Load Changes Research Articles

Time Series Analysis in Electrical Load Forecasting

Time series analysis in electricity load projection is a key part of the energy sector because it makes it possible to control and distribute energy more efficiently. It is important to have accurate load forecasts in order to balance supply and demand, make power systems run more efficiently, and lower operating costs. This essay covers a wide range of ways that time series analysis can be used to guess how much electricity will be used. The study looks at a number of different models, such as the autoregressive integrated moving average (ARIMA), exponential smoothing, and machine learning methods like Long Short-Term Memory (LSTM) networks. These models are judged by how well they can show the patterns and time relationships that are common in electricity load data. A lot of attention is paid to the problems that come up because of non-stationarity, timing, and outside factors like weather that cause load changes. The study talks about how feature selection, data pre-processing, and model evaluation can help make forecasts more accurate. The study shows the trade-offs between model interpretability and forecasting power by comparing how well standard statistical methods and new machine learning techniques work. The study also talks about how mixed models, which take the best parts of more than one method and combine them, might help make forecasts more accurate. The results make it clear how important time series analysis is for building accurate load predicting models, which are needed to keep modern power systems reliable and efficient.

Exploring the impact of deglaciation on fault slip in the Sangre de Cristo Mountains, Colorado, USA

Few natural examples exist where climate’s influence on tectonics is clear. Based on a study of the Sangre de Cristo Mountains in southern Colorado, we argue that climate-driven changes in ice loads affected spatial and temporal slip patterns on the range-front normal fault. Relict glacial features enable the reconstruction of paleoglacier extents and show variable amounts of footwall ice coverage during the Last Glacial Maximum (LGM). Line load models indicate post-LGM ice melting reduced fault clamping stress by ∼20−55 kPa at seismic depths. Flexural isostatic modeling shows several meters of footwall uplift due to ice unloading with spatial patterns and magnitudes consistent with post-LGM fault throw measured from offset Holocene and late Pleistocene alluvial fans. Post-LGM fault throw rates are at least a factor of five higher than middle and early Pleistocene rates. We infer that climate-modulated ice-load changes can pace fault clamping stress and slip patterns on range-bounding normal faults.

Characteristics of Sudden Change in Aerodynamic Load of High-Speed Train Caused by Wind Barrier and Its Buffer Measure

When high-speed trains (HSTs) rapidly enter or exit the wind barrier area of the bridge, the quick change in the operating environment can lead to sudden changes in train aerodynamic loads, resulting in the deterioration of their aerodynamic performance and adversely affecting safe and stable operation. In this paper, the effects of wind barrier porosity, crosswind speed, and train speed on the sudden change in the aerodynamic load of the HST induced by wind barriers are analyzed, and the reasons for the sudden change from a flow field perspective are given. Additionally, the influences of the buffer structure with three lengths of 45 m, 90 m, and 135 m on the sudden change in the aerodynamic load of HSTs are studied. The results show that the lower the porosity of the wind barrier, the higher the crosswind speed, and the lower the speed of trains entering and exiting the wind barrier area, resulting in a greater degree of sudden change in the aerodynamic load of the HST. The buffer structure measuring 90 m in length is considered the most suitable, as it can significantly alleviate the sudden change in the aerodynamic load and effectively enhance the safety of train operations.

Koala ocular disease grades are defined by chlamydial load changes and increases in Th2 immune responses

IntroductionThis study employs bulk RNA sequencing, PCR, and ELISA assays to analyze the pathological factors affecting the outcomes of C. pecorum ocular infections in koalas. It investigates the immune responses and gene expression profiles associated with various stages of koala ocular chlamydiosis.MethodsA cohort of 114 koalas from Queensland, Australia were assessed, with 47% displaying clinical signs of ocular disease. Animals were classified into three cohorts: acute active disease (G1), chronic active disease (G2), and chronic inactive disease (G3), along with subclinical Chlamydia pecorum positive (H2) and healthy (H1) cohorts.ResultsAnalysis of clinical, microbiological, humoral immune and cellular immune biomarkers revealed varying chlamydial loads and anti-chlamydial IgG levels across disease grades, with a negative correlation observed between ocular chlamydial load and anti-chlamydial IgG. Koala ocular mucosa gene expression analysis from 27 koalas identified shared expression pathways across disease cohorts, with a significant upregulation of IFNγ expression and tryptophan metabolism in all disease stages.DiscussionThese findings help elucidate immune response dynamics and molecular pathways underlying koala ocular chlamydiosis, providing insights crucial for disease management strategies.

