible power supply systems, microgrids or other applications where voltage regulation is critical. Experimental studies validate the effectiveness of the control strategy under various load conditions, reference voltage changes, and simulated system fault scenarios. The results highlight the robustness and reliability of the proposed voltage control using the multi-modular matrix converter.

Dynamic performance analysis of proton exchange membrane fuel cell in marine applications

To investigate the dynamic characteristics of proton exchange membrane fuel cells (PEMFCs) on maritime vessels, a lumped-parameter model for the PEMFC systems was developed based on a hybrid vessel propulsion systems model. Simulations and analyses of the dynamic characteristics of PEMFCs were conducted under typical sailing conditions. The results reveal a noticeable hysteresis in the operating temperature of PEMFC stacks during vessel voyages, with a delay of about 25 s, leading to significant overvoltage in activation, ohmic, and concentration differences. Significant variations in vessel loads can cause large fluctuations in the component gas pressures in the cathode and anode flow paths within 16.6–19.8 kPa and 78.57–93.06 kPa, respectively. A comparison of different humidification levels for cathode and anode gases demonstrates that, at the same moment, as the humidification of cathode and anode gases increases from 20 % to 100 %, the water content in the proton membrane increases from 2.29 to 13.47, the ohmic impedance decreases from 1.35 mΩ/cm2 to 0.174 mΩ/cm2, and the overshoot of the fuel cell voltage decreases. The output delay decreases from 25 s to 8 s, enhancing overall fuel cell performance. These findings are significant for optimizing the design, performance, and real-time control of marine PEMFC systems.

Real-Time Identification and Nonlinear Control of a Permanent-Magnet Synchronous Motor Based on a Physics-Informed Neural Network and Exact Feedback Linearization

This work proposes a novel method for the real-time identification and nonlinear control of a permanent-magnet synchronous motor (PMSM) based on a Physics-Informed Neural Network (PINN) and the exact feedback linearization approach. The proposed approach is presented in a direct-quadrature framework, where the quadrature current and the rotational speed are selected as outputs and the direct and quadrature voltages are selected as inputs. A nonlinear difference equation is selected to describe the physical dynamics of the PMSM, and a PINN is designed based on the aforementioned structure. A simplified training scheme is designed for the PINN based on a least-squares structure to facilitate online training in real time. A nonlinear controller based on exact feedback linearization is designed by considering the nonlinear model of the system identified based on the PINN. Therefore, the proposed approach involves identification and control in real time, where the PINN is trained online. In order to track the reference for the rotational speed, a nonlinear controller with integral action based on exact feedback linearization is designed based on a linear quadratic regulator. As a result, the proposed approach can be used to identify the system to be controlled in real time, and it is able to track any small change in the real model; in addition, it is robust to both external and internal disturbances, such as variations in torque load and resistance. The proposed approach is evaluated through simulation and using a real PMSM, and the results of reference tracking are evaluated under disturbances. The identification performance is evaluated by using a Taylor diagram under closed-loop and open-loop structures, where ARX and NARX structures are used for comparison. It is thereby verified that this novel proposed control approach involving a PINN-based model can adequately track the dynamics of a PMSM system, where the performance of the proposed nonlinear control is maintained even when using the identified model based on the PINN.

A simple 60‐pulse voltage source inverter using voltage reinjection strategy to improve voltage harmonics for high‐power local loads

AbstractThe voltage reinjection strategy is an effective solution to improve the voltage quality of the voltage source inverters for high‐power applications. In this article, the fundamentals of voltage reinjection strategy are analyzed to reduce voltage harmonics of the output voltage. Then, a novel voltage reinjection circuit is proposed which can generate a 5‐level voltage to be injected on the voltages of the DC sources. The reinjected voltage can create a 60‐pulse voltage waveform for a three‐phase load. Turn ratio optimization of the reinjection transformer has a significant effect on the improvement of the harmonic situation of the output voltage. Furthermore, the proposed reinjection circuit provides ZVS conditions for the main switches to reduce switching losses considerably. The simulation results indicate that the proposed reinjection circuit can reduce the output voltage THD to 3.16% without any type of filter or modulation, and the load variation does not have a considerable effect on the voltage quality. The experimental results approve the analytical trend and simulation process and also confirm the reinjection circuit performance. According to the results, the proposed method has generated a semi‐sinusoidal voltage waveform with in the laboratory.

Research on the characteristics of photovoltaic-driven refrigerated warehouse with ice storage in field under weather and load variation

The field photovoltaic refrigerated warehouse works well in pre-cooling and refrigerating fruits and vegetables in remote areas. Thus, it is crucial to ensure its long-term stable operation, particularly under the dual challenges of fluctuating solar energy supply and the unstable energy consumption required for load variation. This paper investigates the operational characteristics of a 1.5-ton photovoltaic-driven refrigerated warehouse with ice storage under varying loads and weather conditions. Results indicate that the average cooling time of the material was 26 h, under the condition of continuous material inflow into the warehouse. Additionally, 26 kg of ice were produced per day per kilowatt of solar panel capacity. Energy requirements of the device for 1.1 nights match its daily ice production. Furthermore, a maximum COP of 0.34 was achieved under photovoltaic drive. The internal temperature of the load was effectively maintained within the range of 3–4 °C, even during consecutive overcast and rainy weather. Based on this, an 8-ton field photovoltaic-driven refrigerated warehouse with ice storage was constructed. These studies provide an effective alternative refrigeration solution for remote regions, addressing their cooling needs efficiently.

