System Load Research Articles

A Shunt DC Electric Spring Control Strategy for MVDC Bus Voltage Stability Onboard AES

Background: The recent trend in the all-electric ship (AES) electrical systems, especially in military vessels, is to move towards medium voltage direct current (MVDC) distribution. Bus voltage instability is a major issue in direct current (DC) distribution systems. Nowadays, direct current electric springs (DCES) are extensively used in low-voltage direct current (LVDC) microgrids to address voltage instability issues. This paper extends the use of a shunt DCES to stabilize the bus voltage in an MVDC grid. The work proposes an addition to the MVDC onboard ship distribution system architecture, described in IEEE 1709, by integrating a shunt DCES with a novel control strategy to stabilize the bus voltage under various loading conditions, including propulsion motor (PM) and online pulsed power load (PPL). Methods: The shunt DCES is designed to provide current into the MVDC bus, which reduces the bus current ripple to attain a stable bus voltage with reduced ripple. A dual loop control with a battery management system (BMS) is proposed for the shunt DCES and simulated in MATLAB/Simulink. BMS is designed based on the state of charge (SOC) of the battery and bus current ripple extracted from the system's source and load side currents. The current supplied by the shunt DCES and the extracted ripple current validate the effectiveness of the proposed control. Total harmonic distortions (THDs) as a measure of voltage ripple of the MVDC bus voltage at different intervals are measured and compared for both systems, with and without shunt DCES. Results: It was observed that the shunt DCES could reduce the voltage ripple well below the permissible limit, which is 5% as per IEEE 1709. Conclusion: The proposed control strategy could attain a reduction of 68-85% in THD under peak to off-peak loading conditions with the addition of shunt DCES.

Analysis of Fracture Modes and Acoustic Emission Characteristics of Low‐Frequency Disturbed Water‐Bearing Soft Rock With Different Cyclic Initial Value

ABSTRACTIn the complex geological environment of deep mining area, water‐bearing soft rock is more prone to damage and destruction by low‐frequency disturbance. In this paper, the dynamic–static combination test was conducted on the basis of uniaxial compression test by using creep dynamic disturbance impact loading system and acoustic emission technique. The test results show that with the increase of the initial value of disturbance loading, the fracture morphology of sandstone gradually changes from a single major crack to multiple cracks coexisting, and some saturated sandstones lose the bearing capacity in the process of disturbance, presenting a cone‐shaped fracture surface. The increase of the initial value of the disturbance changes the bearing capacity of the sandstone, and the peak energy of acoustic emission reaches the maximum value when the initial value of the disturbance is 80% UCS. The results of the study can provide some reference for the stability analysis of deep water‐rich soft rock mines.

Study on the characteristics of acoustic-thermal precursors of destabilization damage in coal-rock combination bodies with different proportions

The instability and failure processes of coal-rock combinations are accompanied by the release of acoustic emission (AE) and infrared radiation (IR) signals. To investigate the characteristics of AE and IR signals during the failure process in coal-rock combinations with different ratios, and to analyze the effectiveness and applicability of the monitoring methods for these two signals in various specimen ratios. In this paper, the uniaxial compression tests were conducted on seven coal-rock combinations with different ratios by using the rock mechanics loading system and the cooperative monitoring platform of infrared and acoustic emission. The results show that (1) the AE counts for failure precursors in coal-rock combinations are positively correlated with the coal-rock ratio, whereas the AE peak counts are negatively correlated. Moreover, as the coal-rock ratio decreases, the slope of the cumulative AE count curve during the peak failure stage approaches 1. (2) During the elastic and yield stages, the maximum infrared radiation temperature (MIRT) curve fluctuates, and just before peak failure, the curve displays a distinctive “V” shape. This “V” shape becomes more pronounced as the coal-rock ratio decreases. (3) In the monitoring of damage precursors of coal-rock combinations, when the ratio of coal to rock is 1:3 or less, IR monitoring is better than AE monitoring. Conversely, when the ratio of coal to rock is greater than 1:3, AE monitoring is more suitable. Consequently, the combined monitoring of AE and IR signals can increase the reliability and precision of early warning signals for coal-rock assemblage damage precursors.