Integral Sliding Mode‐Composite Nonlinear Feedback Control Strategy for Microgrid Inverter Systems

To enhance the dynamic performance and robustness of the voltage control system of islanded microgrid inverters, a new control strategy combining integral sliding mode (ISM) control and composite nonlinear feedback (CNF) control is proposed. In ISM control, firstly, a new reaching law is designed to improve movement quality in the reaching phase by improving the power term and introducing the inverse tangent function. Then, to improve disturbance observation accuracy, a variable gain extended state observer is designed by improving the regulation mechanism of the state variables according to deviation control. Using the idea of variable damping, linear and nonlinear feedback are combined into CNF control. Specifically, linear feedback provides a small damping ratio for faster system response, while nonlinear feedback increases the damping ratio to improve steady‐state performance. Simulation results show that the integral sliding mode‐composite nonlinear feedback (ISM‐CNF) control strategy cam balances convergence speed and chattering better and achieves higher steady‐state accuracy than conventional strategies. Moreover, ISM‐CNF control has better robustness to reference voltage variations and disturbances caused by sudden load changes. Therefore, the ISM‐CNF control strategy can accomplish voltage control of islanded microgrid inverters quickly and steadily, effectively suppressing system disturbances and enhancing stability and power quality. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Wear Calculation for a Shaft Lip Seal under Randomly Changing Temperature and Load

A stochastic model of the wear process of a shaft lip seal that takes into account random changes in the temperature and external load was described. The results of a numerical analysis of the process of seal wear in relation to the conditions of operation in open space at near-Earth orbital stations are presented. The importance of taking into account random changes in temperature and external load for predicting the seal durability was evaluated.

Load frequency and virtual inertia control for power system using fuzzy self-tuned PID controller with high penetration of renewable energy

AbstractReliance on renewable energy is increasing, and generating units are being added to the network. Since renewable power fluctuates greatly, the frequency deviation of the grid becomes a crucial problem with access to renewable power generation. The fluctuation of the renewable power output of the system puts forward a higher demand for load frequency control of the power grid to increase the penetration of renewable power in the system. PID controller has proven its effectiveness for the LFC due to its simple structure and clear concept. In this article, the virtual synchronous generator is introduced and a fuzzy self-tuned PID controller is proposed for inertia control. The proposed controller is implemented in light of the significant integration of renewable energy and virtual inertia. The efficacy of the suggested controller is evaluated against the traditional PID controller for the Egyptian Power System as a case study under various load disturbance scenarios. The control technique is employed for variable loads with photovoltaic and wind turbine generation systems. Three instances of load changes are studied and the controller design is performed based on grey wolf optimizer in each case. The overshot and integral time absolute error are considered as comparison measures. The new contribution is applied to the proposed controller for the grid and virtual inertia. In the case of many load variations imposed, the disturbances of residential and industrial loads varied from 0.05, 0.01, 0.15, and 0.02 pu. The maximum overshoot is 0.005 for the proposed controller and 0.0078 for the classic PID controller. The integral time absolute error is 0.06429 for the proposed controller and 0.11481 for the classic PID controller. The results demonstrate the efficacy of the proposed controller for inertia control with high penetration of renewable energy. The results show that the proposed fuzzy self-tuned PID controller has an overshot less than the classical PID controller by 25% and integral time absolute error by 45%. These results show that the use of the proposed fuzzy self-tune controller for the grid and inertia gave a better performance in terms of the overshot value and the integral time absolute error.

Effects of Controlling Assist Robots to Follow Lumbar Load on Muscle Fatigue

ABSTRACTWe developed an assist robot that changes its assist force in real time according to the lumbar load estimated from the load information on the hand measured using a hand sensor device and the posture information. Furthermore, using the developed assist robot, the effect of the load‐following control on muscle fatigue was verified. The load was set at 6 and 1 kgf, and the amount of muscle activity in the lumbar region was measured using a muscle potential sensor during continuous flexion–extension exercises. The central frequency of the power spectrum was calculated as muscle fatigue, and its time trend was obtained. For comparison, similar experiments were also conducted without an assistive robot and with an existing assistive robot. Consequently, when load‐following control was used, muscle fatigue was reduced compared with existing assist robots in which the assist force was excessive in relation to the load. This shows the importance of load‐following control that is necessary to control the assist force according to the load when the assist robot is worn during work in which the load changes in a complex manner.

