Voltage Stability Analysis of Front-End Speed Controlled Wind Turbine Integrated into Regional Power Grid Based on Bifurcation Theory

Since wind power has characteristics such as intermittent and fluctuation, the integration of large-scale wind turbines into the power grid will bring a great impact on the voltage stability of the system. In this paper, the influence of the front-end speed controlled wind turbine (FSCWT) on the system voltage stability is studied. An actual model of the wind turbines, including the FSCWTs, connected to a regional power grid in Zhangye, Gansu Province, is established. Firstly, differential-algebraic equations (DAEs) describing the dynamic characteristics of the wind turbine are given and the mathematical model of the system includes FSCWT is established. The continuation method is used to track the balance solution of the DEAs within given parameter intervals. Based on that, the influence of the reactive power variation and wind speed fluctuation on the stability of system voltage is analyzed through both the bifurcation theory and the time-domain simulation. Results show that the Hopf bifurcation (HB) and the saddle-node bifurcation (SNB) are inherited for the system, indicating that such bifurcations are the essence of nonlinear dynamics that lead to voltage instability. The greater the disturbance of the bifurcation parameter Q1, the shorter the time of voltage collapse and the smaller the stable operation area of the system. With the increase of wind speed, the amplitude of system voltage will increase slightly, but the HB point will appear in advance, which is more likely to lead to voltage instability and further reduce the stable operation area of system voltage

On Variable-Universe Fuzzy Control for Drive Chain of Front-End Speed Regulated Wind Generator

The rapid development of wind generation technology has boosted types of the new topology wind turbines. Among the recently invented new wind turbines, the front-end speed regulated (FSR) wind turbine has attracted a lot of attention. Unlike conventional wind turbine, the speed regulation of the FSR machines is realized by adjusting the guide vane angle of a hydraulic torque converter, which is converterless and much more grid-friendly as the electrically excited synchronous generator (EESG) is also adopted. Therefore, the drive chain control of the wind turbine owns the top priority. To ensure that the FSR wind turbine performs as a general synchronous generator, this paper firstly modeled the drive chain and then proposed to use the variable-universe fuzzy approach for the drive chain control. It helps the wind generator operate in a synchronous speed and outperform other types of wind turbines. The multipopulation genetic algorithm (MPGA) is adopted to intelligently optimize the parameters of the expansion factor of the designed variable-universe fuzzy controller (VUFC). The optimized VUFC is applied to the speed control of the drive chain of the FSR wind turbine, which effectively solves the contradiction between the low precision of the fuzzy controller and the number of rules in the fuzzy control and the control accuracy. Finally, the main shaft speed of the FSR wind turbine can reach a steady-state value around 1500 rpm. The response time of the results derived using VUFC, compared with that derived from a neural network controller, is only less than 0.5 second and there is no overshoot. The case study with the real machine parameter verifies the effectiveness of the proposal and results compared with conventional neural network controller, proving its outperformance

Prediction of lung papillary adenocarcinoma-specific survival using ensemble machine learning models

Abstract Accurate prognostic prediction is crucial for treatment decision-making in lung papillary adenocarcinoma (LPADC). The aim of this study was to predict cancer-specific survival in LPADC using ensemble machine learning and classical Cox regression models. Moreover, models were evaluated to provide recommendations based on quantitative data for personalized treatment of LPADC. Data of patients diagnosed with LPADC (2004–2018) were extracted from the Surveillance, Epidemiology, and End Results database. The set of samples was randomly divided into the training and validation sets at a ratio of 7:3. Three ensemble models were selected, namely gradient boosting survival (GBS), random survival forest (RSF), and extra survival trees (EST). In addition, Cox proportional hazards (CoxPH) regression was used to construct the prognostic models. The Harrell’s concordance index (C-index), integrated Brier score (IBS), and area under the time-dependent receiver operating characteristic curve (time-dependent AUC) were used to evaluate the performance of the predictive models. A user-friendly web access panel was provided to easily evaluate the model for the prediction of survival and treatment recommendations. A total of 3615 patients were randomly divided into the training and validation cohorts (n = 2530 and 1085, respectively). The extra survival trees, RSF, GBS, and CoxPH models showed good discriminative ability and calibration in both the training and validation cohorts (mean of time-dependent AUC: > 0.84 and > 0.82; C-index: > 0.79 and > 0.77; IBS: < 0.16 and < 0.17, respectively). The RSF and GBS models were more consistent than the CoxPH model in predicting long-term survival. We implemented the developed models as web applications for deployment into clinical practice (accessible through https://shinyshine-820-lpaprediction-model-z3ubbu.streamlit.app/ ). All four prognostic models showed good discriminative ability and calibration. The RSF and GBS models exhibited the highest effectiveness among all models in predicting the long-term cancer-specific survival of patients with LPADC. This approach may facilitate the development of personalized treatment plans and prediction of prognosis for LPADC

3D Characterization of Pore Structure and Pore Scale Seepage Simulation of Sandstone Based on Computational Tomography

The microscopic pore structure of sandstone determines its macroscopic permeability. Based on computer tomography (CT) technology, CT scans were performed on three different types of sandstone pore structures, namely coarse sandstone, medium sandstone, and fine sandstone. And the three-dimensional microscopic structure of sandstone pores was reconstructed. Furthermore, based on the Navier–Stokes equations, the fluid flow process in the pore structure of sandstone was simulated, and the effective permeability of sandstone was obtained. By extracting the pore structure from sandstone CT images, the average porosity of coarse sandstone, medium sandstone, and fine sandstone was 16.43%, 12.03%, and 11.64%, respectively. And the porosity of unconnected pores is less than 0.5%. The porosity and permeability of coarse sandstone are higher than those of medium sandstone and fine sandstone with an average value of 1.7 D. The porosity of medium sandstone and fine sandstone is relatively similar. However, the average pore radius and pore throat radius of medium sandstone are larger than those of fine sandstone. More importantly, although the permeability and porosity of sandstone are generally linearly related, when the porosity is low, the data show a large dispersion, and auxiliary indicators such as pore structure characteristic parameters such as pore throat radius should be adopted to evaluate the permeability of sandstone. The flow trajectory of fluid in the pore structure of sandstone is revealed through the streamline of fluid in the pore structure, revealing the mechanism of fluid flow

Human Umbilical Cord Mesenchymal Stem Cells: A New Therapeutic Option for Tooth Regeneration

Tooth regeneration is considered to be an optimistic approach to replace current treatments for tooth loss. It is important to determine the most suitable seed cells for tooth regeneration. Recently, human umbilical cord mesenchymal stem cells (hUCMSCs) have been regarded as a promising candidate for tissue regeneration. However, it has not been reported whether hUCMSCs can be employed in tooth regeneration. Here, we report that hUCMSCs can be induced into odontoblast-like cells in vitro and in vivo. Induced hUCMSCs expressed dentin-related proteins including dentin sialoprotein (DSP) and dentin matrix protein-1 (DMP-1), and their gene expression levels were similar to those in native pulp tissue cells. Moreover, DSP- and DMP-1-positive calcifications were observed after implantation of hUCMSCs in vivo. These findings reveal that hUCMSCs have an odontogenic differentiation potency to differentiate to odontoblast-like cells with characteristic deposition of dentin-like matrix in vivo. This study clearly demonstrates hUCMSCs as an alternative therapeutic cell source for tooth regeneration