Load Changes Research Articles

Voltage and frequency control of microgrid in presence of micro-turbine interfaced to matrix converter

The active and reactive load changes have a significant impact on voltage and frequency. In this paper, in order to stabilize the microgrid (MG) against load variations in islanding mode, the active and reactive power of all distributed generators (DGs), including energy storage (battery), diesel generator, and micro-turbine, are controlled. The micro-turbine generator is connected to MG through a three-phase to three-phase matrix converter, and the droop control method is applied for controlling the voltage and frequency of MG. In addition, a method is introduced for voltage and frequency control of micro-turbines in the transition state from grid-connected mode to islanding mode. A novel switching strategy of the matrix converter is used for converting the high-frequency output voltage of the micro-turbine to the grid-side frequency of the utility system. Moreover, using the switching strategy, the low-order harmonics in the output current and voltage are not produced, and consequently, the size of the output filter would be reduced. In fact, the suggested control strategy is load-independent and has no frequency conversion restrictions. The proposed approach for voltage and frequency regulation demonstrates exceptional performance and favorable response across various load alteration scenarios. The suggested strategy is examined in several scenarios in the MG test systems, and the simulation results are addressed.

Power Transformer On-Load Capacity-Regulating Control and Optimization Based on Load Forecasting and Hesitant Fuzzy Control

The operational stability of a power transformer exerts an extremely important impact on the power symmetry, balance, and security of power systems. When the grid load fluctuates greatly, if the load factor of the transformer cannot be maintained within a reasonable range, it leads to increased instability in grid operation. Adjusting the transformer capacity based on load changes is of great significance. The existing control methods for on-load capacity-regulating (OLCR) transformers have low timeliness, and the daily switching frequency of the capacity-regulating switch is not controlled. To ensure the safe and stable operation of transformers, this paper proposes a control method for OLCR transformers based on load prediction and fuzzy control. Firstly, the operating principle of OLCR transformers is analyzed, and a multi-strategy enhanced dung beetle optimizer (MSDBO) combined with a CNN−LSTM model is proposed for load forecasting. On this basis, the daily switching frequency of the capacity-regulating transformer is introduced, and hesitant fuzzy control is used to select the optimal capacity-regulating strategy relying on three factors: loss, economy, and switching frequency. Finally, simulation models are constructed using the MATLAB/SIMULINK platform and simulation analysis is conducted to verify the effectiveness and superiority of the proposed control method. For the three scenarios in this paper, the method reduces daily power loss by 28.5% to 56.3% and daily operating costs by 25.4% to 50.8%. The method used in this paper can sacrifice 3.5% to 9.2% of the loss reduction capability in exchange for reducing the number of switch operations by 28.6% to 57.1%, significantly extending the lifespan of the switches and thereby increasing the operational lifespan of the transformer.

Service Quality Assessment of Sharedautorickshaws Using Subsets-Based Confirmatory Factor Analysis

Abstract Shared mobility is changing urban transportation in India by providing transportation services without the need for ownership. Sharedautorickshaws (also called as share-autos) are a popular mode of shared mobility in the country. These informal vehicles can hold six to ten passengers and operate on a hail-to-board basis. It is important to evaluate the service quality of share-autos as they gain popularity. While research on passenger satisfaction with shared mobility services exists, studies on service quality perception related to share-autos are limited. To address this research gap, a survey was conducted with 581 shareauto users in India. The study created a Confirmatory Factor Analysisbased model with five latent variables and 22 manifest variables. The results revealed that 18 variables significantly influenced service quality. Variables that had weaker factor loading in the overall analysis were found more important when analysed for different subsets of the sample population. For instance, female-only or low-income-group respondents may prioritize different factors than the overall sample, and the ranking of factor loading changes across the subsets. The study shows that subset-based analysis can provide a more nuanced understanding of the passenger experience in share-autos, identifying potential opportunities to improve the quality of these services.

