Load Frequency Research Articles

Influence of puberty stage in immune-inflammatory parameters in well-trained adolescent soccer-players, following 8-weeks of pre-seasonal preparation training

Aim: The immune system can experience temporary suppression following acute or prolonged strenuous exercise, potentially increasing susceptibility to infections. Young athletes, who must balance school, training, studies, and social life, may further disrupt their immune-inflammatory responses. This study aimed to investigate the effects of an 8-week pre-season training on circulating leukocyte responses in well-trained adolescent soccer-players at different stages of puberty. Methods: Thirty-six soccer players, divided into two groups based on their biological age-under sixteen (U16) and above sixteen (A16)-underwent two rounds of assessments. These included evaluations of anthropometrics, physical fitness, and immune parameters before and after an 8-week pre-season soccer training program. Results: No significant treatment or interaction effects were found for erythrocyte sedimentation rate (ESR), platelets, total leucocyte count, immune inflammation markers (IIM), weekly rating of perceived exertion, body composition, sit-and-reach flexibility, or VO2max (P > 0.05). However, post-intervention neutrophil counts were increased, while lymphocyte counts decreased, and IIM levels rose in both groups (P < 0.05). Neuromuscular explosiveness and strength endurance were significantly higher in the A16 group compared to the U16 group (P < 0.05). Body fat was reduced, and all physical fitness parameters improved in both groups over time (P < 0.05). Conclusions: The 8-week soccer-specific training program did not lead to different effects on immune-inflammatory parameters between youth soccer players at different stages of puberty. However, training adaptations, including improvements in physical fitness, were observed in both groups. This suggests that when the same training load (frequency, duration, intensity) is applied to adolescents at different stages of puberty, immune-inflammatory responses are not significantly influenced by age, but physical fitness improves regardless of puberty stage. A key limitation of this study is the lack analysis of lymphocyte subpopulations, which could provide a deeper understanding of the cellular effects of training on immune function.

Advanced Neural Network-Based Load Frequency Regulation in Two-Area Power Systems

In this paper, enhancing dynamic performance in power systems through load frequency control (LFC) is explored across diverse operating scenarios. A new Neural Network Model Predictive Controller (NN-MPC) specifically tailored for two-zone load frequency power systems is presented. ” Make your paper more scientific. The NN-MPC marries the predictive accuracy of neural networks with the robust capabilities of model predictive control, employing the nonlinear Levenberg-Marquardt method for optimization. Utilizing local area error deviation as feedback, the proposed controller's efficacy is tested against a spectrum of operational conditions and systemic variations. Comparative simulations with a Fuzzy Logic Controller (FLC) reveal the proposed NN-MPC's superior performance, underscoring its potential as a formidable solution in power system regulation.

Effect of pre-deformation on corrosion fatigue crack growth of Type 316NG steel in 325 °C deaerated water

Abstract The pre-deformed 316NG (00Cr17Ni12Mo2N) was prepared by cold rolling at room temperature and hot rolling at 400°C. The effect of pre-deformation on fatigue crack growth both in 325 °C deaerated water and room temperature air was investigated. The effect of pre-deformation on corrosion fatigue crack growth (CFCG) in 325 °C deaerated water was much smaller than that on fatigue crack growth (FCG). The cold rolling significantly enhanced CFCG rates while the applied Δ K closed to 11 MPa•m1/2 or the applied loading frequency decreased to 0.1Hz. The accelerating effect of hot rolling on CFCG tended to get evident until the pre-deformation increased to 30%. Dynamic recovery was observed near the corrosion fatigue crack tip in hot rolled 316NG, which may result in relatively low CFCG rates.

Deterioration Analysis of Pavement Structures Incorporating Polymer-Modified Asphalt

It is well-established that structural pavement deterioration is largely influenced by the frequency and magnitude of wheel loads. Furthermore, Polymer-Modified Asphalt (PMA) has gained a widespread application in pavement engineering, as the incorporation of polymers enhances the mechanical properties and improves the overall performance of the pavement, particularly with regard to fatigue resistance. In this study, an experimental program was conducted, comprising the Beam Wheel Tracker Fatigue Test (BWTFT), the Indirect Tensile Stiffness Modulus (ITSM) test, and the Indirect Tensile Fatigue Test (ITFT), on three types of asphalt mixtures: one conventional and two polymer-modified asphalt mixtures, under various conditions. Three distinct pavement structure scenarios were assumed/created/built to perform a deterioration analysis. Subsequently, an iterative approach was developed utilizing the average stiffness reduction and stiffness modulus data obtained from the laboratory results. The findings indicated that this method allows for a more accurate simulation of pavement behavior, confirming that strain levels fluctuate throughout the lifespan of the pavement. Furthermore, the study concluded that the use of PMA provides significantly greater benefits when a deterioration analysis is conducted, compared to traditional approaches.