Recent advances in LDO chip research

Abstract. With the rapid development of integrated circuit technology and the wide application of Internet of things devices, the demand for power management chips is increasing. Low-dropout regulators (LDO) play an important role in modern electronic devices. It provides a stable low output voltage while maintaining low power consumption. Recently some progress has been made in the research of LDO chips, focusing on improving power efficiency, reducing static current, and enhancing transient response. The study shows that the static current has been significantly reduced by using the subthreshold MOS tube and dynamic current bias technology. In terms of power supply rejection ratio (PSRR), by improving the circuit design, the PSRR of LDO can be maintained above -80 dB under various conditions. In addition, improvements to the transient response result in significantly optimized overshoot and recovery times of the output voltage, ensuring voltage stability when the load changes rapidly. These advances enable LDO chips to meet the demanding requirements of high-performance and Internet of things device applications. Future challenges include further reducing static current, improving PSRR performance in high-frequency environments, and effectively addressing thermal management issues.

Multi-Time Scale Optimal Control of Voltage Fluctuation at PV Grids Considering Load Changes

Multi-Time Scale Optimal Control of Voltage Fluctuation at PV Grids Considering Load Changes

Study on the correlation between the level of insulin resistance and changes in the degree of cerebral atherosclerosis in non-diabetic patients.

Cerebral infarction is a health problem of global concern and brings a particular burden to medical treatment. To analyze the correlation between insulin resistance (IR) levels and changes in cerebral atherosclerosis (AS) degree in non-diabetic patients with cerebral infarction. A total of 134 non-diabetic patients with cerebral infarction who visited the Department of Neurology of our hospital from May 2019 to October 2020 were selected and underwent MRA/CTA (Magnetic resonance angiography/Computed tomography angiography) of cerebral arteries to refine the cerebrovascular imaging data, and according to the results of cerebral AS load, the patients were divided into mild AS group and severe AS group, and the insulin resistance index was calculated with HOMA-IR (homeostasis model assessment of insulin resistance) to evaluate the IR level and HOMA-IR was compared between the two groups. Spearman correlation was used to analyze the correlation between the levels of IR in patients and the changes in cerebral AS load. 54 individuals had severe AS and 80 patients had mild AS, according to an MRA/CTA of the cerebral arteries. There was a significant difference (P< 0.05) in HOMA-IR between the difficult and gentle AS groups. A significant link between HOMA-IR and the severity of cerebral AS in patients was found using Spearman correlation analysis (r= 0.850, P< 0.05). The IR phenomenon was prevalent in non-diabetic patients with cerebral infarction, and the level of IR was closely related to the severity of cerebral AS.

Microbial metabolism disrupts cytokine activity to impact host immune response

Host-pathogen interactions are shaped by the metabolic status of both the host and pathogen. The host must regulate metabolism to fuel the immune response, while the pathogen must extract metabolic resources from the host to enable its own survival. In this study, we focus on the metabolic interactions of Mycobacterium abscessus with Drosophila melanogaster. We identify MAB_1132c as an asparagine transporter required for pathogenicity in M. abscessus. We show that this requirement is specifically associated with damage to the host: flies infected with MAB_1132c knockout bacteria, or with wild-type bacteria grown in asparagine-restricted conditions, are longer lived without showing a significant change in bacterial load. This is associated with a reduction in the host innate immune response, demonstrated by the decreased transcription of antimicrobial peptides as well as a significant reduction in the ability of the infection to disrupt systemic insulin signaling. Much of the increase in host survival during infection with asparagine-limited M. abscessus can be attributed to alterations in unpaired cytokine signaling. This demonstrates that asparagine transport in M. abscessus prior to infection is not required for replicative fitness in vivo but does significantly influence the interaction with the host immune responses.

The Sexual Dimorphism of the Neuroimmune Response in the Brains of Taenia crassiceps-Infected Mice

Background: Helminth infections are associated with cognitive deficits, especially in school-age children. Deworming treatment in heavily infected children improves their short- and long-term memory recall. In mice, intraperitoneal helminth infection with Taenia crassiceps (T. crassiceps) shows sexual dimorphism in terms of the parasite load, immune response, hormone levels, and behavioral changes. We have previously shown poorer short-term memory performance and changes in the concentrations of cytokines and neurotransmitters in the hippocampus, which were replicated in this study. The molecular changes in other brain structures, such as those related to reproduction, are unknown. Methods: Male and female Balb/cAnN mice were chronically infected with T. crassiceps larvae. We determined the peritoneal parasite load and established the presence of cytokines and neurotransmitters in the hippocampus, olfactory bulb, and hypothalamus. Results: The parasite load was higher in female than male infected mice, as expected. In the hippocampus, the neurotransmitters norepinephrine and serotonin increased in males but decreased in females. In contrast, in the olfactory bulb and hypothalamus, the neurotransmitters assessed showed no statistical differences. The cytokine profiles were different in each brain structure. The TNF-α levels in the olfactory bulb and the IL-4 levels in the hippocampus of infected mice were dimorphic; IFN-γ was augmented in both male and female infected animals, although the increase was higher in infected males. Conclusions: The brain responds to peripheral infection with cytokine levels that vary from structure to structure. This could be a partial explanation for the dimorphic behavioral alterations associated with infection, it also demonstrates the synergic interaction between the immune, the endocrine, and the nervous systems.