Torque balance control of underwater dual-motor system

Abstract The duocentric motor drive system is commonly employed in various fields such as the printing and dyeing paper industry and industrial production. As one of the dual-motor systems, the duocentric motor system sometimes causes torque imbalance due to sudden load changes during underwater operation. This imbalance seriously affects the coordinated operation of the duocentric motor system. Addressing these issues, this paper proposes a control approach utilizing a combination of speed and torque control to manage the duocentric motor system. Through simulation, this control technique is validated to enhance the operation capability of the duocentric motor while ensuring system stability and reliability.

An NMOS output-capacitorless low-dropout regulator with dynamic-strength event-driven charge pump

In this paper, an NMOS output-capacitorless low-dropout regulator (OCL-LDO) featuring dual-loop regulation has been proposed, achieving fast transient response with low power consumption. An event-driven charge pump (CP) loop with the dynamic strength control (DSC), is proposed in this paper, which overcomes trade-offs inherent in conventional structures. The presented design addresses and resolves the large signal stability issue, which has been previously overlooked in the event-driven charge pump structure. This breakthrough allows for the full exploitation of the charge-pump structure's potential, particularly in enhancing transient recovery. Moreover, a dynamic error amplifier is utilized to attain precise regulation of the steady-state output voltage, leading to favorable static characteristics. A prototype chip has been fabricated in 65 nm CMOS technology. The measurement results show that the proposed OCL-LDO achieves a 410 nA low quiescent current (I Q) and can recover within 30 ns under 200 mA/10 ns loading change.

A speed decoupling control method for multi-channel pressure-flow coupling system

Abstract The main engine of construction machinery often involves the compound action of multi-action, such as excavator bucket rod, shovel bucket, and moving arm. Due to the matching relationship between the constant pressure differential valve and main valve, constant power limitation of a pump, load change, and other factors, it will cause the load with low pressure to move first and the load with high pressure to move later. To solve this problem, the existing speed control (or flow control) in the industry generally adopts pre-valve or post-valve compensation load-sensitive systems, positive flow control, negative flow control, constant power control, and so on. The speed control method can solve the coarse flow distribution under multi-load conditions, but is not suitable for working conditions with high precision of flow control or flow distribution, such as speed tracking, trajectory control, etc., and cannot meet the accurate control of flow or speed. In this paper, a speed decoupling control method based on hydraulic factors (such as pressure drop, flow rate, oil temperature, hydraulic pump speed, system pressure, etc.) is proposed. Through accurate modeling and simulation technology, the accuracy of compound action flow control is improved, and the anti-interference energy of the compound action system is enhanced, which lays a foundation for hydraulic speed tracking, displacement tracking, and so on.

Improving power ramp rate of a coal-fired power plant by a bypass steam accumulator

The increasing penetration of high-volatility renewable energy sources in the power system presents higher demands for flexibility from coal-fired power plant (CFPP). To enhance the flexibility of CFPPs, researchers have conducted a significant amount of thermal-system-level research in recent years on increasing system peak shaving depth. However, the load ramp rate of CFPPs under deep peak shaving is rarely discussed, despite its significance to the overall flexibility performance of CFPPs. This paper proposes a steam accumulator storage system integrating to the turbine's bypass system. The steam accumulator charges directly with working fluid from the live steam or reheat systems and discharge to the turbine, responding quickly to power ramp commands. A steady state model and a dynamic model of the proposed system were built and validated, and the calculation shows that the proposed scheme provides a load change of +2.13 % Pe and −8.3%Pe during a round-trip with a power efficiency of 63.6 % at a unit load of 40 % THA. The unit's load increase rate under coordinated control was enhanced by 1.5 % Pe/min, reaching 3 % Pe/min, using the proposed steam accumulator without revising the original controls, and the load decrease rate reached at least 5 % Pe/min. The results indicate that the proposed system provides a straightforward, easy-to-implement, and efficient solution for enhancing the load ramp rate of CFPPs at low loads.

