As the large-scale grid connection of wind turbines poses challenges to the safe and stable operation of the power grid, it is necessary to forecast the power of wind turbine clusters. However, with the rapid increase...
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As the large-scale grid connection of wind turbines poses challenges to the safe and stable operation of the power grid, it is necessary to forecast the power of wind turbine clusters. However, with the rapid increase in the installed capacity of wind turbines, simultaneously improving the accuracy and efficiency of large-scale windturbine cluster power prediction models has become a challenge. In this research, a novel hybrid powerforecasting model for wind-turbine clusters has been designed using density-based spatial clustering of applications with noise (DBSCAN) and an enhanced hunter-prey optimization algorithm (ENHPO). First, the Pearson correlation coefficient was used to select multiple variables that significantly affected the wind power. Second, multiple wind turbines are clustered into different groups using the DBSCAN clustering algorithm, and ENHPO is employed to optimize the DBSCAN parameters to strengthen the clustering performance. Finally, one wind turbine with a high correlation was selected as the representative wind turbine in each group, and power prediction was achieved using the multivariate long short-term memory. Simulation results of four datasets in different seasons show that, compared with other clustering methods, such as fuzzy C-means, balanced iterative reduction, and clustering using hierarchies, K-means, and density peak clustering, the prediction accuracy and prediction efficiency of the proposed hybrid prediction model are improved by 21.85% and 18.07%, respectively, on average. The experimental results effectively demonstrate that the designed model can enhance the accuracy and efficiency of power forecasting simultaneously.
Accurate damage identification is of great significance to maintain timely and prevent structural failure. To accurately and quickly identify the structural damage, a novel two-stage approach based on convolutional ne...
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Accurate damage identification is of great significance to maintain timely and prevent structural failure. To accurately and quickly identify the structural damage, a novel two-stage approach based on convolutional neural networks (CNN) and an improved hunter-prey optimization algorithm (IHPO) is proposed. In the first stage, the cross-correlation-based damage localization index (CCBLI) is formulated using acceleration and is input into the CNN to locate structural damage. In the second stage, the IHPO algorithm is applied to optimize the objective function, and then the damage severity is quantified. A numerical model of the American Society of Civil Engineers (ASCE) benchmark frame structure and a test structure of a three-storey frame are adopted to verify the effectiveness of the proposed method. The results demonstrate that the proposed approach is effective in locating and quantifying structural damage precisely regardless of noise perturbations. In addition, the reliability of the proposed approach is evaluated using a comparison between it and approaches based on CNN or the IHPO algorithm alone. The comparison results indicate that in single and multiple damage events, the proposed two-stage damage identification approach outperforms the other two approaches on the accuracy, and the average consumption time is 20% less than the method using the IHPO algorithm alone. Therefore, this paper provides a guideline for the study of high-accuracy and quick damage identification using both data-based and model-based hybrid methods.
The squeeze film damper (SFD) is a damping device that is widely used in turbomachinery. A well-designed SFD structure can effectively mitigate vibration at critical speeds, while a poorly designed SFD may increase vi...
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The squeeze film damper (SFD) is a damping device that is widely used in turbomachinery. A well-designed SFD structure can effectively mitigate vibration at critical speeds, while a poorly designed SFD may increase vibration. The comprehensive multi-parameter optimization method is used for optimizing the vibration response at critical speed and improving the reliability and overall performance by designing the crucial structural parameters of the SFD. Firstly, the coupling dynamic model is proposed for the rotor and SFD, taking into account the influence of non-linear oil film forces of SFD. The test rig is used to verify the vibration response in a typical SFD-rotor system. To provide the optimal vibration reduction effect within a specific SFD parameter range for a particular rotor, a method for comprehensive multi-parameter optimization is introduced. This method introduces the hunter-prey intelligent optimization (HPO) algorithm and compares its results with the PSO algorithm. The comprehensive optimization method revealed that the key parameters of the SFD, when designed using this approach, can effectively alleviate the vibration response of the entire rotor system, achieving the rotor system amplitude reduction ratio of up to13.89%.
Aiming at the problems of various nonlinear characteristics caused by fiber twist in the manufacturing and packaging process of interventional catheter shape sensor, this paper proposes a fiber grating shape sensor co...
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ISBN:
(纸本)9798350364200;9798350364194
Aiming at the problems of various nonlinear characteristics caused by fiber twist in the manufacturing and packaging process of interventional catheter shape sensor, this paper proposes a fiber grating shape sensor configuration and its packaging method. The HPO-BPNN algorithm is employed to predict discrete curvature and the direction angles of curvature vectors, thus avoiding complex sensor parameter calibration steps. Additionally, the Frenet-Serret equation is integrated to achieve three-dimensional posture reconstruction of the catheter. Through the multi-curvature calibration experiment of the designed sensor, the average error at the end of the 2D curves is 21.15mm (3.91%), and for the 3D curves, it is 26.43mm (4.89%), thereby verifying the feasibility of the data-driven shape reconstruction algorithm.
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