Variation Of Internal Parameters Research Articles

ST40 electromagnetic predictive studies supported by machine learning applied to experimental database

Nuclear fusion is entering the era of power plant-scale devices, which are now undergoing extensive studies to support the design phase. Plasma disruptions pose a high risk to these classes of devices because of the large stored thermal and magnetic energy which might jeopardize machine integrity and availability. Therefore, disruptions within these devices must be virtually eliminated, and any disruptions which do happen must be highly mitigated. However, the characterisation, prediction and technology used to mitigate disruptions is still an area of active development. In this paper, the authors investigate the disruptions within ST40, with particular attention at the identification of causes and effects associated with disruptions, both from a physics basis and an engineering standpoint. This paper aims at presenting preliminary predictive analyses of ST40 plasma scenarios by exploiting Machine Learning techniques applied to an experimental database populated by plasma pulses executed during the ST40 2021–2022 experimental campaign. The database contains both disrupted and non-disrupted pulses. Using Machine Learning, common features within disruptions are automatically classified and identified, mapping the controllable operational space in terms of plasma displacement and variation of specific plasma internal parameters. The classification was validated by benchmarking the numerical reconstruction of the plasma dynamics with experimental data recovered from the plasma diagnostics. Subsequent Machine Learning analyses allowed the extrapolation of new disrupted plasma configurations for preliminary predictive simulations of the plasma column displacement. Thanks to the numerical simulations performed in MAXFEA environment, it is possible to investigate the plasma vertical displacement both during disrupted and regularly terminated plasma scenarios and to provide lessons to be learnt for the next ST40 experimental campaign and for the design of future ST devices.

Modeling and experimental research on adjustable ejector in fuel cell gas circulation system

Fuel cells can be made to operate under a variety of conditions by implementing adjustable ejectors in their anode gas circulation systems. Based on a quasi-two-dimensional model and considering factors such as friction and variable area effects, a theoretical model of ejectors with needles was established to predict their primary and secondary flow rates. Theoretical calculations were used to analyze the variations in internal parameters and flow losses along the ejector, explain the reasons and laws behind the effect of needle position on ejector performance, and explore the control mechanism of different needle shapes on ejector flow rates. Finally, flow control experiments were conducted on three types of adjustable needle shapes–oblique straight, quadratic, and parabolic–to validate the precision of the adjustable ejector theoretical model and the controls of different shapes on the ejector flow rates. The research indicates that the relative errors of the quasi-two-dimensional model in predicting primary and secondary flow rates are within ± 5 % and + 20 % to −5%, respectively. Both the primary and secondary flow rates were impacted by the needle position variation, though the impact on the secondary flow was weaker. The parabolic shape was optimal for the linear control of primary flow rates, followed by the oblique straight shape. The quadratic shape was the weakest, although the quadratic-shaped needle had a wider range of flow rate control. Modeling and experimental research on the adjustable ejector contributes to understanding the influence of the needle on the ejector performance under different working conditions, broadening the application range of the ejector, and providing a basis for flow control in the fuel cell system.

Optimizing dye-sensitized solar cell efficiency through TiO2/CuS doping: effects of internal parameter variations

Dye-sensitized solar cells (DSSC) have attracted significant research interest due to their semi-transparency, ease of fabrication, cost-effectiveness, and environmental friendliness. This research focuses on enhancing dye-sensitized solar cells efficiency by doping titanium dioxide with copper sulfide and varying internal parameters such as concentration, thickness, and temperature. The primary issue addressed is the low electron mobility of titanium dioxide, which limits its performance as a photoanode. Using simulation methods, this study analyzed dye-sensitized solar cells performance under different doping conditions. The results showed that the highest efficiency of 8.18 % was achieved at a TiO2/CuS concentration of 0.3 %. The optimal photoanode thickness was approximately 3 μm, yielding an efficiency of 8.33 %. Temperature variations at room temperature (275 K, 300 K, and 325 K) resulted in efficiency values of 13.94 %, 15.06 %, and 16.18 %, respectively. These findings indicate that targeted doping and precise control of internal parameters can significantly enhance the performance of titanium dioxide-based dye-sensitized solar cells. The improved efficiency is attributed to enhanced electron mobility and better structural and morphological characteristics of the doped titanium dioxide photoanode. This research provides valuable insights into developing more efficient and sustainable dye-sensitized solar cells. The practical implications of these results are significant for advancing dye-sensitized solar cells as a viable alternative to conventional solar cells, contributing to the global effort to address the energy crisis by providing a cost-effective and environmentally friendly energy source.