Design of photovoltaic and battery energy storage systems through load demand characterization: A case study in Thailand

The integration of photovoltaic (PV) system at behind the meter has gained popularity due to the growing trend toward environmentally friendly energy solutions. Coupling PV systems with battery energy storage systems (BESS) addresses the uncertainties of PV energy production while enhancing energy management. Load characteristics have influence on PV and BESS design both in technical and economic aspects. This paper presents a comprehensive analysis of load demand characterization methodologies tailored for the design of PV and BESS. The fundamental load properties such as daily maximum power, minimum power, and load energy, categorizing loads based on their daily and annual variation degrees are investigated. The techno-economic performance of PV-only and PV+BESS systems is analyzed, focusing on metrics such as excess energy, peak limiting performance, and economic feasibility using the levelized cost of energy (LCOE). The findings of this study reveal three distinct load characteristics based on daily variation: moderate (50–70 %), moderate to high (70–80 %), and high (80–90 %). Load profiles with moderate variation (50–70 %) demonstrate economic feasibility when sized with a PV-only system matching maximum power demand. With BESS prices reduced to less than 140 USD/kWh, these loads could also integrate a 1C-BESS sized at half capacity under the same control strategy, resulting in lower excess energy and LCOE. Conversely, loads with higher variation (>70 %) and lower self-consumption rates (<70 %) face fewer viable options for both PV-only and PV+BESS configurations, especially if BESS costs remain high. These loads require custom-designed solutions and more extensive load analysis. Through analysis, this study establishes the relationship between load characteristics and system design suitability for the loads studied. The findings underscore the importance of incorporating load demand characterization techniques in the design systems, highlighting the need for tailored approaches based on specific load characteristics and economic viability.

A hybrid experimental configuration for emissivity measurement at cryogenic temperatures

Abstract In cryogenic and high vacuum systems, radiation mode of heat transfer is the primary source of energy interaction owing to the wide temperature differentials and very low temperatures and pressures. Measurement of radiative properties such as emissivity is crucial in the design of cryogenic systems. Emissivity measurement at cryogenic temperatures requires careful consideration of the sample preparation and measurement conditions to ensure accuracy and reliability. Most experimental techniques developed so far are complex in their design and equipment, and they work only on a single measuring method. To address these challenges, this research has proposed a simpler and hybrid experimental setup that could employ both calorimetric and heat flux methods to directly measure the total hemispherical emissivity in the range 90 K–180 K. Experiments were conducted with five types of materials (black paint, stainless steel, aluminum foil, aluminized mylar foil, and copper sheet) by using both methods, and the results were validated with the literature data. The data obtained by calorimetric and heat flux methods for each sample showed a high level of consistency with each other, with a variation of only ±1.5%. Moreover, the discrepancy between the measured and literature data was within the acceptable range of 0.3%–10%. The total hemispherical emissivity of mechanically treated copper was higher than that of chemically polished copper by 11%. This simplified setup has incorporated all possible means to minimize heat leakages into and from the cryostat while maintaining accuracy. The obtained data can be used to accurately estimate the radiation heat loads in cryopumps and other cryogenic systems that use similar components.

Heat transfer of mid-deep ground source heat pump for crude oil gathering and transportation

Wax precipitation in crude oil gathering and transportation (COG&T) causes substantial decrease in oil flowability and thus blockages of pipelines, presenting high safety risks and dramatic economic loss. Conventional oil/gas blending and low-temperature transport are inefficient and energy-intensive. Geothermal energy released from abandoned oil wells may be used as renewable heat source, but remains unexplored. This study focused on feasibility of utilizing geothermal energy from abandoned oil wells by mid-deep ground source heat pump (MD-GSHP), so as to alleviate wax precipitation for short-distance COG&T. Numerical models of ground heat exchanger (GHE) were established by using finite difference method (FDM) based on validation of engineered examples. The results demonstrated that, to enhance geothermal extraction, it was essential to increase fluid flow rate, lower inlet temperature, use materials with high thermal resistance, and optimize the pipe dimensions. Additionally, the impacts of operational parameters of GHE and COG&T subsystems on MD-GSHP system performances were also analyzed. Increasing inlet temperature by 8 °C resulted in 39.5 % reduction in system load and 89.4 % increase in coefficient of performance (COP). This study provides a proof-in-concept demonstration for extracting geothermal energy from abandoned wells by MD-GSHP to mitigate wax precipitation in COG&T project.