Fatigue and Rutting Life Design of Layered Flexible Pavements Via Advanced MultiSmart3D

AbstractThis paper introduces a graphical user interface (GUI) built on the foundation of MultiSmart3D_Dynamics (or simply MultiSmart3D) code. Through this GUI, we calculate the pavement design parameters associated with fatigue cracking and rutting deformation. Besides the time-harmonic loading case, the moving loading case is further analyzed based on a simple relation between the moving velocity and frequency. Several numerical examples pertaining to static and dynamic (time-harmonic and moving) responses are studied for two typical pavement structures in Taiwan. The efficiency and accuracy of MultiSmart3D GUI are validated by comparing the present results with those from experimental loading frequency and real-world frequency. Our results reveal that the thickness of the asphalt concrete and base layers have a significant impact on fatigue and rutting lives. In addition, we have found that incorporating dynamic load into the structure may offer potential benefits in terms of cost reduction for optimizing the lifespan of the pavement.

Asymmetric LKF approach for stability analysis of multi-area load frequency control systems with time-varying delays

ABSTRACT The widespread use of communication networks in power systems to send and receive control signals causes time delays in the feedback loop. Improper handling of these delays might degrade controller performance or cause system instability. This work presents new stability criteria that are less conservative in analysing the influence of delays on the stable operation of PI controller-based multi-area power systems with uncertainties and disturbances. An asymmetric Lyapunov-Krasovskii functional technique is used to reduce conservatism by relaxing the condition that all matrices associated with the LKF formulation need not be symmetric or have positive definiteness. In contrast, the symmetric LKF requires all matrices to be symmetric. Two tight-bound integral inequalities are implemented to compute the quadratic integral terms included in the derivative of asymmetric LKF. The less conservative stability conditions are derived for multi-area LFC systems subjected to delays, parameter uncertainties, and disturbances by utilising asymmetric LKF. The superiority of the proposed stability criterion is determined both theoretically and through simulation. According to the analysis, the proposed stability criterion provides higher delay margins than existing techniques.

Study on dynamic modulus of polyurethane mixture: Impact factors and prediction models

Polyurethane mixture (PUM) is proposed as an innovative and promising paving material due to its superior engineering performance and environmentally-friendliness. This study aims to experimentally investigate dynamic modulus of PUM at various temperatures, loading frequencies and loading strains. Furthermore, prediction models on its dynamic modulus considering factors of temperature, loading frequence as well as their coupling effect are proposed. The results demonstrate that dynamic modulus of PUM approximately varies between 2000 MPa and 19,000 MPa in temperature range of 15–50°C, loading frequency range of 0.1–25 Hz, and loading strains range of 20–100με. Dynamic modulus of PUM at 15°C is approximately 5.62 times higher than that at 50°C. The value at 20 Hz is approximately 2.76 times higher than that at 0.2 Hz. By implementing the analysis of variance (ANOVA) method, it is explored that temperature and loading frequency yield significant impact on dynamic modulus of PUM while loading strain imposes negligible influence on dynamic modulus of PUM. By statistical fitting analysis, dynamic modulus of PUM is linear with temperature and presents power function with loading frequency. Moreover, by considering coupling effect of temperature and loading frequency, prediction model on dynamic modulus of PUM is proposed and verified through experiments. The outcome of this study facilitates design and applications of PUM at different climate conditions and traffic loads.

A deformation-diffusion interactive model to study crack-tip behaviour and predict crack growth rate under fatigue and hydrogen-embrittlement conditions

Hydrogen is accepted as a cleaner and more sustainable energy source, since it carries abundant energy and does not produce any effluent gas from burning when compared to fossil fuels. However, long-term degradation in mechanical properties, attributed to hydrogen embrittlement, is reported for metallic materials exposed to gaseous hydrogen, leading to accelerated crack growth behaviour under fatigue. In this paper, a visco-plastic deformation-diffusion interactive model is developed to study fatigue crack growth behaviour in a nickel-based superalloy under hydrogen-embrittlement condition. In the model, cyclic deformation is described by visco-plasticity constitutive model, while hydrogen diffusion is simulated by a modified form of Fick’s first law of diffusion which can address hydrogen diffusion driven by the hydrostatic stress gradient. In particular, trapped hydrogen, expressed as dependent on both diffusible hydrogen concentration and inelastic strain, is considered in the modelling of hydrogen diffusion. The model also describes the effects of hydrogen concentration on mechanical deformation by considering its influence on the evolution of isotropic hardening as well as on the strain state. Interaction between cyclic deformation and hydrogen diffusion is therefore studied for a crack tip in a compact tension specimen subjected to fatigue and hydrogen attack, showing a strong dependency on both loading frequency and stress intensity factor range. Subsequently, a two-parameter criterion is proposed for fatigue crack growth prediction, which accounts for the contributions of both cyclic deformation and hydrogen diffusion. The predictions show a good agreement with experimental data in terms of crack growth rate under fatigue and hydrogen-embrittlement conditions.