Circuit topology aware GNN-based multi-variable model for DC-DC converters dynamics prediction in CCM and DCM

A regression model based on graph neural network, tailored for electric circuit dynamics prediction is introduced, providing converter performance predictions on converter circuit level and internal parameter variations. Regardless of the number of components or connections present in a converter circuit, the proposed model can be readily scaled to incorporate different converter circuit topologies. Moreover, the model can be used to analyse converter circuits with any number of circuit components and any control parameters variation. To enable the use of machine learning methods and applications, all physical and switching circuit properties such as converter circuits operating in continuous conduction mode or discontinuous conduction mode are accurately mapped to graph representation. Three of the most common converters (Buck, Boost, and Buck-boost) are used as example circuits applied to model and the target is to predict the gain and current ripples in inductor. The model achieves 99.51% on the R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} measure and a mean square error of 0.0263.

Disturbance Observation and Suppression in an Airborne Electro-Optical Stabilized Platform Based on a Generalized High-Order Extended State Observer.

Active disturbance rejection control (ADRC) is widely used in airborne optoelectronic stabilization platforms due to its minimal reliance on the mathematical model of the controlled object. The extended state observer (ESO) is the core of ADRC, which treats internal parameter variations and external disturbances as total disturbances, observes the disturbances as extended states, and then compensates them into the control loop to eliminate their effects. However, the ESO can only achieve a precise estimation of constant or slowly varying disturbances. When the disturbance is periodically changing, satisfactory results cannot be obtained. In this paper, a generalized high-order extended state observer (GHOESO) is proposed to achieve the precise estimation of known frequency sinusoidal disturbance signals and improve disturbance suppression levels. Through numerical simulations, a traditional ESO and GHOESO are compared in terms of disturbance observation capability and disturbance suppression ability for single and compound disturbances based on our prior knowledge of disturbance frequency. The effectiveness of the proposed GHOESO method is verified. Finally, the algorithm is applied to an airborne optoelectronic stabilization platform for a 1°/1 Hz swing experiment on a space hexapod swing table. The experimental results demonstrate the superiority of the GHOESO proposed in this paper.

Investigation of Lithium-Ion Battery Negative Pulsed Charging Strategy Using Non-Dominated Sorting Genetic Algorithm II

To address the critical issue of polarization during lithium-ion battery charging and its adverse impact on battery capacity and lifespan, this research employs a comprehensive strategy that considers the charging duration, efficiency, and temperature increase. Central to this approach is the proposal of a novel negative pulsed charging technique optimized using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). This study initiates the creation of an intricate electrothermal coupling model, which simulates variations in internal battery parameters throughout the charging cycle. Subsequently, NSGA-II is implemented in MATLAB to fine-tune pulsed charging and discharging profiles, generating a Pareto front showcasing an array of optimal solutions tailored to a spectrum of goals. Leveraging the capabilities of the COMSOL Multiphysics software 6.2 platform, a high-fidelity simulation environment for lithium-ion battery charging is established that incorporates three charging strategies: constant-current (CC) charging, a multi-stage constant-current (MS-CC) charging protocol, and a pulsed-current (PC) charging strategy. This setup works as a powerful instrument for assessing the individual effects of these strategies on battery characteristics. The simulation results strongly support the superiority of the proposed pulsed-current charging strategy, which excels in increasing the battery temperature and amplifying battery charge capacity. This dual achievement not only bolsters charging efficiency significantly but also underscores the strategy’s potential to augment both the practical utility and long-term viability of lithium-ion batteries, thereby contributing to the advancement of sustainable energy storage solutions.