Characteristics of Nitrogen Removal from an Integrated Fixed-Film Activated Sludge (IFAS) System and the Relationship Between Activated Sludge and Biofilm Interactions

In order to investigate the enhancement mechanism of modified three-dimensional elastic filler (MTEF) on the nitrogen removal performance of the integrated fixed-film activated sludge (IFAS) process, and to clarify the interactions between competition and synergy between activated sludge and biofilm in the IFAS system, an IFAS reactor (T2) filled with MTEF was employed for the study, while a sequencing batch reactor activated sludge process (SBR) reactor (T1) was utilized for comparison. IFAS and SBR reactors were operated over an extended period at ambient temperature to assess the enhancement of pollutant removal performance with the addition of the filler to investigate the competitive dynamics between activated sludge and biofilm under varying influent water qualities (C/N, N/P, and organic loading), and to analyze the synergistic relationship between activated sludge and biofilm at the microbial level using high-throughput sequencing technology. The results demonstrate that throughout the entire operational phase, reactor T2 exhibited superior pollutant removal efficiency. Compared to reactor T1, reactor T2 achieved an average increase in the removal rates of COD, ammonia nitrogen, and total nitrogen by 13.07%, 12.26%, and 28.96%, respectively. The findings on the competitive dynamics between activated sludge and biofilm indicate that the nitrification volumetric load of the IFAS system is significantly higher than that of a pure activated sludge system, suggesting that the IFAS system possesses enhanced nitrification capabilities. Furthermore, when dealing with wastewater characterized by low C/N ratios and high phosphorus pollution, or under substantial organic loads, the biofilm holds a competitive edge and the IFAS system exhibits improved stability. High-throughput sequencing data reveal that the microbial community structures in activated sludge and biofilm can influence each other, thereby enabling the IFAS system to effectively enrich denitrification-related functional microbial populations. Additionally, the biofilm has a certain enhancing effect on the expression levels of nitrogen metabolism-related functional genes in the activated sludge phase microorganisms, indicating that, in addition to competitive interactions, there is also a synergistic effect between the biofilm and activated sludge.

Construction method for novel power load management system detection environment based on model adaptive equivalence

With the focus on integrating large-scale clean energy, the requirements for demand-side load resource management and maintaining the power system's supply-demand balance have increased. New types of power load management are thus gaining importance. Considering the varied load management needs and response strategies across different scenarios, and taking into account essential network elements like topology and connectivity, a multi-level interactive architecture is established to support hierarchical load control networking models for energy flow. By leveraging statistical analysis and simulation deduction, this approach improves the comprehensive evaluation of load system indicators, particularly in scenarios with small sample sizes and significant randomness. This methodology contributes to more efficient power system management and enhances load control adaptability.

Reducing Peak Power in a Multiple Load System by Delaying the Activation of Electrical Loads Using a Filter Based on a PI Controller

In a power grid with multiple two-state loads, the total power can vary over a significant range. This results in the inability to supply this system from a low-power source. The solution is an algorithm that shapes energy demand depending on its availability. For this purpose, a new load distribution method is proposed based on introducing a buffer between the temperature controller output and the heater and filtering the load using a master Proportional–Integral (PI) controller. The aim of the work was to evaluate the quality of the developed algorithm for limiting power peaks in the power grid. The research was conducted on a model of the Creep Test Laboratory with 389 heaters simulated in MATLAB Simulink R2023b. The algorithm was tested with various settings of the master controller parameters. By experimentally adjusting these parameters, a ten-fold reduction in peak power was achieved. The standard deviation for the L1 phase was reduced from 7.6 kW to 0.6 kW. Similar results were obtained for phases L2 and L3. The tested control system tracked changes in the average power value by changing the number of loads switched on and by frequency-modulating the signal when the change was less than the power of a single load. It was demonstrated that the controlled delayed switching of electrical loads can modify the shape of the total electric power without affecting their operation. The proposed solution features a low computational complexity, which allows its implementation in various systems.

Design, Evaluation & Implementation of a Novel MRI-compatible Physiologic Loading Simulator for Ex-Vivo Joints.

