大规模风电集群功率输出不确定性对接入电网频率造成不利影响,为了使风电集群与传统电源协调配合共同参与系统调频,提出一种基于随机分层分布式模型预测控制(stochastic-hierarchical-distributed model predictive control,S-H-DMPC)...大规模风电集群功率输出不确定性对接入电网频率造成不利影响,为了使风电集群与传统电源协调配合共同参与系统调频,提出一种基于随机分层分布式模型预测控制(stochastic-hierarchical-distributed model predictive control,S-H-DMPC)的风电集群频率控制机会约束目标滚动规划方法(chance constrained goal rolling programming,CCGRP)。首先,建立考虑功率波动相关性的风电集群功率预测误差模型;其次,提出考虑风电集群功率预测误差随机向量的双层机会约束目标滚动规划方法,上层规划侧重电网拓扑结构及全区系统经济性,下层规划侧重平均系统频率增广模型(average system frequency augmented model,ASFAM)及分区运行安全性;最后,提出基于蒙特卡罗随机模拟的模型求解方法,该方法采用仿射变换算法,通过对风电集群功率预测误差随机向量进行抽样实现机会约束条件的处理。仿真算例表明,所提控制方法能有效提高风电集群参与系统调频的准确性,证明了方法的可行性与鲁棒性。展开更多
During environment testing, the estimation of random vibration signals （RVS） is an important technique for the airborne platform safety and reliability. However, the available meth- ods including extreme value envel...During environment testing, the estimation of random vibration signals （RVS） is an important technique for the airborne platform safety and reliability. However, the available meth- ods including extreme value envelope method （EVEM）, statistical tolerances method （STM） and improved statistical tolerance method （ISTM） require large samples and typical probability distri- bution. Moreover, the frequency-varying characteristic of RVS is usually not taken into account. Gray bootstrap method （GBM） is proposed to solve the problem of estimating frequency-varying RVS with small samples. Firstly, the estimated indexes are obtained including the estimated inter- val, the estimated uncertainty, the estimated value, the estimated error and estimated reliability. In addition, GBM is applied to estimating the single flight testing of certain aircraft. At last, in order to evaluate the estimated performance, GBM is compared with bootstrap method （BM） and gray method （GM） in testing analysis. The result shows that GBM has superiority for estimating dynamic signals with small samples and estimated reliability is proved to be 100% at the given confidence level.展开更多
文摘大规模风电集群功率输出不确定性对接入电网频率造成不利影响,为了使风电集群与传统电源协调配合共同参与系统调频,提出一种基于随机分层分布式模型预测控制(stochastic-hierarchical-distributed model predictive control,S-H-DMPC)的风电集群频率控制机会约束目标滚动规划方法(chance constrained goal rolling programming,CCGRP)。首先,建立考虑功率波动相关性的风电集群功率预测误差模型;其次,提出考虑风电集群功率预测误差随机向量的双层机会约束目标滚动规划方法,上层规划侧重电网拓扑结构及全区系统经济性,下层规划侧重平均系统频率增广模型(average system frequency augmented model,ASFAM)及分区运行安全性;最后,提出基于蒙特卡罗随机模拟的模型求解方法,该方法采用仿射变换算法,通过对风电集群功率预测误差随机向量进行抽样实现机会约束条件的处理。仿真算例表明,所提控制方法能有效提高风电集群参与系统调频的准确性,证明了方法的可行性与鲁棒性。
基金supported by Aviation Science Foundation of China (No. 20100251006)the Technological Foundation Project (No. J132012C001)
文摘During environment testing, the estimation of random vibration signals （RVS） is an important technique for the airborne platform safety and reliability. However, the available meth- ods including extreme value envelope method （EVEM）, statistical tolerances method （STM） and improved statistical tolerance method （ISTM） require large samples and typical probability distri- bution. Moreover, the frequency-varying characteristic of RVS is usually not taken into account. Gray bootstrap method （GBM） is proposed to solve the problem of estimating frequency-varying RVS with small samples. Firstly, the estimated indexes are obtained including the estimated inter- val, the estimated uncertainty, the estimated value, the estimated error and estimated reliability. In addition, GBM is applied to estimating the single flight testing of certain aircraft. At last, in order to evaluate the estimated performance, GBM is compared with bootstrap method （BM） and gray method （GM） in testing analysis. The result shows that GBM has superiority for estimating dynamic signals with small samples and estimated reliability is proved to be 100% at the given confidence level.