Economic optimization of exterior wall insulation in Chinese office buildings by coupling artificial neural network and genetic algorithm

Envelope insulation is an essential measure to reduce building energy consumption and is the focus of many building energy codes. In China, the current standard (GB 55015-2021) only specified limit values for the thermal conductivity (U) of the exterior walls, and it ignored variations in building characteristics and internal parameters. Therefore, it is difficult for architects to quickly determine the exterior wall U-value in building design and retrofit. To address this issue, the exterior wall U-values of office buildings in China were economically optimized by TRNSYS, ANN, and GA. To begin with, the effect of enhancing exterior wall insulation on the heating and cooling energy consumption of office buildings was investigated by TRNSYS software, taking into account variations in building characteristics. Moreover, the exterior wall U-values of office buildings were optimized by coupling ANN and GA. The results showed that the design values of the GB 55015-2021 did not achieve the optimal results in China, and the most economical exterior wall U-value was 0.24 W/(m2·K) in Harbin, 0.24 W/(m2·K) in Beijing, 0.42 W/(m2·K) in Changsha, 1.82 W/(m2·K) in Guangzhou, 0.66 W/(m2·K) in Kunming. Compared with the pre-optimization, the annual life cycle cost of building heating and cooling reduced by 0.96 % in Harbin, 2.07 % in Beijing, 1.44 % in Changsha, 1.68 % in Guangzhou, and 7.37 % in Kunming. In addition, variations in building parameters should be considered in the design phase of new buildings or in the renovation of old buildings, such as internal heat gain, which may lead to different optimal results.

Physics-informed differentiable method for piano modeling

Numerical emulations of the piano have been a subject of study since the early days of sound synthesis. High-accuracy sound synthesis of acoustic instruments employs physical modeling techniques which aim to describe the system’s internal mechanism using mathematical formulations. Such physical approaches are system-specific and present significant challenges for tuning the system’s parameters. In addition, acoustic instruments such as the piano present nonlinear mechanisms that present significant computational challenges for solving associated partial differential equations required to generate synthetic sound. In a nonlinear context, the stability and efficiency of the numerical schemes when performing numerical simulations are not trivial, and models generally adopt simplifying assumptions and linearizations. Artificial neural networks can learn a complex system’s behaviors from data, and their application can be beneficial for modeling acoustic instruments. Artificial neural networks typically offer less flexibility regarding the variation of internal parameters for interactive applications, such as real-time sound synthesis. However, their integration with traditional signal processing frameworks can overcome this limitation. This article presents a method for piano sound synthesis informed by the physics of the instrument, combining deep learning with traditional digital signal processing techniques. The proposed model learns to synthesize the quasi-harmonic content of individual piano notes using physics-based formulas whose parameters are automatically estimated from real audio recordings. The model thus emulates the inharmonicity of the piano and the amplitude envelopes of the partials. It is capable of generalizing with good accuracy across different keys and velocities. Challenges persist in the high-frequency part of the spectrum, where the generation of partials is less accurate, especially at high-velocity values. The architecture of the proposed model permits low-latency implementation and has low computational complexity, paving the way for a novel approach to sound synthesis in interactive digital pianos that emulates specific acoustic instruments.

Enhancing Stability of Grid-Supporting Inverters from an Analytical Point of View with Lessons from Microgrids

The central components influencing future grid stability in the future are inverters and their controllers. This paper delves into the pivotal role of inverters and their controllers in shaping the future stability of grids. Focusing on grid-supporting inverters, the study utilizes a microgrid test setup to explore their impact on overall grid stability. Employing impedance-based stability analysis with the Nyquist criterion, the paper introduces variations in internal inverter parameters and external grid parameters using pole-zero map considerations. The inverter’s control structure, resembling standard generators with droop control, facilitates the application of grid operators’ knowledge to inverter control. Mathematical insights into stability principles are provided, highlighting the influence of poles related to the phase-locked loop and the strategic placement of additional poles for enhanced stability. Furthermore, the paper evaluates the effects of rotating inertia, revealing that a 50% increase in system inertia can stabilize unstable microgrid behavior, enabling grid-supporting inverters to actively contribute to grid reliability.

