Goldwind (China)

The high proportion of wind power access makes the system frequency regulation face serious challenges, and the time delay of wind turbine FM response exacerbates the frequency security problem. For this reason, this paper proposes a joint dispatch model for power grid and wind farms considering frequency modulation delay. First, the wind turbine response characteristics and frequency safety constraints are derived by equivalently modeling the wind turbine FM delay. Second, power grid-wind farm joint dispatch model is constructed on this basis, where the system level optimizes the operation cost under the premise of satisfying the frequency safety constraints, and the wind farm level tracks the wind power output target issued by the system to meet the FM demand. Finally, by the case study, Scenario 1 reduces average frequency nadir deviation from 0.205 Hz to 0.098 Hz and RoCoF from 0.216 Hz/s to 0.168 Hz/s in the IEEE-39 system. The stability of the system is enhanced, which verifies the effectiveness of the proposed method.

Background and objectiveThreatened abortion, a common pregnancy complication that often leading to abortion, is hard to predict due to its non-specific symptoms and difficulty in differentiating from other early pregnancy bleeding causes. Current diagnostic methods like serial ultrasounds and clinical monitoring are time-consuming and lack timeliness. To fill the gap in using advanced analytics for early detection and risk stratification, this study develops a machine learning (ML) model based on routine blood data to better predict threatened abortion, providing a reference for early detection and intervention.MethodsIn this study, we collected medical records from January 2022 to March 2024. We analyzed data from 1764 patients with threatened abortion and 1489 healthy controls. Blood test data of all participants were gathered. The Z-score normalization technique was applied to standardize blood routine indicators. This reduced the influence of outliers and noise. During hyperparameter optimization, ‘class_weight="balanced"’ was set to handle sample imbalance. The screening data was partitioned into a training set of 2928 cases (including the validation set) and a test set of 325 cases at an 8:1:1 ratio. Python was used to facilitate data transformation. Eight different ML algorithms—Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting (GBM), Extreme Gradient Boosting (XGB), Deep Neural Network (DNN), Decision Tree (DT) and Naive Bayes (NB)—were used to construct a threatened abortion prediction model. The prediction performances of the ML models were evaluated by calculating the area under the curve (AUC) values. We used the SHapley Additive exPlanation (SHAP) method to explain the models.ResultsComparatively, the DNN model showed the highest predictive performance among the eight models, with the highest AUC value of 96.76% and top metrics for accuracy (91.88%), specificity (91.62%), sensitivity (92.11%), and F1 score (92.48%). SHAP analysis identified Red Cell Distribution Width - Standard Deviation (RDW-SD), Platelet Distribution Width (PDW), Mean Platelet Volume (MPV), Red Cell Distribution Width - Coefficient of Variation (RDW-CV), Absolute Basophil Count (BAS#), Platelet Count (PLT), Mean Corpuscular Hemoglobin Concentration (MCHC) and Lymphocyte Percentage (LYM) as the most influential features in predicting threatened abortion, with PDW, RDW-CV, BAS#, PLT, MCHC and LYM positively contributing to the prediction, whereas RDW-SD and MPV had negative contributions.ConclusionsOur research on constructing a prediction model for threatened abortion through routine blood tests has revealed the great potential of ML algorithms in detecting threatened abortion. This algorithm is expected to analyse routine blood data to identify at-risk pregnancies at an early stage, significantly improving the early detection of this common pregnancy complication. It will assist healthcare providers in intervening earlier and reducing the incidence of abortion. However, before the model can be translated into routine clinical applications, more extensive validation studies are still needed.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12884-025-08030-z.

A digital twin for a wind turbine gearbox (WTG-DT) is essential for advancing wind farm intelligence and improving the efficiency of wind turbine operation. This study addresses key limitations of existing condition monitoring systems, such as low accuracy and slow parameter updates, by proposing a long short-term memory (LSTM) network to intelligently calibrate model parameters. This ensures the real-time operation and maintenance of wind turbines. A high-fidelity dynamic model is developed and validated by performing frequency analysis of vibration signals collected through the condition monitoring system (CMS), with wind speed and load data from the supervisory control and data acquisition (SCADA) system as inputs. To simplify the complex finite element analysis process, a parameter sensitivity analysis is conducted, and a GA-PSO optimization algorithm, based on genetic algorithms, is applied. These methods generate sufficient training data for constructing an LSTM-based predictive agent model, which is then used to accurately calibrate the virtual model. When applied to 6 MW turbines, this approach significantly improves real-time performance, accuracy, and reliability, enhancing the overall operational efficiency of wind turbines.

Grid-forming converters have become a promising solution for connecting multiple energy sources such as wind power, photovoltaic, and energy storage into the power system, providing enhanced stability and control capability for high percentage renewable energy power grids. In this paper, an active support control method considering energy constraints is proposed for the stability issues of grid-forming converters in high percentage renewable energy power systems. The relationship between the DC-link voltage stability of the grid-forming converter and the energy buffer boundary and grid energy shock is analyzed from the energy perspective. The internal voltage angular frequency dynamic compensation and phase angle dynamic compensation strategies based on the DC-link voltage are then proposed to r mitigate energy imbalances while considering energy buffer constraints. The effectiveness of the method is validated through PSCAD simulations and control hardware-in-the-loop (CHIL) tests on a grid-forming wind-storage integrated system. Results demonstrate significant improvements in system stability and tolerance to phase angle disturbances.