基于人工神经网络的电力系统精细化安全运行规则
发布时间：2019-05-28 22:03
【摘要】：随着大规模可再生能源不断并网,对电网的实时调控能力提出了更高的要求。传统的基于在线关键断面自动发现以及基于连续潮流的在线极限传输容量计算方法,模型复杂、计算周期长,难以做到在线运行。从数据驱动的角度出发,首先将电网实时运行状态的潮流量抽象为该时刻电网的运行特征;然后对所有特征进行聚类和分布式特征选择;最后运用人工神经网络建立所选特征与关键断面极限传输容量之间的对应关系。算例分析表明,所提基于人工神经网络的电力系统精细化安全运行规则,在保证时间效率的前提下,能够在一定程度上提高关键断面极限传输容量的预测准确度。
[Abstract]:With the continuous grid connection of large-scale renewable energy, the real-time regulation and control ability of power grid is put forward higher requirements. The traditional calculation method of online limit transmission capacity based on online key section automatic discovery and continuous power flow is complex and the calculation period is long, so it is difficult to run online. From the point of view of data-driven, the tidal flow of the real-time operation state of the power grid is abstracted as the operation characteristics of the power grid at that time, and then all the features are clustering and distributed feature selection. Finally, the artificial neural network is used to establish the corresponding relationship between the selected features and the ultimate transmission capacity of the key section. The example analysis shows that the proposed fine and safe operation rules of power system based on artificial neural network can improve the prediction accuracy of critical section limit transmission capacity to a certain extent on the premise of ensuring time efficiency.
【作者单位】： 广东电网有限责任公司电力调度控制中心;北京清大高科系统控制有限公司;
【基金】：南方电网科技项目(GDKJ00000058)“面向大数据的复杂大电网安全特征选择和知识发现的关键技术与示范应用”~~
【分类号】：TM732;TP183
[Abstract]:With the continuous grid connection of large-scale renewable energy, the real-time regulation and control ability of power grid is put forward higher requirements. The traditional calculation method of online limit transmission capacity based on online key section automatic discovery and continuous power flow is complex and the calculation period is long, so it is difficult to run online. From the point of view of data-driven, the tidal flow of the real-time operation state of the power grid is abstracted as the operation characteristics of the power grid at that time, and then all the features are clustering and distributed feature selection. Finally, the artificial neural network is used to establish the corresponding relationship between the selected features and the ultimate transmission capacity of the key section. The example analysis shows that the proposed fine and safe operation rules of power system based on artificial neural network can improve the prediction accuracy of critical section limit transmission capacity to a certain extent on the premise of ensuring time efficiency.
【作者单位】： 广东电网有限责任公司电力调度控制中心;北京清大高科系统控制有限公司;
【基金】：南方电网科技项目(GDKJ00000058)“面向大数据的复杂大电网安全特征选择和知识发现的关键技术与示范应用”~~
【分类号】：TM732;TP183
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