Slug flow parameters applied to dynamic riser analysis

Slug flow is a typical pattern in the petroleum production system. Its main feature is intermittency. This feature influences the riser dynamic behavior due to the coupling of its geometric configuration and time and space variation of internal fluid parameters. Although the studies that illustrate slug flow intermittency and dynamic riser behaviors have been growing recently, this discussion still needs to be expanded. Most slug models proposed in the literature are simplified and do not adequately capture the intermittency to a proper riser dynamic influence determination. Previous models neglected at least one of the following slug flow mechanisms: random features, bubble shape, liquid slug aeration, wake, and pipeline inclination effects. This paper aims to cover all these phenomena and present a model that provides realistic slug flow data for any riser analysis program. The dynamic riser analysis is not the scope of this paper. This article used a new slug tracking model that captures the intermittency to study the influence of the internal flow over the dynamic riser behavior. The model considers the liquid slug hold-up and its change over the pipe, the oil and gas thermodynamic properties, and the geometrical transition from typically horizontal plane interfaces to vertical concentric bubble shapes. The slug tracking model tests use three gas and liquid ratios (0.55, 0.7, and 0.82) with a 2.05 m/s constant mixture velocity. The maximum relative pressure deviation was 24.1. Time and space analysis of the slug flow parameters demonstrates the slug tracking capacity to reproduce the intermittency of the bubble and liquid slug lengths, void fractions, velocities, and pressures. This intermittence is essential to describe the internal slug flow influence on the structure behavior.

Online learning-based active disturbance rejection control of autonomous surface vehicles with HIL simulations

Modeling and control of autonomous surface vehicles (ASVs) is a challenging task due to the requirement of real-time adaptation to internal hydrodynamic parameter variations and external ocean disturbances induced by wind, waves, and ocean currents. In this paper, an online learning-based active disturbance rejection control method is proposed by combining real-time model learning and anti-disturbance control. Specifically, a filtering learning extended state observer (FLESO) is proposed for online identifying the unknown hydrodynamic parameters, the unknown control input gain, and estimating system uncertainties composed of the unknown external disturbances and dynamic modeling errors, with guaranteed convergence. Then, a self-learning anti-disturbance control law is developed based on the FLESO, enabling self-learning anti-disturbance speed tracking without prior knowledge of model parameters. A salient feature of the proposed method is that the unknown hydrodynamic parameters, the unknown control input gain, and the unknown system uncertainties can be estimated online simultaneously. Lyapunov stability analysis is employed to verify the stability of the closed-loop system. The effectiveness of the proposed online learning-based active disturbance rejection control method is demonstrated through HIL simulations.

Sliding Input Output Control of Squirrel Cage Motor Based NPC Five Level Inverter

Speed squirrel cage motor control is an area of research that has been in evidence for some time now. In this paper, a nonlinear controller is presented for the squirrel cage motor drives, based on a combination between input-output feedback linearization control (IOLC) technique and sliding mode control (SMC) to create a new control which is sliding input-output linearization (SIOLC) control of squirrel cage motors which associates by an NPC five levels inverter PWM technical used for the variation of its speed, where the sliding mode control it used for controlling the speed of squirrel cage motor and the input-output linearization control applied for two input witch are flux and current. To test robustness and performance of sliding input-output linearization control (SIOLC) we created a variation of internal parameters of the motor. The simulation results show robustness the sliding input-output linearization control of squirrel cage motor responses.

Health-aware model predictive energy management for fuel cell electric vehicle based on hybrid modeling method

Fuel cell lifetime is strongly affected by dynamic conditions. Most existing energy management works only focus on the fuel cell durability protection from the perspective of output power slope, without deeply considering the influence of the important parameters inside the stack. However, considering the variation of stack internal parameters (mechanism analysis) is more significant for fuel cell lifetime evaluation. In this paper, a health-aware model predictive control (HA-MPC) energy management strategy is proposed for fuel cell electric vehicle. A fuel cell health state model is established from the perspective of stack hydrogen excess ratio (HER), oxygen excess ratio (OER) and humidity through the hybrid modeling method. The fuel cell mechanism model and the low-dimensional data-driven model are established through the grey-box model estimation method and genetic algorithm-based radial basis function (GA-RBF) neural network. Then the objective function of energy management strategy is developed considering the total equivalent hydrogen consumption and stack improper parameter changes of HER, OER and humidity. Comparing with model predictive control strategy based on the typical power cost function, the HA-MPC can effectively reduce the steep drop of HER and OER in low power region by 3.58% and 4.41%, which can protect the fuel cell system lifetime.