OPTIMAL CONTROL DESIGN FOR FREQUENCY REGULATION IN ELECTRIC POWER SYSTEM WITH LOW INERTIA

Electricity is a very important element in this era because almost all aspects of modern life depend on electricity. Therefore, electricity plays a very important role in improving people's quality of life and maintaining an efficient and productive life. An efficient and reliable electrical system is essential to ensure adequate electricity availability and maintain system reliability. Therefore, planning, designing and operating electrical systems must be carried out carefully to ensure stability, reliability and efficiency. However, a decrease in frequency in the electrical system sometimes occurs when there is a sudden change in load. This can affect system stability. Therefore, Load Frequency Control (LFC) and Linear Quadratic Regulator (LQR) analysis is needed to maintain the stability of the electrical system frequency. The combination of these two techniques, namely LFC with LQR modeling, provides a better solution for maintaining frequency stability and optimizing electrical system performance. LFC analysis regulates power generation settings automatically to compensate for fluctuations in load demand and maintain a stable frequency, while LQR is a control technique used to minimize system errors and optimize system performance. Therefore, LFC with LQR results in system performance increasing very significantly with a faster response, undershoot that can be reduced to 0.001 and a better settling time of 300s in area-1 and 450s in area-2 and rise time reaching 270s in area-1 and 405s in area-2 as well as the use of LQR can maintain the system frequency at its nominal limit and the presence of New Renewable Energy (EBT) has an effect in the form of a greater undershoot level than without EBT.

저압 유도전동기 부하 변동에 따른 역률 보상에 관한 연구

저압 유도전동기 부하 변동에 따른 역률 보상에 관한 연구

Dynamic performance and control analysis of supercritical CO2 brayton cycle for solid oxide fuel cell waste heat recovery

Due to the high-temperature operation of solid oxide fuel cell, recovering its exhaust waste heat is worthwhile. The supercritical CO2 cycle features low compression power consumption and high thermal efficiency, making it a promising power cycle. Therefore, this paper adopts the supercritical CO2 recompression cycle as the bottom cycle, combined with solid oxide fuel cell to form a joint system, further improving energy utilization. Given the long temperature response time of the solid oxide fuel cell and the variability of its exhaust waste heat with load changes, this paper innovatively proposes establishing an integrated dynamic model of the joint system, instead of directly providing heat source flow and temperature changes as in traditional research, to more accurately study the dynamic response of the cycle during solid oxide fuel cell startup and load variations. Based on this, a control strategy utilizing inventory control is proposed, and the feasibility of the control effect is verified. The research results indicate that the uncontrolled cycle may lead to safety issues with a significant increase in turbine inlet temperature when solid oxide fuel cell load increases. Under the control strategy, the turbine inlet temperature and main compressor inlet temperature are rapidly stabilized at the target values. The controlled cycle achieves higher efficiency compared to the uncontrolled cycle, particularly prominent during solid oxide fuel cell load reduction, with an efficiency increase of approximately 5.23%.

Power system load frequency management using fuzzy logic

A power system is a sizable, intricately linked network that has multiple sources and loads. The population growth causes variations and unpredictability in load demand. The utilities are responsible for meeting load demand while abiding by operational restrictions on the power system. Control of load frequency is alone in charge of providing the customers with sustainable electrical power. Its primary purpose is to control system frequency and power flow in additional areas within advised bounds by controlling tie-line power and the utility generator's output power in reaction to changes in load without compromising system frequency. The paper's recommended concept illustrates the load frequency control of two area systems using fuzzy logic, fuzzy tuned proportional integral, conventional proportional integral, and proportional integral derivative controllers. SIMULINK is used for simulation research.

Optimizing AVR system performance via a novel cascaded RPIDD2-FOPI controller and QWGBO approach.