FUZZY-PROPORTIONAL INTEGRAL DERIVATIVE CONTROLLER WITH INTERACTIVE DECISION TREE

The STATCOM is extensively used in the power system to address the power quality issues by actively compensating the reactive power requirements of the load. However, the STATCOM performance depends on the underlying controller operation to handle sudden load changes and disturbances optimally with faster response. To solve this issue, this paper presents the intelligent algorithm-optimized fuzzy rule-based Proportional Integral Derivative (PID) controller to enhance the performance of the STATCOM. The proposed controller consists of a fuzzy-PID controller capable of handling non-linear dynamics and sudden changes in the load. The interactive decision tree (IDT) technique detects weaker metrics and improves their influence in control scenarios. The Biogeography-based optimization (BBO) algorithm minimizes the controller's integral absolute error to tune the fuzzy-PID controller parameters. The algorithm terminates once all the metrics are tuned to satisfaction with the operator’s consent. The proposed IDT-based fuzzy PID controller is tested on a power system containing a nonlinear load, and its performance is compared with the existing controller and simulated using the MATLAB/Simulink tool. The proposed controller provides fast control action, reduces the reactive power from the source by 42.5%, and improves the power factor by 1.5%.

RBFNN based fixed time sliding mode control for PEMFC air supply system with input delay

Ensuring rapid regulation of the oxygen excess ratio (OER) in proton exchange membrane fuel cells (PEMFC) during load changes is an important challenge. In this paper, fixed time sliding mode control of a PEMFC with input delay has been investigated. First, a simplified fourth-order nonlinear dynamical model with input disturbance and input delay is considered and a cascade structure is selected for the control design. A radial basis function neural network (RBFNN) is designed to estimate the input disturbance. To achieve precise estimation, a Cuckoo Search Algorithm is utilized to calculate the parameters of the RBFNN. Then, a new sliding mode controller is proposed for trajectory tracking within a fixed time. To ensure the effectiveness of the proposed controller, the fixed time convergence of both sliding and reaching phases is investigated and proven. Finally, a robust prediction based sliding mode control is designed for the PEMFC system that by incorporating the disturbance estimation, can eliminate the effect of input delay. The effectiveness and robustness of the proposed controller are validated via comparative simulations. It is noteworthy that this is the first study to propose predictor based control for input delay PEMFCs.

Exploring the role of nanoparticle additives in reducing emissions in compression ignition engine

The paper discusses the growing environmental issues caused by vehicle emissions, leading many countries to tighten their emission standards to reduce the harmful gases released by motor vehicles. This is especially important as urbanization has increased, leading to more consumption of petroleum products. As a result, there is a growing need for cleaner, more sustainable fuel alternatives. Biodiesel has been recognized as a potential solution since it is renewable, non-toxic, and less environmentally harmful than traditional diesel. The study in question explores how adding biodiesel and nanoparticles affects diesel engine performance, particularly in terms of noise and particulate matter emissions (small particles of pollution). The research was carried out using a compression ignition engine (like those found in diesel vehicles) under constant RPM (revolutions per minute) and variable loads (changes in engine stress or power requirements). The results showed that when biodiesel was blended with nanoparticles and used as fuel, there was a noticeable reduction in noise levels, carbon dioxide (CO2), and particulate matter emissions compared to regular diesel. This suggests that biodiesel, especially when enhanced with nanoparticles, can effectively lower the harmful effects of diesel engines on the environment, offering a greener and quieter alternative.