Study on Co-Estimation of SoC and SoH for Second-Use Lithium-Ion Power Batteries

Lithium-ion batteries are an ideal power supplier for electric vehicles (EVs) due to their high-power density and wide operating voltage, but their performance decays to 80% before retirement from EVs. Nevertheless, they still have a particular use value after decommissioning, so recycling the retired power battery in cascade can be considered. Therefore, accurate estimation of battery state-of-charge (SoC) and state-of-health (SoH) is crucial for extending the service life and echelon utilization of power lithium-ion battery packs. This paper proposes a comprehensive co-estimation scheme of battery SoC/SoH for the second-use of lithium-ion power batteries in EVs under different cycles using an adaptive extended Kalman filter (AEKF). First, according to the collected battery test data at different aging cycle levels, the external battery characteristics are analyzed, and then a cycle-dependent equivalent circuit model (cECM) is built up. Next, the parameter estimation of this battery model is performed via a recursive least square (RLS) algorithm. Meanwhile, the variations in internal battery parameters of the cycle numbers are fitted and synthesized. Moreover, validation of the estimated parameters is further carried out. Based on this enhanced battery model, the AEKF algorithm is utilized to fulfill battery SoC/SoH estimation simultaneously. The estimated results of SoC/SoH are obtained for a LiCoO2 cell in the case of CCC (constant current condition) under different cycle times. The results show that this proposed co-estimation scheme can predict battery SoC and SoH well, wherein the peak values of the SoC errors are less than 2.2%, and the peak values of SoH, calculated by the estimated capacity and internal resistance, are less than 1.7% and 2.2%, respectively. Hence, this has important guiding significance for realizing the cascade utilization of lithium-ion power batteries.

Novel deadbeat predictive control strategy for DFIG’s back to back power converter

To successfully carry out a wind energy conversion system, it is necessary to simultaneously control the rotor and the grid side. This paper proposes a doubly-fed induction generator's predictive power control. While the powers are controlled indirectly through currents, the latter is controlled using the deadbeat command. Based on discrete-time, the control suggests at each sample period the required voltages to the the-back-to-back converter to reach the desired setpoints, control the powers, and the DC link voltages. For these reasons, a presentation of the system is given first, then a description of the predictive control, followed by applying this strategy on the rotor side control and grid side control. Finally, a random wind profile was applied to analyze the system's performance with a unitary power factor. The simulation results are presented in the MATLAB/Simulink environment using a 1.5 kW DFIG. The results obtained by applying a random wind profile have well fulfilled the objectives of the control and the system robustness is approved by the excellent tracking allowing the machine's internal parameters variation. By comparing the quality and the tracking reference of the proposed control method to other control methods, the deadbeat controller was very promising.

Interspecific variations in external and internal egg quality among various captive avian species from Punjab, Pakistan.

Present study was planned to determine variations in external and internal quality egg parameters of different avian species including ostrich Struthio camelus, ducks Anas platyrhynchos, chicken Gallus gallus, turkeys Meleagris gallopavo and grey francolin Francolinus pondicerinus. All the birds were kept under similar rearing conditions. A total of 150 eggs were collected for each species to record external features of these eggs. Statistically significant (p<0.05) variations were recorded in egg weight, egg length and egg width between ostrich, ducks, chicken, turkey and quail eggs. Significantly (p<0.05) higher egg weight, egg length and egg width was observed for ostrich eggs while the same was lowest for grey francolin eggs. Similarly, significantly (p<0.05) greater shape index and egg volume values were observed for ostrich eggs while lowest shape index values were recorded for turkey eggs and egg volume was lowest for grey francolin. Significantly, higher (p<0.05) values of egg density were noted for eggs of the quail and the same were lowest for ostrich eggs. Non-significant variations in egg density values were observed between eggs of the ducks, chicken, turkey and grey francolin. It has been concluded that the positive correlations between the internal and external egg quality traits indicated that the traits can be improved through selection.