A Hybrid Optimization Algorithm Based on Cascaded (1+PI)-PI-PID Controller for Load Frequency Control in Interconnected Power Systems

Electric power generation is crucial for electrical systems, and load frequency control (LFC) ensures system synchronization and reliability. Modern systems use well-designed controllers, with selecting the right type and design approach being essential for a good LFC controller design. This study introduces a cascaded (1+PI)-PI-PID controller optimized using the improved Artificial Bee Colony-Particle Swarm Optimization (IABC-PSO) algorithm for LFC in interconnected power systems, aiming to address complex challenges associated with LFC in modern power systems. The research examines a two-area power system model with hydro and thermal power plants and a capacitive energy storage (CES) device. It proposes a (1+PI)-PI-PID controller structure that integrates traditional PID controllers, enhancing performance and debugging capabilities. The study uses integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE), and integral time square error (ITSE) as objective functions. The IABC-PSO algorithm introduces a novel decision block to increase scout bee (SB) occurrences to 10% for each employed bee (EB), and a new control search phase to optimize SBs' exploration capabilities in a controlled manner, enhancing their efficiency. The algorithm's performance was assessed using four benchmark functions, revealing superior results compared to the Hybrid ABC-PSO algorithm. The study examined six experimental configurations of the controller, varying the sequence of (1+PI), PI, and PID components. The proposed strategy was tested under 1% and 5% step load perturbations in area-1 and area-2 to evaluate dynamic performance. This study conducts a sensitivity analysis by adjusting hydro parameters within a ±25% range, and considers generation rate constraint (GRC) and boiler dynamics (BD) in a two-area reheat thermal-hydro-power system model for reliability and credibility. The proposed IABC-PSO algorithm outperforms traditional methods (ABC, PSO, and hybrid ABC-PSO, WOAw, GWO, hSFS-LUS, and FA) in optimizing parameters, achieving shortest settling times and minimal ITAE values. Its performance is evaluated under random step load perturbations (SLP) and its internal robustness is demonstrated through stability analysis using the Lyapunov method. In conclusion, the hybrid IABC-PSO algorithm-based cascaded (1+PI)-PI-PID controller offers a robust and efficient solution for LFC in interconnected power systems. It ensures enhanced performance, stability, and adaptability, making it a valuable contribution to modern power system management.

Erratum to “Event-triggering-based H∞ load frequency control for multi-area cyber–physical power system under DoS attacks” [Franklin Open Volume 3, June 2023, 100012

Erratum to “Event-triggering-based H∞ load frequency control for multi-area cyber–physical power system under DoS attacks” [Franklin Open Volume 3, June 2023, 100012

Load frequency control based on bandwidth-aware event-triggering mechanism under hybrid attacks

Load frequency control based on bandwidth-aware event-triggering mechanism under hybrid attacks

T–S Fuzzy-Based Load Frequency Control of Multiarea Power System With Hybrid Delays: Performance Analysis and Improvement

T–S Fuzzy-Based Load Frequency Control of Multiarea Power System With Hybrid Delays: Performance Analysis and Improvement

Multi-rate sampled-data observer-based networked load frequency control for multi-area interconnected power systems under multiple cyber-attacks

This paper addresses the problem of adaptive event-triggered load frequency control (LFC) for multi-rate sampling multi-area interconnected power systems with integrated renewable energy. Different network channels can be vulnerable to multiple cyber-attacks, including deception attacks and DoS attacks. First, a matching mechanism is constructed to fuse the multi-rate sampled data, and a set of observers based on multi-rate sampled data is designed to independently estimate the states of the individual power systems. A networked sampled-data PI controller is then developed by introducing a new synchronous sampling scheme and an adaptive event-triggered mechanism. A co-design approach is proposed that integrates decentralized PI controllers, observers, and adaptive event-triggered mechanisms to achieve exponential mean-square stability with a given H∞ performance level for the networked LFC system in the presence of multiple cyber-attacks and multi-rate sampling. Finally, the effectiveness of the proposed method is verified through an example of an interconnected power system.