Advanced Sliding Mode Design for Optimal Automatic Generation Control in Multi-Area Multi-Source Power System Considering HVDC Link

The multi-area multi-source power system (MAMSPS), which uses a variety of power sources including gas, hydro, thermal, and renewable energy, has recently been implemented to balance the growing demand for electricity and the overall capacity for power generation. In this paper, an integral sliding mode control with a single-phase technique (ISMCSP) is applied to two areas, with each area including gas–wind–thermal power systems with HVDC system. Firstly, a two-area gas–wind–thermal power system with HVDC (TAGWTPSH) is the first model in this scheme to consider the parameter uncertainties of a MAMSPS. Secondly, sliding mode design law with a single-phase technique is introduced to alleviate chattering and oscillation problems. Then, power system stability is ensured by the Lyapunov control theory based on the new LMIs technique. Thirdly, the ISMCSP’s effectiveness in a MAMSPS is also assessed under random load patterns and parameter variations regarding settling time and over-/undershoot. The ISMCSP was created to alter the fundamental sliding mode control, and therefore the suggested approach performs better than recently published approaches. This is demonstrated by the frequency overshoot deviation value in frequency deviations: 0.7 × 10−3 to 2.8 × 10−3 for the TAGWTPSH with the suggested ISMCSP. In the last case, for random changes in load from −0.4 to +0.5 p. u, the proposed ISMCSP method still stabilizes the frequency of the areas meeting the standard requirements for AGC.

Optimization of chemical engineering control for large-scale alkaline electrolysis systems based on renewable energy sources

In the context of renewable energy, dynamic control is crucial for large-scale alkaline water electrolysis systems. Under traditional Proportional-Integral-Derivative (PID) control, the system's temperature and impurity levels may exceed safe limits during sudden load changes, compromising system safety. Therefore, this study proposes a control strategy based on mathematical models of system temperature and impurities, including Model Predictive Control (MPC) for temperature and Optimal Curve Tracking (OCT) for impurities. The MPC controller offsets the disturbances in temperature caused by current fluctuations through state prediction, while the OCT controller ensures that impurity levels remain within safe limits. Real-time application of these control strategies is achieved through MATLAB-PLC communication, validating controller performance. Experimental results show that the MPC controller exhibits excellent dynamic response and stability in controlling the stack temperature, with a maximum temperature peak deviation of only 2.9 K and a temperature fluctuation range of 5.9 K. This represents a reduction of 1.6 K in peak deviation compared to traditional PID controllers and significantly decreases the temperature fluctuation range. The OCT controller also demonstrates good safety and stability in controlling hydrogen to oxygen (HTO) impurity levels, consistently maintaining the HTO content at the set upper limit of 1.5%, thereby expanding the safe operational range of the system and reducing the need for frequent adjustments to the system setpoints.

Flexible heat and power load control of subcritical heating units based on energy demand-supply balance

To fulfill the requirement for enhancing the flexibility of heating units within the framework of renewable energy grid integration. In this study, a proposed optimal control strategy for heating units borrows the heat extraction output to perform rapid load changes; the boiler heat supply within the unit is clearly differentiated from the turbine energy demand, and formulate corresponding control signals. The coordinated control of the boiler and turbine based on the energy demand-supply balance of the unit as secondary control for the final load accuracy, ensuring its safe and stable operation. To further enhance the control performance, an improved sparrow optimization algorithm with butterfly search is designed to adjust the controller parameters. Finally, field tests on a 300MW subcritical heating unit reveal that the automatic generation control performance index is improved by 32.16% compared to the traditional coordinated control strategy, while taking less impact on the heat user. These results validate the exceptional performance and significant practical value of the optimized control strategy.

Rollover prevention by designing a new fuzzy set solution for automotive active stabilizer bars based on a complex dynamic model

In this paper, the author introduces using active stabilizer bars (hydraulic anti-roll bars) for automobiles to improve the stability of rolling over when an automobile is steering. The car rollover oscillation is described by a complex dynamic model, a combination of the Pacejka tire model, the motion model, and the full oscillation model. A new fuzzy solution has been developed in order to control the active stability bars. This algorithm has three inputs related to the car’s rollover factor. The fuzzy rule (with 125 situations) and membership function are determined based on views related to the car’s stability and the antiroll system’s responsiveness. Numerical calculations and simulation are performed with two cases corresponding to two kinds of steering angles. Three situations correspond to three velocity values: v1, v2, and v3, and four scenarios are simulated in each situation to facilitate the comparison of results. According to the research findings, output values such as roll angle, rollover index, and load change are drastically reduced when the new fuzzy solution is applied to the active anti-roll bars. In the last case, the rollover occurs when the automobile does not use any stability bars or uses regular mechanical bars. The roll angle peak values reach 8.79° and 10.79°, while the maximum value belonging to the Active with Fuzzy situation is 10.94° without rolling over (corresponding to a rollover index of 0.64). The results obtained from this paper are the basis for developing more complex solutions for stabilizer bars in the future.