Quantitative magnetic resonance imaging (qMRI), in combination with mechanical testing, offers potential to investigate how loading is related to joint and tissue function. However, current testing devices compatible with MRI are often limited to uniaxial compression, often applying low loads, or loading individual tissues (instead of multiple), while more complex simulators do not facilitate MRI. Hence, in this work, we designed, built and tested (N=1) an MRI-compatible multiaxial load-control system which enables scanning cadaveric joints (healthy or pathologic) loaded to physiologically-relevant levels. Testing involved estimating and validating physiologic loading conditions before implementing them experimentally on cadaver knees to simulate and image gait loading (stance and swing). The resulting design consisted of a portable loading device featuring pneumatic actuators to reach a combined loading scenario, including axial compression (=2.5 kN), shear (=1 kN), bending (=30 N·m) and muscle tension. Initial laboratory testing was carried out; specifically, the device was instrumented with force and pressure sensors to evaluate loading and contact response repeatability in one cadaver knee specimen. This loading system was able to simulate healthy or pathologic gait with reasonable repeatability (e.g., 1.23 to 2.91 % coefficient of variation for axial compression), comparable to current state-of-the-art simulators, leading to generally consistent contact responses. Contact measurements demonstrated a tibiofemoral to patellofemoral load transfer with knee flexion and large contact pressures concentrated over small sites between the femoral cartilage and menisci, agreeing with experimental studies and numerical simulations in the literature.

Simulation of 1 MWe hybrid solar power plant by the use of nano-fluid with eccentric backup system.

In past years, concentrated solar power (CSP) with an energy backup system has been a unique renewable energy utilization system among intermittent renewable energy systems. It could allow a CSP plant to operate as a base load system in the future. This paper simulates a solar power plant for 1 MWe. Parabolic trough collector (PTC) array and linear Fresnel reflector (LFR) field attached consecutively to produce superheated steam at 40MPa. The Rankine cycle has been used to run the steam turbine and an electricity generator is attached to a steam turbine to convert mechanical energy into electrical energy. Maximum temperature attained at the turbine inlet is 418.13 ˚C in 12:00-13:00 time slot in the month of January. Results show that solar power plant is feasible to produce 1 MWe. The minimum value of the power produced by the generator is 1.01 MWe in November in the 10:00-11:00 time slot whereas the maximum value of generated power is 1.57 MWe in December in the 11:00-12:00 time slot. The overall efficiency of power generated by the Rankine cycle is 21.25% in January in the 10:00-11:00 time slot. An energy storage system is attached to the system to work at night hours or in cloudy weather conditions.

How large language model-powered conversational agents influence decision making in domestic medical triage contexts

IntroductionEffective delivery of healthcare depends on timely and accurate triage decisions, directing patients to appropriate care pathways and reducing unnecessary visits. Artificial Intelligence (AI) solutions, particularly those based on Large Language Models (LLMs), may enable non-experts to make better triage decisions at home, thus easing the healthcare system's load. We investigate how LLM-powered conversational agents influence non-experts in making triage decisions, further studying different persona profiles embedded via prompting.MethodsWe designed a randomized experiment where participants first assessed patient symptom vignettes independently, then consulted one of the two agent profiles—rational or empathic—for advice, and finally revised their triage ratings. We used linear models to quantify the effect of the agent profile and confidence on the weight of advice. We examined changes in confidence and accuracy of triage decisions, along with participants' perceptions of the agents.ResultsIn a study with 49 layperson participants, we found that persona profiles can be differentiated in LLM-powered conversational agents. However, these profiles did not significantly affect the weight of advice. Notably, less confident participants were more influenced by LLM advice, leading to larger adjustments to initial decisions. AI guidance improved alignment with correct triage levels and boosted confidence in participants' decisions.DiscussionWhile LLM advice improves triage recommendations accuracy, confidence plays an important role in its adoption. Our findings raise design considerations for human-AI interfaces, highlighting two key aspects: encouraging appropriate alignment with LLMs' advice and ensuring that people are not easily swayed in situations of uncertainty.

