Abstract This paper proposes a virtual inertia control strategy for wind farms based on auto disturbance rejection controller with artificial bee colony (ABC) algorithm. First, based on the system frequency dynamic response equation, an active disturbance rejection controller is designed according to the non‐linear feedback control law and the extended state observer to improve the anti‐interference capability of the virtual inertia control system. Second, in order to solve the problem of difficulty in tuning the parameters of the active disturbance rejection control (ADRC), an ABC algorithm based on nectar collection behaviour was proposed to iteratively optimize the parameters of ADRC. Finally, the proposed control method is effectively verified based on Matlab/Simulink. The simulation results show that compared with traditional algorithms, the proposed ABC‐ADRC can effectively control the rotor speed to adjust the frequency of the power grid system, achieve power sharing and adaptive noise elimination, reduce the complexity of parameter settings, not only improve anti‐interference ability, but also enhance the robustness of the system.
Improving the operating conditions can enhance the output performance of PEMFCs based on Ammonia decomposition gas as fuel (Ammonia–Hydrogen fuel cell). Therefore, in this study, a 3-dimensional non-isothermal numerical model was established to study the gas–heat–water distribution of Ammonia–Hydrogen fuel cell under different operating conditions, and the performance improved by operating conditions is analyzed through regional quantification, and then optimize the operating conditions for improving performance. The research results show that increasing the working pressure, gas humidity and nitrogen adsorption can improve the output performance of the fuel cell effectively. Under low-pressure operating conditions, increasing the unit pressure can increases the output performance by 80%; under low-humidity operating conditions, increasing the relative humidity by 0.15 can increases the output performance by 8%; under low-hydrogen content operating conditions, increasing the mole fraction of hydrogen by 5% through nitrogen adsorption can increases the output performance by 10%. Along the flow direction, the molar concentration distribution characteristics of reactant gas is opposite to the change trend of membrane water content, which is closely related to the reaction rate; the overall change trend of heat is consistent with the water content of the membrane, but the hot spot value at the outlet has a downward trend with the strengthening of the reaction conditions and the continuous increase of the load. When operating conditions changes, the variation trend of the regional total output is different, and the maximum value of the total output varies from the operating conditions. Optimizing the operating conditions can make it possible to achieve the output performance based on pure hydrogen as fuel.
Lithium metal batteries (LMBs) are viewed as one of the most promising high energy density battery systems, but their practical application is hindered by significant fire hazards and fast performance degradation due to the lack of a safe and compatible configuration. Herein, nonflammable quasi-solid electrolytes (NQSEs) are designed and fabricated by using the in situ polymerization method, in which 1,3,2-dioxathiolan-2,2-oxide is used as both initiator to trigger the in situ polymerization of solvents and interphase formation agent to construct robust interface layers to protect the electrodes, and triethyl phosphate as a fire-retardant agent. The NQSEs show a high ionic conductivity of 0.38 mS cm-1 at room temperature and enable intimate solid-electrolyte interphases, and demonstrate excellent performance with stable plating/striping of Li metal anode, and high voltage (4.5 V) and high temperature (>60 °C) survivability. The findings provide an effective strategy to build high-temperature, high-energy density, and safe quasi-solid LMBs.
In view of the application requirements of power conversion system (PCS) under different working conditions, a control strategy of PCS with DC voltage loop and constant power loop synergizing outer loop and current inner loop is designed. It solves the problem of voltage and current dynamic oscillation caused by traditional methods in mode switching (constant voltage mode, constant power mode). This strategy prevents the over-voltage or under-voltage of the PCS bus voltage during the switching process from causing battery over-current damage and improves the stability of the system. By building a Matlab/Simulink simulation platform, the stable operation of the PCS during power scheduling and mode switching is verified.
Abstract Mass spectrometry (MS) has become a prominent choice for large-scale absolute protein quantification, but its quantification accuracy still has substantial room for improvement. A crucial issue is the bias between the peptide MS intensity and the actual peptide abundance, i.e., the fact that peptides with equal abundance may have different MS intensities. This bias is mainly caused by the diverse physicochemical properties of peptides. Here, we propose a novel algorithm for label-free absolute protein quantification, LFAQ, which can correct the biased MS intensities by using the predicted peptide quantitative factors for all identified peptides. When validated on datasets produced by different MS instruments and data acquisition modes, LFAQ presented accuracy and precision superior to those of existing methods. In particular, it reduced the quantification error by an average of 46% for low-abundance proteins.
In this article, a scalable knowledge-based neural network (KBNN) large-signal model of gallium nitride (GaN) high-electron-mobility transistors (HEMTs) with accurate trapping and self-heating effects characterization is developed. An improved empirical drain current model is proposed and added to the neural network as prior knowledge, thereby establishing the drain current model, including self-heating effect. A new empirical equation is proposed to model the buffer-related trapping effect more accurately. Taking Angelov capacitance models as prior knowledge, the KBNN capacitance models are completed. Moreover, the scaling characteristics of the proposed KBNN model are studied. The developed model has been fully verified by different sizes of GaN HEMTs. Good agreement between the model simulation results and the measurement data, including current–voltage ( $I$ – $V$ ), $S$ -parameters, power characteristics, and load-pull data, confirms the effectiveness of the proposed model. The proposed scalable KBNN model is fast and accurate and would be useful for accurate large-signal modeling of large gate periphery GaN HEMTs for high-power radio frequency (RF) applications.
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