Gain scheduled torque compensation of PMSG-based wind turbine for frequency regulation in an isolated grid

Frequency stability in an isolated grid can be easily impacted by sudden load or wind speed changes. Many frequency regulation techniques are utilized to solve this problem. However, there are only few studies designing torque compensation controllers based on power performances in different Speed Parts. It is a major challenge for a wind turbine generator (WTG) to achieve the satisfactory compensation performance in different Speed Parts. To tackle this challenge, this paper proposes a gain scheduled torque compensation strategy for permanent magnet synchronous generator (PMSG) based wind turbines. Our main idea is to improve the anti-disturbance ability for frequency regulation by compensating torque based on WTG speed Parts. To achieve higher power reserve in each Speed Part, an enhanced deloading method of WTG is proposed. We develop a new small-signal dynamic model through analyzing the steady-state performances of deloaded WTG in the whole range of wind speed. Subsequently, H∞ theory is leveraged in designing the gain scheduled torque compensation controller to effectively suppress frequency fluctuation. Moreover, since torque compensation brings about untimely power adjustment in over-rated wind speed condition, the conventional speed reference of pitch control system is improved. Our simulation and experimental results demonstrate that the proposed strategy can significantly improve frequency stability and smoothen power fluctuation resulting from wind speed variations. The minimum of frequency deviation with the proposed strategy is improved by up to 0.16 Hz at over-rated wind speed. Our technique can also improve anti-disturbance ability in frequency domain and achieve power balance

Atomic-scale structure and nonlinear optical absorption of two-dimensional GeS

info:eu-repo/semantics/publishedVersio

SMYD5 Is a Ribosomal Methyltransferase That Catalyzes RPL40 Lysine Methylation To Enhance Translation Output and Promote Hepatocellular Carcinoma

While lysine methylation is well-known for regulating gene expression transcriptionally, its implications in translation have been largely uncharted. Trimethylation at lysine 22 (K22me3) on RPL40, a core ribosomal protein located in the GTPase activation center, was first reported 27 years ago. Yet, its methyltransferase and role in translation remain unexplored. Here, we report that SMYD5 has robust in vitro activity toward RPL40 K22 and primarily catalyzes RPL40 K22me3 in cells. The loss of SMYD5 and RPL40 K22me3 leads to reduced translation output and disturbed elongation as evidenced by increased ribosome collisions. SMYD5 and RPL40 K22me3 are upregulated in hepatocellular carcinoma (HCC) and negatively correlated with patient prognosis. Depleting SMYD5 renders HCC cells hypersensitive to mTOR inhibition in both 2D and 3D cultures. Additionally, the loss of SMYD5 markedly inhibits HCC development and growth in both genetically engineered mouse and patient-derived xenograft (PDX) models, with the inhibitory effect in the PDX model further enhanced by concurrent mTOR suppression. Our findings reveal a novel role of the SMYD5 and RPL40 K22me3 axis in translation elongation and highlight the therapeutic potential of targeting SMYD5 in HCC, particularly with concurrent mTOR inhibition. This work also conceptually broadens the understanding of lysine methylation, extending its significance from transcriptional regulation to translational control

Single-cell analysis reveals specific neuronal transition during mouse corticogenesis

Background: Currently, the mechanism(s) underlying corticogenesis is still under characterization.Methods: We curated the most comprehensive single-cell RNA-seq (scRNA-seq) datasets from mouse and human fetal cortexes for data analysis and confirmed the findings with co-immunostaining experiments.Results: By analyzing the developmental trajectories with scRNA-seq datasets in mice, we identified a specific developmental sub-path contributed by a cell-population expressing both deep- and upper-layer neurons (DLNs and ULNs) specific markers, which occurred on E13.5 but was absent in adults. In this cell-population, the percentages of cells expressing DLN and ULN markers decreased and increased, respectively, during the development suggesting direct neuronal transition (namely D-T-U). Whilst genes significantly highly/uniquely expressed in D-T-U cell population were significantly enriched in PTN/MDK signaling pathways related to cell migration. Both findings were further confirmed by co-immunostaining with DLNs, ULNs and D-T-U specific markers across different timepoints. Furthermore, six genes (co-expressed with D-T-U specific markers in mice) showing a potential opposite temporal expression between human and mouse during fetal cortical development were associated with neuronal migration and cognitive functions. In adult prefrontal cortexes (PFC), D-T-U specific genes were expressed in neurons from different layers between humans and mice.Conclusion: Our study characterizes a specific cell population D-T-U showing direct DLNs to ULNs neuronal transition and migration during fetal cortical development in mice. It is potentially associated with the difference of cortical development in humans and mice