Load frequency Control in Renewable based Micro Grid with Deep Neural Network based Controller.

Load frequency Control in Renewable based Micro Grid with Deep Neural Network based Controller.

Optimization of Frequency Stability in Hydraulic Load Frequency Control Systems Using Controllers with Filters

This study meticulously investigates the design and analysis of a Load Frequency Control (LFC) system tailored explicitly for hydraulic power systems by employing diverse configurations of PID controllers, namely Proportional (P), Proportional-Integral (PI), Proportional-Derivative (PD), Proportional-Integral-Derivative (PID), Proportional-Derivative-Fractional (PDF), and Proportional-Integral-Derivative-Fractional (PIDF). The primary objective of this exploration is to improve frequency stability in response to various load fluctuations typical in hydraulic systems. A comprehensive evaluation of each controller's performance is conducted through extensive MATLAB simulations, focusing on critical performance metrics such as rise time, peak time, steady-state time, and maximum overshoot. The findings reveal that the PD and PDF controllers exhibit superior response characteristics, offering the fastest and most stable outcomes regardless of whether droop characteristics are utilized or filtering techniques are introduced. Although the implementation of filters significantly mitigates overshoot, it is evident that controllers incorporating droop characteristics tend to compromise optimal steady-state time stability. The overall analysis underscores the PD and PDF controllers as the preeminent solutions for ensuring frequency stability in hydraulic LFC systems, especially under abrupt and substantial load changes, thus positioning them as vital tools in enhancing hydraulic power system reliability and efficiency.

An Optimal Load Frequency Control in a Two-Area Power System Using a Fractional Order Proportional- Integral-Derivative-Based Zebra Optimization Algorithm

Load frequency control systems are crucial for maintaining the stability and reliability of power grids. They help ensure that the power supply matches the demand, preventing fluctuations in grid frequency. However, Load frequency control systems have inherent limitations, such as the potential for instability and oscillation if not properly controlled. In this work, A fractional order proportional integral derivative controller is proposed to address this issue, which exhibits strong capabilities in managing parameter uncertainties, rejecting disturbances, and handling non-linear systems controllers. The novel approach in this search is the application of the zebra optimization algorithm to fine-tune controller parameters, a technique not previously used in load frequency control systems. In this Study the two-area power system with a reheat turbine is used a test case for fractional order proportional integral derivative controller, and the system is simulated using MATLAB/SIMULINK. The objective function integral time of square error is used that acts as a bridge of communication between the system's behavior and the control strategy. The performance of this controller is evaluated under disturbance 0.04. The results of the analysis demonstrated that the fractional order proportional integral derivative based zebra optimization algorithm controller outperformed the traditional fractional order proportional integral derivative controller in terms of three essential criteria: overshoot, settling time, and steady-state error. This research contributes to the advancement of load frequency control systems, ensuring reliable and stable power system operation.

Improved fractional order control with virtual inertia provision methodology for electric vehicle batteries in modern multi-microgrid energy systems

Providing inertia in modern electrical power grids has become a highly demanding task to mitigate reduced power system inertia of renewable energy sources (RES) based power grids. To mitigate the reduced inertia-related problems and consequences, this paper presents improved FOCs for achieving load frequency control (LFC) and the virtual inertia control (VIC) provision of electric vehicles (EVs) lithium-ion batteries. The proposed method includes a twofold contribution using a new hybrid FOC method and an optimum control parameter design method. An innovative hybrid controller is proposed by merging the characteristics of FOPID and TID methods with a fractional filtering stage, leading to hybrid tilt plus FOC integrator-derivative terms. From the VIC side, the TID is proposed to provide a fast response to lithium-ion batteries of the already available EVs. Furthermore, a metaheuristic-based design method is proposed to simultaneously determine the best control parameters set and to facilitate the design procedures using the recently presented powerful growth optimization algorithm (GO). The two interconnected areas case study with high-RES penetration is suggested for testing and investigating the new proposed controller. The results include performance evaluations and comparisons at various expected model uncertainties, renewable energy intermittency, and parametric variations. For instance, the proposed VIC method has a minimized ISE after 28 iterations of 0.001 compared to 0.003 with conventional VIC, and 0.004 without VIC. Thence,the proposed controller achieves more accurate frequency tracking with reduced deviations and fluctuations and exhibits greater robustness compared to previous controllers used in all case studies.