Maintaining stable voltage levels is essential for power systems' efficiency and reliability. Voltage fluctuations during load changes can lead to equipment damage and costly disruptions. Automatic voltage regulators (AVRs) are traditionally used to address this issue, regulating generator terminal voltage. Despite progress in control methodologies, challenges persist, including robustness and response time limitations. Therefore, this study introduces a novel approach to AVR control, aiming to enhance robustness and efficiency. A custom optimizer, the quadratic wavelet-enhanced gradient-based optimization (QWGBO) algorithm, is developed. QWGBO refines the gradient-based optimization (GBO) by introducing exploration and exploitation improvements. The algorithm integrates quadratic interpolation mutation and wavelet mutation strategy to enhance search efficiency. Extensive tests using benchmark functions demonstrate the QWGBO's effectiveness in optimization. Comparative assessments against existing optimization algorithms and recent techniques confirm QWGBO's superior performance. In AVR control, QWGBO is coupled with a cascaded real proportional-integral-derivative with second order derivative (RPIDD2) and fractional-order proportional-integral (FOPI) controller, aiming for precision, stability, and quick response. The algorithm's performance is verified through rigorous simulations, emphasizing its effectiveness in optimizing complex engineering problems. Comparative analyses highlight QWGBO's superiority over existing algorithms, positioning it as a promising solution for optimizing power system control and contributing to the advancement of robust and efficient power systems.

The Injury Risk Prediction of Firefighters with Biomechanical Parameters during Single- and Double-Leg Jumps

The incidence of knee injuries during firefighter training is high, but there is a lack of research on predictive factors and risk assessment for such injuries. Biomechanical assessments can provide a better understanding of how the body’s load changes during exercise, which may alter the risk of injury. Ten firefighters were recruited for our study. Each participant completed the FMS test and the single- and double-leg jump tasks. Motion information was collected and musculoskeletal models of the participants were constructed using OpenSim 4.4 to obtain the joint angle and joint moment. The peak GRF and CoM-CoP angle were also calculated. The findings showed a significantly larger PKFM (p = 0.0195), VAFM (p = 0.0039), and peak AP GRF (p = 0.0039) during the single-leg jump. The opposite performance was observed for KFA (p = 0.0098) and MPA (p = 0.0273). A stepwise multiple linear regression analysis was used to explore the relationship between these parameters and both the FMS score and the times of injuries. The risk of injury is higher in the single-leg jump compared to the double-leg jump. The biomechanical parameters of these two jumps can be used to assess sports injuries and to provide methods and references for injury risk monitoring during firefighter occupational training.

Olgotrelvir as a Single-Agent Treatment of Nonhospitalized Patients with Covid-19

BackgroundOlgotrelvir is an oral antiviral with dual mechanisms of action targeting severe acute respiratory syndrome coronavirus 2 main protease (i.e., Mpro) and human cathepsin L. It has potential to serve as a single-agent treatment of coronavirus disease 2019 (Covid-19).MethodsWe conducted a phase 3, double-blind, randomized, placebo-controlled trial to evaluate the efficacy and safety of olgotrelvir in 1212 nonhospitalized adult participants with mild to moderate Covid-19, irrespective of risk factors, who were randomly assigned to receive orally either 600 mg of olgotrelvir or placebo twice daily for 5 days. The primary and key secondary end points were time to sustained recovery of a panel of 11 Covid-19–related symptoms and the viral ribonucleic acid (RNA) load. The safety end point was incidence of treatment-emergent adverse events.ResultsThe baseline characteristics of 1212 participants were similar in the two groups. In the modified intention-to-treat population (567 patients in the placebo group and 558 in the olgotrelvir group), the median time to symptom recovery was 205 hours in the olgotrelvir group versus 264 hours in the placebo group (hazard ratio, 1.29; 95% confidence interval [CI], 1.13 to 1.46; P<0.001). The least squares mean (95% CI) changes of viral RNA load from baseline were –2.20 (–2.59 to –1.81) log10 copies/ml in olgotrelvir-treated participants and –1.40 (–1.79 to –1.01) in participants receiving placebo at day 4. Skin rash (3.3%) and nausea (1.5%) were more frequent in the olgotrelvir group than in the placebo group; there were no treatment-related serious adverse events, and no deaths were reported.ConclusionsOlgotrelvir as a single-agent treatment significantly improved symptom recovery. Adverse effects were not dose limiting. (Funded by Sorrento Therapeutics, a parent company of ACEA Therapeutics; ClinicalTrials.gov number, NCT05716425.)