Power quality improvement of grid‐connected solar power plant systems using a novel fractional order proportional integral derivative controller technique

AbstractRecently, there has been a push to integrate renewable energy system (RES) into grid‐connected load system in enhancing reliability and reducing losses. However, integrating these systems introduces power quality (PQ) issues, especially with non‐linear, critical, and imbalanced loads. Addressing this, a hybrid mantis search‐reptile search algorithm (HMS‐RSA) combined with a unified power quality conditioner (UPQC) to mitigate PQ problems related to current and voltages in RES systems. In other words, the UPQC, enhanced by fractional order proportional integral derivative controller parameters tuned using the proposed HMS‐RSA assists in enhancing the power quality. The approach has been validated by connecting a non‐linear load to the system, which typically creates PQ issues. The proposed method is implemented in MATLAB/Simulink and their performance is analysed in three scenarios, such as sag, swell, and disturbance, and the total harmonic distortion is evaluated to quantify improvements in PQ. Finally, the proposed method is compared with existing approaches, such as ant colony optimization (ACO), artificial bee colony optimization (ABC), and bacterial foraging optimization (BFO). The method also outperforms ACO, ABC, and BFO in terms of convergence speed and effectiveness in mitigating PQ issues.

GRU-IKF scene taxiing trajectory prediction model based on attention mechanism

Aircraft taxi trajectory prediction helps solve operational problems such as airport taxiing conflicts and long waiting times, ensuring airport safety while improving service levels and increasing airport throughput. In view of the fact that the performance of machine learning models depends on good data sets, a method for predicting taxi trajectories of aircraft on the ground based on attention mechanism, fusion of gated recurrent unit (GRU) and improved Kalman filter algorithm (IKF) is proposed. Firstly, three independent gated recurrent unit networks are used to capture the motion state and temporal dependency of aircraft at future moments, and the attention mechanism is introduced to enhance the ability to extract data difference features and learn the mapping relationship from input to output; then, it is fused with the improved extended Kalman filter to integrate the output results of the neural network into the state prediction and update process to improve the accuracy of the predicted trajectory sequence. Finally, the effectiveness of the model was verified using the actual taxiing trajectory of aircraft at Lukou Airport. The simulation results show that the model can effectively and accurately predict the taxiing trajectory of aircraft on the surface, with an overall mean square error of about 0.00128. Compared with the single recurrent neural network (RNN), long short-term memory network (LSTM) and GRU model, the RMSE is reduced by 72.9%and respectively 39.9%, 54.7%and the prediction time is 40 ms. It can accurately and quickly predict the taxiing trajectory, which helps to reduce the operating load of the airport surface management system.

Fusion Forecasting Algorithm for Short-Term Load in Power System

Fusion Forecasting Algorithm for Short-Term Load in Power System

Research on power metering method for nonlinear loads in power system based on improved S-transform

Abstract Traditional power metering algorithms are no longer suitable for new power systems to address the issue of distorted voltage and current waveforms caused by a growing number of nonlinear loads in modern power systems. The paper proposes a power-metering algorithm based on an Improved Stockwell Transform (IST). Based on the Stockwell transform, several parameters that can control the Gaussian window are introduced to improve the flexibility of the Gaussian window. Additionally, the inequality constraint based on the energy concentration measure is used to optimally select the best window parameters, enhancing the time–frequency domain analysis capability of the IST. The study uses IST to decompose and reconstruct the voltage and current fundamental characteristic signals and distortion characteristic signals in the power system. This provides good time resolution at low frequencies and good frequency-domain resolution at high frequencies. Power metering based on unsteady voltage and current is achieved by calculating the resulting fundamental and harmonic powers, respectively. The simulation results demonstrate that the IST-based power metering method has a higher level of accuracy than the existing metering methods.

Impact of Organic Load on Methane Yields and Kinetics during Anaerobic Digestion of Sugarcane Bagasse: Optimal Feed-to-Inoculum Ratio and Total Solids of Reactor Working Volume

The effect of increasing organic load on the specific methane yields (SMYs) and kinetics of methane production during the anaerobic digestion (AD) of sugarcane bagasse (SB) was investigated in batch experiments at 37 °C. The organic load of the batch AD system was increased based on an increase in the feed-to-inoculum (F/I) ratio (T1–T5) and increase in the Total Solids (TS)% of the working volume (T6–T10). The results show that in both the treatment sets, an increase in organic load led to a decrease in SMY. Higher organic loads in terms of F/I ratio (T4 and T5) were inhibited due to Volatile Fatty Acid (VFA) accumulation. On the other hand, higher organic loads (T8, T9 and T10) in terms of the higher TS% of the working volume was inhibited by the accumulation of NH4-N. Thus, an organic load of 50 gVS/L at an F/I ratio = 1.0 and TS = 10% (T3) was found to be the highest organic load that had no significant inhibitions among the tested treatments. The results from the kinetic studies show that the first-order kinetic model is the best fit for the SMY data, with average differences% of 2.32% and 3.13% for treatments T1–T5 and T6–T10, respectively.

