In this paper, a new method has been introduced to find the most vulnerable lines in the system dynamically in an interconnected power system to help with the security and load flow analysis in these networks. Using t...In this paper, a new method has been introduced to find the most vulnerable lines in the system dynamically in an interconnected power system to help with the security and load flow analysis in these networks. Using the localization of power networks, the power grid can be divided into several divisions of sub-networks in which, the connection of the elements is stronger than the elements outside of that division. By using our proposed method, the probable important lines in the network can be identified to do the placement of the protection apparatus and planning for the extra extensions in the system. In this paper, we have studied the pathfinding strategies in most vulnerable line detection in a partitioned network. The method has been tested on IEEE39-bus system which is partitioned using hierarchical spectral clustering to show the feasibility of the proposed method.展开更多
电力系统多样化的运行方式对薄弱支路辨识的速度与拓扑泛化性提出更高要求。结合图深度学习及解释方法对薄弱支路进行辨识与溯因分析。采用基于初始残差和单位映射的图卷积神经网络(graph convolutional network via initial residual a...电力系统多样化的运行方式对薄弱支路辨识的速度与拓扑泛化性提出更高要求。结合图深度学习及解释方法对薄弱支路进行辨识与溯因分析。采用基于初始残差和单位映射的图卷积神经网络(graph convolutional network via initial residual and identity mapping,GCNII)搭建薄弱支路辨识模型,模型可基于拓扑关系聚合元件特征,结合邻近电网的安全态势评估支路的薄弱程度。采用基于互信息优化的解释方法分析辨识模型的决策依据,提取薄弱支路的主导因子。IEEE68节点系统、实际电网算例结果表明,辨识模型具有较好的辨识准确性和拓扑泛化性,溯因分析结果符合传统机理认知所得结论,可为连锁故障的实时预警和预防控制提供有效指导。展开更多
文摘In this paper, a new method has been introduced to find the most vulnerable lines in the system dynamically in an interconnected power system to help with the security and load flow analysis in these networks. Using the localization of power networks, the power grid can be divided into several divisions of sub-networks in which, the connection of the elements is stronger than the elements outside of that division. By using our proposed method, the probable important lines in the network can be identified to do the placement of the protection apparatus and planning for the extra extensions in the system. In this paper, we have studied the pathfinding strategies in most vulnerable line detection in a partitioned network. The method has been tested on IEEE39-bus system which is partitioned using hierarchical spectral clustering to show the feasibility of the proposed method.
文摘电力系统多样化的运行方式对薄弱支路辨识的速度与拓扑泛化性提出更高要求。结合图深度学习及解释方法对薄弱支路进行辨识与溯因分析。采用基于初始残差和单位映射的图卷积神经网络(graph convolutional network via initial residual and identity mapping,GCNII)搭建薄弱支路辨识模型,模型可基于拓扑关系聚合元件特征,结合邻近电网的安全态势评估支路的薄弱程度。采用基于互信息优化的解释方法分析辨识模型的决策依据,提取薄弱支路的主导因子。IEEE68节点系统、实际电网算例结果表明,辨识模型具有较好的辨识准确性和拓扑泛化性,溯因分析结果符合传统机理认知所得结论,可为连锁故障的实时预警和预防控制提供有效指导。
