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机译:基于扰动后转子角轨迹簇特征的电力系统暂态稳定预测
School of Electrical Engineering, Beijing Jiaotong Univeisity, Beijing, China;
School of Electrical Engineering, Beijing Jiaotong Univeisity, Beijing, China;
School of Electrical Engineering, Beijing Jiaotong Univeisity, Beijing, China;
School of Electrical Engineering, Beijing Jiaotong Univeisity, Beijing, China;
China Electric Power Research Institute, Beijing, China;
transient stability; support vector machine; decision tree; cluster features; machine learning; power systems; phasor measurement unit; incomplete measurements;
机译:后扰动电压轨迹的转子角不稳定性预测
机译:使用稳定性指数向量具有高风力渗透的动力系统转子角度稳定性预测
机译:基于轨迹学习的机器学习方法为瞬态稳定性预测定义最相关的特征
机译:基于扰动后电压轨迹的转子角不稳定性预测
机译:基于DFIG的风力涡轮发电机的穿透力增加对电力系统转子角稳定性的影响。
机译:RaptorX-Angle:通过聚类和深度学习的混合方法对蛋白质骨架二面角进行实值预测
机译:利用轨迹聚类定义基于机器学习方法的暂态稳定预测最相关特征