Generation Control of Multi-Area Multi-Source Power System using Fire-Fly Algorithm

This article presents automatic generation control (AGC) of an interconnected three area power system having different production sources such as hydropower, thermal and gas power plants. The control areas are provided with single reheat turbine and generation rate constraints of 3%/min. An attempt has been made to apply a Proportional derivative–Proportional integral derivative (PD–PID) cascade controller in AGC. Controller gains are optimized simultaneously using more robust and powerful evolutionary computational technique Firefly Algorithm (FFA). Performance of classical controllers such as Proportional Integral (PI) and Proportional Integral Derivative (PID) controller are investigated and compared with PD–PID cascade controller. Investigations reveal that PI, and PID provide more or less same response where as PD–PID cascade controller provides much better response than the later. The system dynamic performances are studied with 1% and 2% step load perturbation in Area1. Sensitivity analysis reveals that the FFA optimized PD–PID Cascade controller parameters obtained at nominal condition of loading, size and position of disturbance and system parameter (Inertia constant, H) are robust and need not be reset with wide changes in system loading, size, position of disturbance and system parameters.

Day-ahead load forecast based on Conv2D-GRU_SC aimed to adapt to steep changes in load

With the significant increase in the proportion of volatile new energy in the power system in recent years, the difficulty of system scheduling has increased. Accurate load forecasting is an important prerequisite for flexible scheduling. The load itself is a highly regular object that is relatively easy to predict. However, steep changes in load can cause significant deviations in load forecasting. In response to this issue, this article first selects input variables that can help the model identify steep changes in load based on Pearson correlation coefficient and the proposed “Steep change impact rate”. Then, Conv2D-Gate Recurrent Unit (Conv2D-GRU) model is built to fully extract steep changes information from inputs and achieve day-ahead load forecasting. Naive persistence, Auto regressive (AR), Gradient boosting decision trees (GBDT), Convolutional neural network (CNN), Long short-term memory (LSTM) and Gate recurrent unit (GRU) are used for comparison. Compared to Naive persistence, the Conv2D-GRU-SC resulted in a decrease of 54.08 % in Mean absolute error (MAE), a decrease of 57.58 % in Root mean square error (RMSE) and an increase of 51.31 % in the R-Square (R2).

Bacterial dynamics of the plastisphere microbiome exposed to sub-lethal antibiotic pollution

BackgroundAntibiotics and microplastics are two major aquatic pollutants that have been associated to antibiotic resistance selection in the environment and are considered a risk to human health. However, little is known about the interaction of these pollutants at environmental concentrations and the response of the microbial communities in the plastisphere to sub-lethal antibiotic pollution. Here, we describe the bacterial dynamics underlying this response in surface water bacteria at the community, resistome and mobilome level using a combination of methods (next-generation sequencing and qPCR), sequencing targets (16S rRNA gene, pre-clinical and clinical class 1 integron cassettes and metagenomes), technologies (short and long read sequencing), and assembly approaches (non-assembled reads, genome assembly, bacteriophage and plasmid assembly).ResultsOur results show a shift in the microbial community response to antibiotics in the plastisphere microbiome compared to surface water communities and describe the bacterial subpopulations that respond differently to antibiotic and microplastic pollution. The plastisphere showed an increased tolerance to antibiotics and selected different antibiotic resistance bacteria (ARB) and antibiotic resistance genes (ARGs). Several metagenome assembled genomes (MAGs) derived from the antibiotic-exposed plastisphere contained ARGs, virulence factors, and genes involved in plasmid conjugation. These include Comamonas, Chryseobacterium, the opportunistic pathogen Stenotrophomonas maltophilia, and other MAGs belonging to genera that have been associated to human infections, such as Achromobacter. The abundance of the integron-associated ciprofloxacin resistance gene aac(6’)-Ib-cr increased under ciprofloxacin exposure in both freshwater microbial communities and in the plastisphere. Regarding the antibiotic mobilome, although no significant changes in ARG load in class 1 integrons and plasmids were observed in polluted samples, we identified three ARG-containing viral contigs that were integrated into MAGs as prophages.ConclusionsThis study illustrates how the selective nature of the plastisphere influences bacterial response to antibiotics at sub-lethal selective pressure. The microbial changes identified here help define the selective role of the plastisphere and its impact on the maintenance of environmental antibiotic resistance in combination with other anthropogenic pollutants. This research highlights the need to evaluate the impact of aquatic pollutants in environmental microbial communities using complex scenarios with combined stresses.3sW_WiGiki6wh61j1XWikeVideo