A novel artificial intelligence based multistage controller for load frequency control in power systems

The imbalance between generated power and load demand often causes unwanted fluctuations in the frequency and tie-line power changes within a power system. To address this issue, a control process known as load frequency control (LFC) is essential. This study aims to optimize the parameters of the LFC controller for a two-area power system that includes a reheat thermal generator and a photovoltaic (PV) power plant. An innovative multi-stage TDn(1 + PI) controller is introduced to reduce the oscillations in frequency and tie-line power changes. This controller combines a tilt-derivative with an N filter (TDn) with a proportional-integral (PI) controller, which improves the system’s response by correcting both steady-state errors and the rate of change. This design enhances the stability and speed of dynamic control systems. A new meta-heuristic optimization technique called bio-dynamic grasshopper optimization algorithm (BDGOA) is used for the first time to fine-tune the parameters of the proposed controller and improve its performance. The effectiveness of the controller is evaluated under various load demands, parameter variations, and nonlinearities. Comparisons with other controllers and optimization algorithms show that the BDGOA-TDn(1 + PI) controller significantly reduces overshoot in system frequency and tie-line power changes and achieves faster settling times for these oscillations. Simulation results demonstrate that the BDGOA-TDn(1 + PI) controller significantly outperforms conventional controllers, achieving a reduction in overshoot by 75%, faster settling times by 60%, and a lower integral of time-weighted absolute error by 50% under diverse operating conditions, including parameter variations and nonlinearities such as time delays and governor deadband effects.

Evaluating the dynamic response and phase angle behavior of SBS-modified asphalt mixtures for enhanced pavement performance

Bitumen exhibits viscoelastic properties, showcasing both viscous and elastic behaviors, which are characterized by the phase angle and dynamic modulus. Issues like early fatigue fractures, rutting, and permanent deformations in bituminous asphalt pavements arise due to moisture susceptibility, high-temperature deformation, low-temperature cracking, and overloading. These distresses result in potholes, alligator cracks, and specific deformations that lead to early pavement failure, increasing rehabilitation and maintenance costs. To address these issues, this study examines the dynamic modulus and phase angle behavior of Styrene Butadiene Styrene (SBS) modified and unmodified asphalt mixtures. SBS was incorporated in various proportions, ranging from 2 to 7% by the weight of bitumen. The asphalt mixture performance tester (AMPT) was utilized to measure the dynamic modulus at temperatures of 4.4, 21.1, 37.8, and 54.4 °C, and frequencies of 0.1, 0.5, 1, 5, 10, and 25 Hz. The study found significant correlations between dynamic modulus, temperature, loading frequency, and SBS content. Additionally, Multi Expression Programming (MEPX) and regression modeling were employed to estimate the dynamic modulus of SBS-modified HMA. Results indicated that increasing SBS content up to 7% decreased penetration and ductility values by up to 46% and 56%, respectively, while raising the softening point by 63% due to increased stiffness. The blend with 6% SBS by weight of bitumen exhibited superior performance compared to other mixtures. Phase angle initially increased with rising temperature, peaking at 37.8 °C at lower frequencies, and continued to increase at higher frequencies. Isothermal and isochronal plots showed that the 0% SBS mix had a higher phase angle due to increased bitumen content. Overall, the HMA mix with 6% SBS provided the best outcomes.

A Comprehensive Approach to Load Frequency Control in Hybrid Power Systems Incorporating Renewable and Conventional Sources with Electric Vehicles and Superconducting Magnetic Energy Storage

This study addresses the load frequency control (LFC) within a multiarea power system characterized by diverse generation sources across three distinct power system areas. area 1 comprises thermal, geothermal, and electric vehicle (EV) generation with superconducting magnetic energy storage (SMES) support; area 2 encompasses thermal and EV generation; and area 3 includes hydro, gas, and EV generation. The objective is to minimize the area control error (ACE) under various scenarios, including parameter variations and random load changes, using different control strategies: proportional-integral-derivative (PID), two-degree-of-freedom PID (PID-2DF), fractional-order PID (FOPID), fractional-order integral (FOPID-FOI), and fractional-order integral and derivative (FOPID-FOID) controllers. The result analysis under various conditions (normal, random, and parameter variations) evidences the superior performance of the FOPID-FOID control scheme over the others in terms of time-domain specifications like oscillations and settling time. The FOPID-FOID control scheme provides advantages like adaptability/flexibility to system parameter changes and better response time for the current power system. This research is novel because it shows that the FOPID-FOID is an excellent control scheme that can integrate these diverse/renewable sources with modern systems.