Improved State-of-health prediction based on auto-regressive integrated moving average with exogenous variables model in overcoming battery degradation-dependent internal parameter variation

State-of-health (SOH) of lithium-ion battery is a health indicator that predicts the life of target application by estimating the internal state of battery. It may alarm the replacement of battery in the application and caution when it is abnormal. However, it is very complicated to accurately predict the battery lifetime because battery degradation depends on user`s driving paten, and external factor. Therefore, it is very important to predict SOH by selecting factors that are highly related to battery life. This study introduces the auto-regressive moving average (ARMA) time-series model and proposes SOH prediction with extended models. ARMA-extend models used in this paper are ARIMA, Seasonal ARIMA (SARIMA) and auto-regressive integrated moving average with exogenous variables (ARIMAX) models. To analyze the impact of additional external variables in the model on SOH prediction, SOH prediction performance is compared using ARIMA, Seasonal ARIMA, and ARIMAX models. Among them, ARIMA model was excluded since it did not take into account seasonality which is important attribute of battery degradation. SARIMA model has the advantage of being able to consider the seasonality of data and ARIMAX model has the advantage of being able to consider external factors which is highly related with battery SOH. The two models were compared using Means square error (MSE) technique, and it was proved that ARIMAX model had higher accuracy than SARIMA since ARIMAX can consider external factors affecting battery degradation.

Research on UDE control based on load torque compensation of PMSM

In order to solve the problem that the traditional uncertainty and disturbance estimator (UDE) control needs to increase the filter order to keep good performance when facing rapid disturbance changes, thus lead to cost increase in implementing the system, a speed control strategy of permanent magnet synchronous motor (PMSM) driver based on reduced order observer compensation is proposed. The designed control strategy is robust to the system with internal parameter variation and external torque disturbance. Through the compensation of load torque, the pressure of UDE controller is relieved, and then the tracking error of high-frequency component in load torque is eliminated, and the control performance of the system is improved more effectively. This paper proves the superiority of the new compound controller through comparison of simulation. results

Performance of Steel Monopole Transmission Line Supporting Structure in Various Wind Zones of India

The design of every new transmission line can address the solutions to fresh engineering problem. Therefore, the selection of the monopole structures in place of conventional lattice towers summarized the obligatory design parameters. The reliability climatic wind loads and the security longitudinal broken wire load combinations influence the internal parameter assessment of the steel pole structures. To assess them, a 60m high double circuit (DC) 220kV line with 305 m basic span is selected. All the Six wind zones (33, 39, 44, 47, 50, and 55 m/s) of India are selected to compute the transverse wind loads on the tower. Secondly broken wire security load in the longitudinal direction is adopted for the load combination for the monopole transmission tower design. With the above data, this paper presented the variation of the internal parameters in the wind zones. It is found that even though the basic wind speed from 33 m/s to 55 m/s is increased to 66.66%, top deflection is increased by 1.6%, a bending moment is enhanced by 6.3 % and shear force is increased by 12.6% only. The results show the change of wind speed has a little effect on the monopole tower. However, the variations of internal parameters in the wind zone are strongly depending on the Longitudinal loading only. The ASCE 48-19 steel monopole code is adopted to design the monopole since the Indian standard design code for monopole was not found. However, when comparing with the Indian Standard general Steel code IS 800-2007(Limit state) design guidelines, the Indian steel code contributes 10% more weight.

An Electronic Line-Shafting Control Strategy Based on Sliding Mode Observer for Distributed Driving Electric Vehicles

This paper proposed control schemes to realize the collaborative control as well as enhance the control precision for the distributed driving electric vehicle (DDEV). Firstly, we suggested an electronic line-shafting (ELS) to realize the cooperative control for the Multi-Motor system which is installed in the DDEV. Secondly, we adopted a nonsingular terminal sliding mode control (NTSMC) method combined with a sliding mode observer (SMO) to quicken the response velocity and strengthen the robustness of the single motor controller. Finally, for the chattering value caused by the internal parameter variation and external disturbance, a fuzzy algorithm is proposed to obtain the controller parameters of NTSMC in real-time to eliminate the chattering value of DDEV. Through several simulations and experiments, the results demonstrate that the proposed strategies can realize ideal collaborative control for the Multi-Motor system, as well as improve the dynamic performance and anti-jamming ability for the whole control system of DDEV.