为提高风电场的低-高电压连续故障穿越能力,提出一种基于虚拟磁链的静止无功发生器(static var generator,SVG)控制策略。首先,分析双馈感应发电机、调相机和SVG对暂态电压的支撑效果;其次,建立同步调相机与SVG的暂态无功响应模型,明确...为提高风电场的低-高电压连续故障穿越能力,提出一种基于虚拟磁链的静止无功发生器(static var generator,SVG)控制策略。首先,分析双馈感应发电机、调相机和SVG对暂态电压的支撑效果;其次,建立同步调相机与SVG的暂态无功响应模型,明确磁链不突变是两者暂态电压支撑差异的关键因素;再次,将调相机磁链守恒性质叠加到SVG暂态响应特性中,提出基于虚拟磁链的SVG控制策略,并调整控制器参数,确保SVG的无功响应能力最大化;最后,通过仿真验证提出的SVG控制策略对暂态低电压的支撑和过电压的抑制都有良好效果,能够提高风电机组的故障穿越能力。展开更多
To improve the efficiency of air quality analysis and the accuracy of predictions, this paper proposes a composite method based on Vector Autoregressive (VAR) and Random Forest (RF) models. In the theoretical section,...To improve the efficiency of air quality analysis and the accuracy of predictions, this paper proposes a composite method based on Vector Autoregressive (VAR) and Random Forest (RF) models. In the theoretical section, the model introduction and estimation algorithms are provided. In the empirical analysis section, global air quality data from 2022 to 2024 are used, and the proposed method is applied. Specifically, principal component analysis (PCA) is first conducted, and then VAR and Random Forest methods are used for prediction on the reduced-dimensional data. The results show that the RMSE of the hybrid model is 45.27, significantly lower than the 49.11 of the VAR model alone, verifying its superiority. The stability and predictive performance of the model are effectively enhanced.展开更多
文摘为提高风电场的低-高电压连续故障穿越能力,提出一种基于虚拟磁链的静止无功发生器(static var generator,SVG)控制策略。首先,分析双馈感应发电机、调相机和SVG对暂态电压的支撑效果;其次,建立同步调相机与SVG的暂态无功响应模型,明确磁链不突变是两者暂态电压支撑差异的关键因素;再次,将调相机磁链守恒性质叠加到SVG暂态响应特性中,提出基于虚拟磁链的SVG控制策略,并调整控制器参数,确保SVG的无功响应能力最大化;最后,通过仿真验证提出的SVG控制策略对暂态低电压的支撑和过电压的抑制都有良好效果,能够提高风电机组的故障穿越能力。
文摘To improve the efficiency of air quality analysis and the accuracy of predictions, this paper proposes a composite method based on Vector Autoregressive (VAR) and Random Forest (RF) models. In the theoretical section, the model introduction and estimation algorithms are provided. In the empirical analysis section, global air quality data from 2022 to 2024 are used, and the proposed method is applied. Specifically, principal component analysis (PCA) is first conducted, and then VAR and Random Forest methods are used for prediction on the reduced-dimensional data. The results show that the RMSE of the hybrid model is 45.27, significantly lower than the 49.11 of the VAR model alone, verifying its superiority. The stability and predictive performance of the model are effectively enhanced.