A Novel 10-Watt-Level High-Power Microwave Rectifier with an Inverse Class-F Harmonic Network for Microwave Power Transmission

A novel 10-Watt-Level high-power microwave rectifier with an inverse Class-F harmonic network for microwave power transmission (MPT) is presented in this paper. The high-power microwave rectifier circuit comprises four sub-rectifier circuits, a 1 × 4 power divider, and a parallel-series dc synthesis network. The simple inverse Class-F harmonic control network serves dual roles: harmonic control and impedance matching. The 1 × 4 power divider increases the RF input power fourfold, reaching 40 dBm (10 W). The parallel-series dc synthesis network enhances the resistance to load variation. The high-power rectifier circuit is simulated, fabricated, and measured. The measurement results demonstrate that the rectifier circuit can reach a maximum RF input power of 10 W at 2.45 GHz, with a maximum rectifier efficiency of 61.1% and an output dc voltage of 23.9 V, which has a large application potential in MPT.

A deep learning deviation-based scheme to defend against false data injection attacks in power distribution systems

Defending against false data injection attacks (FDIAs) in cyber-physical power systems is crucial. Detection in power distribution systems is complex due to load variations, uncertainties, and fewer meters. Defense strategies include model-driven and data-driven approaches, but model-based methods can trigger false alarms due to threshold setting issues. The current research proposes a novel data-driven method to address threshold setting issues in detecting and localizing FDIAs in power distribution systems. First, a dataset is created by recording estimated measurement values using an unscented Kalman filter and weighted least squares across various attack scenarios. These estimated measurements are then fed into a deep artificial neural network (ANN) for binary classification to detect attacks. The output, along with the estimated measurements, is used by another ANN to localize the corrupted meter zone. This deep learning-based approach improves threshold setting over the common chi-square method. Results show that the proposed deep learning method for FDIA detection and localization outperforms a recently proposed ensemble of shallow models. The area under the curve value increases by about 5% with lower training time. The approach is also effective against previously unseen attack strategies and different feeder topologies.

The comparison study of PI and Sliding Mode control techniques for Buck-Boost converters

This paper compares the performance of a Sliding Mode Control (SMC) and Proportional-Integral (PI) controller under different voltage and load conditions. The study's findings underscore the need to account for load variations in system designs and to continuously seek system optimization irrespective of the controller type chosen. PI controller demonstrates effectiveness under certain circumstances. However, significant drops in voltage and current during abrupt load changes are obtained. On the other hand, SMC enables superior adaptability, efficiently managing voltage transients and load variations without any electrical disturbances, thereby maintaining system stability. A comparative analysis further emphasizes the SMC's superior time response and robustness against reference voltage changes. Consequently, SMC is proven to be a preferable choice over the PI controller in systems experiencing frequent voltage and load variations. However, both controllers achieve the potential for further response time optimization and stability.

A 13.56-MHz 93.5%-Efficiency Optimal On/Off Timing Tracking Active Rectifier with Digital Feedback-Based Adaptive Delay Control.

This paper presents an adaptive active rectifier with digital feedback delay controllers (DFDC) which quickly tracks optimal on/off timing against input voltage and load variations. To efficiently generate the on/off transition, the proposed active rectifier adopts dynamically controlled coarse/fine delay lines rather than using conventional power-hungry static comparators, while removing the risk of unwanted multiple driving pulses to pass transistors. DFDC conducts the dual-loop digital feedback to independently adjust on/off timing with high-speed 13.56-MHz loop bandwidth, improving the voltage conversion ratio (VCR) and power conversion efficiency (PCE). DFDC can enable real-time power-saving mode control that automatically masks clock-toggling to non-essential blocks to minimize dynamic power loss while driving power transistors. To validate the efficacy of the proposed adaptive rectifier during digital feedback and settling procedures, experiments were carried out with 0.25 μm CMOS prototype at the carrier frequency of 13.56-MHz, input voltages between 1.7 and 2.6 V, and load ranges from 0.33 to 2.2 kΩ. The proposed active rectifier employing DFDC achieves a peak PCE of 93.5% and the peak VCR of 96.3% at the output power of 12.52 mW and 2.02 mW, respectively.

Evaluation of a pilot-scale anaerobic biofilm reactor for the treatment of slaughterhouse effluents in rural areas

ABSTRACT Rural slaughterhouses often lack strict effluent treatment rules, resulting in inadequate treatment prior to release into water bodies. Slaughterhouse wastewater (SWW) is a high-strength effluent, posing risks of contamination to ecosystems and human health. To tackle the need for suitable technologies in these areas, this study evaluated a 215 L pilot-scale fixed-bed biofilm reactor (FBBR), known for its simplicity in implementation, operation, and maintenance. During the operation, an initial startup period, with the liquid fraction of fruit and vegetable waste (LF-FVW) was conducted, followed by a gradual transition to SWW, creating a co-digestion period during the substrate transition. Organic load variations (1.59, 3.35, and 4.00 kgCOD/m3·d), corresponding to flow rates of 50, 75, and 120 L/d, respectively, were implemented to address potential alterations in effluent concentration and composition in authentic rural settings. Removal rates of up to 83.69, 91.81, 87.00, and 92.00% were achieved for COD, sCOD, TS, and VS, respectively, with an average removal of 32.00% for TN, and biogas production exceeded 300 L/h at 4.00 kgCOD/m3·d. These results suggest that FBBR implementation in rural areas effectively confronts SWW treatment challenges by reducing contamination, generating biogas, and adapting to rural conditions.