Failure prediction plays an important role for many tasks such as optimal resource management in large-scale system. However, accurately failure number prediction of repairable large-scale long-running computing （RLL...Failure prediction plays an important role for many tasks such as optimal resource management in large-scale system. However, accurately failure number prediction of repairable large-scale long-running computing （RLLC） is a challenge because of the reparability and large-scale. To address the challenge, a general Bayesian serial revision prediction method based on Bootstrap approach and moving average approach is put forward, which can make an accurately prediction for the failure number. To demonstrate the performance gains of our method, extensive experiments on the data of Los Alamos National Laboratory （LANL） cluster is implemented, which is a typical RLLC system. And experimental results show that the prediction accuracy of our method is 80.2 %, and it is a greatly improvement with 4 % compared with some typical methods. Finally, the managerial implications of the models are discussed.展开更多
当前,我国东部部分沿海地区已形成了规模化的新能源发电集群,海上风电场已从长距离放射形接入电网的形式,逐渐发展为登陆后近距离汇聚再接入主网的接线形式。沿用传统单风电场接入系统的无功电压灵敏度方式响应自动电压控制(automatic v...当前,我国东部部分沿海地区已形成了规模化的新能源发电集群,海上风电场已从长距离放射形接入电网的形式,逐渐发展为登陆后近距离汇聚再接入主网的接线形式。沿用传统单风电场接入系统的无功电压灵敏度方式响应自动电压控制(automatic voltage control, AVC)主站电压指令的运行过程中,易引发局部电网电压异常波动。为此提出了一种风电集群AVC子站无功电压灵敏度协同控制策略。考虑各风电场的交互关系,推导了多种风电集群并网拓扑形式下AVC子站无功电压灵敏度计算方法,并提出了解耦的AVC子站无功电压灵敏度协同控制策略;进一步,具体分析了不同子站无功源出力分配方式下的场内网损。基于MATLAB/MATPOWER平台搭建了三种典型风电集群汇聚模型并对比验证了算法的有效性。算例结果表明,相比传统无功电压灵敏度算法,所提算法能够调节风电集群无功出力以平稳有效应对AVC主站电压指令,在电压偏差指令变化、拓扑结构变化和风电出力水平不同条件下均能够较好地实现各子站并网点电压偏差控制,维持电网电压稳定运行。展开更多
基金supported by the National Natural Science Foundationof China (60701006 60804054 71071158)
文摘Failure prediction plays an important role for many tasks such as optimal resource management in large-scale system. However, accurately failure number prediction of repairable large-scale long-running computing （RLLC） is a challenge because of the reparability and large-scale. To address the challenge, a general Bayesian serial revision prediction method based on Bootstrap approach and moving average approach is put forward, which can make an accurately prediction for the failure number. To demonstrate the performance gains of our method, extensive experiments on the data of Los Alamos National Laboratory （LANL） cluster is implemented, which is a typical RLLC system. And experimental results show that the prediction accuracy of our method is 80.2 %, and it is a greatly improvement with 4 % compared with some typical methods. Finally, the managerial implications of the models are discussed.
文摘当前,我国东部部分沿海地区已形成了规模化的新能源发电集群,海上风电场已从长距离放射形接入电网的形式,逐渐发展为登陆后近距离汇聚再接入主网的接线形式。沿用传统单风电场接入系统的无功电压灵敏度方式响应自动电压控制(automatic voltage control, AVC)主站电压指令的运行过程中,易引发局部电网电压异常波动。为此提出了一种风电集群AVC子站无功电压灵敏度协同控制策略。考虑各风电场的交互关系,推导了多种风电集群并网拓扑形式下AVC子站无功电压灵敏度计算方法,并提出了解耦的AVC子站无功电压灵敏度协同控制策略;进一步,具体分析了不同子站无功源出力分配方式下的场内网损。基于MATLAB/MATPOWER平台搭建了三种典型风电集群汇聚模型并对比验证了算法的有效性。算例结果表明,相比传统无功电压灵敏度算法,所提算法能够调节风电集群无功出力以平稳有效应对AVC主站电压指令,在电压偏差指令变化、拓扑结构变化和风电出力水平不同条件下均能够较好地实现各子站并网点电压偏差控制,维持电网电压稳定运行。