Effects of electromagnetic induction on signal propagation and synchronization in multilayer Hindmarsh-Rose neural networks

The feed-forward neural networks are the basis and have been widely applied on modern deep learning models, wherein connection strength between neurons plays a critical role in weak signal propagation and neural synchronization. In this paper, a four-variable Hindmarsh–Rose (HR) neural model is presented by introducing an additive variable as magnetic flow which changes the membrane potential via a memristor. The improved HR neurons in the feed-forward multilayer (four and eight layers) networks are investigated. The effects of electromagnetic radiation, synaptic weight and noise intensity on the propagation of the subthreshold excitatory postsynaptic current (EPSC) signal and the neural synchronization are discussed. It is found that when the system is in a weak magnetic field, the subthreshold EPSC signal can be successfully transmitted to the post-layers. Moreover, the neural synchronization of each layer is affected by electromagnetic radiation in the network, and with the help of noise the constant input current will transmit to the post-layers in a stable periodic synchronous form. Our findings provide a possible mechanism for enhancing the subthreshold signal propagation and triggering the neural synchronization in the nervous system

The Signal Characteristics of Oil and Gas Pipeline Leakage Detection Based on Magneto-Mechanical Effects

In order to solve the problem of the quantification of detection signals in the magnetic flux leakage (MFL) of defective in-service oil and gas pipelines, a non-uniform magnetic charge model was established based on magnetic effects. The distribution patterns of magnetic charges under different stresses were analyzed. The influences of the elastic load and plastic deformation on the characteristic values of MFL signals were quantitatively assessed. The experimental results showed that the magnetic charge density was large at the edges of the defect and small at the center, and approximately decreased linearly with increasing stress. The eigenvalues of the axial and radial components of the MFL signals were compared, and it was found that the eigenvalues of the radial component exhibited a larger decline rate and were more sensitive to stress. With the increase in the plastic deformation, the characteristic values of the MFL signals initially decreased and then increased, and there was an inflection point. The location of the inflection point was associated with the magnetostriction coefficient. Compared with the uniform magnetic charge model, the accuracy of the axial and radial components of the MFL signals in the elastic stage of the improved magnetic charge model rose by 17% and 16%, respectively. The accuracy of the axial and radial components of the MFL signals were elevated by 9.15% and 9%, respectively, in the plastic stage

Autaptic modulation-induced neuronal electrical activities and wave propagation on network under electromagnetic induction

Based on an improved HR neuron model, the effects of electrical and chemical autapses on the firing activities of single neurons are studied, and the wave propagation in forward feedback neural network is also discussed by considering autapstic regulation under different intensities of electromagnetic induction. It is found that the electrical activities of single neuron can be changed by exerting excitatory or inhibitory of electrical and chemical autapses. With different feedback gains of electromagnetic induction current, membrane potential shows the oscillatory solutions and steady states. Under the condition of different autapse or electromagnetic induction, the propagation of electrical activities caused by the central neuron is transformed in the forward feedback network. Moreover, the spatial synchronization of the network will be changed by choosing different coupling intensities and feedback gains. It is proved that the electrical and chemical autapses play a significant role in firing modes of single neuron and the wave propagation of the forward feedback networks under the electromagnetic induction

Phase noise-induced coherence resonance in three dimension memristive Hindmarsh-Rose neuron model

Phase noise derives from the phase random variation of signal in many physical or biological systems. Based on a three dimension memristive Hindmarsh-Rose neuron model, the influences of phase noise on the neural dynamic features are studied here. It is found that phase noise (an applied voltage source in the flux-controlled memristor) can induce the different hysteresis loops, and the strong phase noise can destroy the memory characteristic of the memristor. The amplitude, angular frequency and noise intensity of phase noise can obviously make the dynamic modes undergo successive transitions. The coherence resonance is related to the angular frequency of phase noise, and is a common phenomenon that persists for the amplitude of phase noise. These results might provide a possible mechanism behind the resonance phenomenon in nonlinear systems