Distributed power conditioning unit of large-scale space solar power station

In this paper, a multi-bus distributed Power Conditioning Unit (PCU) is proposed for the Space Solar Power Station with large scale photovoltaic (PV) array and power levels reaching MW level. In this unit, there are multiple independent PV arrays. In each PV array, there are multiple independent PV subarrays. In this paper, a V-P droop control method with adaptive droop coefficient is proposed, which modifies the droop intercept based on the bus voltage deviation and the power per unit value of the PV array. This method ensures the accuracy of bus voltage and achieves proportional distribution of power between PV arrays based on the proposed topology structure in this paper. When the load changes or the output power of the PV array fluctuates, this method can ensure that power is distributed proportionally. The principle and control method of the proposed droop control method is analyzed in this paper. The effectiveness of the method is verified through MATLAB/Simulink simulation and experiment. Simulation and experimental results show that the proposed method can achieve power distributed proportionally when load changes and PV output power fluctuates, reduce bus voltage error caused by line impedance and differences in rated power of different PV arrays, and improve the performance of PV power generation system applied to space.

Triggering of Land Subsidence in and Surrounding the Hangjiahu Plain Based on Interferometric Synthetic Aperture Radar Monitoring

In the early stages, uncontrolled groundwater extraction led to the Hangjiahu (HJH) Plain becoming one of the areas with the most severe land subsidence in China. Since the beginning of this century, comprehensive measures have been taken to control the continuous aggravation of large land subsidence patterns in some areas; however, urban land subsidence issues, influenced by various factors, still persist and exhibit complex geographical distribution characteristics. In this study, we utilized Sentinel-1A images and the SBAS-InSAR technique to capture surface deformation over the HJH Plain in Zhejiang from 16 March 2017 to 20 January 2023. Through a comparative analysis with geological conditions, changes in surface mass loading, rainfall and groundwater, and land use types, we discussed the contributions of natural and anthropogenic factors to land subsidence. Augmented with optical remote sensing images and field investigations, we conducted a correlation analysis of the land subsidence status. The preliminary findings suggest that changes in surface mass loading and short-term heavy rainfall under extreme weather conditions can lead to periodic land subsidence changes in the region. Additionally, extensive infrastructure construction triggered by urbanization has resulted in significant and sustained land subsidence deformation. The research findings play an important guiding role in formulating scientifically effective strategies for land subsidence prevention and control, as well as urban planning and construction.

Incremental Load Respiratory Muscle Training Improves Respiratory Muscle Strength and Pulmonary Function in Children with Bronchiectasis.

Participants underwent respiratory muscle training for 24 weeks. The main results were changes in respiratory muscle strength and pulmonary function indices (forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), FEV1/FVC, peak expiratory flow rate (PEF), forced expiratory flow 25-75% (FEF25-75%), and maximal midexpiratory flow 75/25 (MMEF75/25)) before, 12 weeks after, and 24 weeks after the intervention. The secondary outcomes were changes in the exercise load and work rate, exercise work, Leicester Cough Questionnaire (LCQ) scale, and Fatigue Severity Scale (FSS). Compared with before the intervention, after 24 weeks of respiratory muscle training, the maximal inspiratory pressure (MIP) and maximal expiratory pressure (MEP) were significantly enhanced (P < 0.05), while FVC, FEV1, and PEF were significantly increased (P < 0.01). FEF25-75 and MMEF75/25 values showed significant improvement compared to those before training (P < 0.05). The exercise loading, work, and exercise work rate of expiratory muscle training were significantly improved compared to those before intervention (P < 0.05). The LCQ score increased significantly (P < 0.001), and the FSS score decreased significantly (P < 0.001). Incremental load respiratory muscle training effectively improved children's lung function over the long term, improved the strength of their inspiratory and expiratory muscles, and improved their quality of life.