ERTH scheduler: enhanced red-tailed hawk algorithm for multi-cost optimization in cloud task scheduling

Effective task scheduling has become the key to optimizing resource allocation, reducing operation costs, and enhancing the user experience. The complexity and dynamics of cloud computing environments require task scheduling algorithms that can flexibly respond to multiple computing demands and changing resource states. Therefore, we propose an enhanced Red-tailed Hawk algorithm (named ERTH) based on multiple elite policies and chaotic mapping, while applying this approach in conjunction with the proposed scheduling model to optimize the efficiency of task scheduling in cloud computing environments. We apply the ERTH algorithm to a real cloud computing environment and conduct a comparison with the original RTH and other conventional algorithms. The proposed ERTH algorithm has better convergence speed and stability in most cases of small and large-scale tasks and performs better in minimizing the task completion time and system load cost. Specifically, our experiments show that the ERTH algorithm reduces the total system cost by 34.8% and 36.4% relative to the traditional algorithm for tasks of different sizes. Further, evaluations in the IEEE Congress on Evolutionary Computation (CEC) benchmark test sets show that the ERTH algorithm outperforms the traditional or emerging algorithms in several performance metrics such as mean, standard deviation, etc. The proposal and validation of the ERTH algorithm are of great significance in promoting the application of intelligent optimization algorithms in cloud computing.

Optimal Scheduling of Integrated Energy System Considering Virtual Heat Storage and Electric Vehicles

Integrated energy systems (IESs) are complex multisource supply systems with integrated source, grid, load, and storage systems, which can provide various flexible resources. Nowadays, there exists the phenomenon of a current power system lacking flexibility. Thus, more research focuses on enhancing the flexibility of power systems by considering the participation of IESs in distribution network optimization scheduling. Therefore, the optimal scheduling of IESs considering virtual heat storage and electric vehicles (EVs) is proposed in this paper. Firstly, the basic structure of IESs and mathematical models for the operation of the relevant equipment are presented. Then, an optimal scheduling strategy of an IES considering virtual heat storage and electric vehicles is proposed. Finally, an IES with an IEEE 33-node distribution network, 20-node Belgian natural gas network, and 44-node heating network topologies is selected to validate the proposed strategy. The proposed models of integrated demand response (IDR), EV orderly charging participation, virtual heat storage, and actual multitype energy storage devices play the role of peak shaving and valley filling, which also helps to reduce the scheduling cost from CNY 11,253.0 to CNY 11,184.4. The simulation results also demonstrate that the proposed model can effectively improve the operational economy of IESs, and the scheduling strategy can promote the consumption of renewable energy, with the wind curtailment rate decreasing from 63.62% to 12.50% and the solar curtailment rate decreasing from 56.92% to 21.34%.

Resource‐adaptive and OOD‐robust inference of deep neural networks on IoT devices

AbstractEfficiently executing inference tasks of deep neural networks on devices with limited resources poses a significant load in IoT systems. To alleviate the load, one innovative method is branching that adds extra layers with classification exits to a pre‐trained model, enabling inputs with high‐confidence predictions to exit early, thus reducing inference cost. However, branching networks, not originally tailored for IoT environments, are susceptible to noisy and out‐of‐distribution (OOD) data, and they demand additional training for optimal performance. The authors introduce BrevisNet, a novel branching methodology designed for creating on‐device branching models that are both resource‐adaptive and noise‐robust for IoT applications. The method leverages the refined uncertainty estimation capabilities of Dirichlet distributions for classification predictions, combined with the superior OOD detection of energy‐based models. The authors propose a unique training approach and thresholding technique that enhances the precision of branch predictions, offering robustness against noise and OOD inputs. The findings demonstrate that BrevisNet surpasses existing branching techniques in training efficiency, accuracy, overall performance, and robustness.