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传统的输电阻塞研究主要集中于输电阻塞发生之后的处理过程,即阻塞管理,属于被动消除阻塞情形。文章基于主动预防输电阻塞的思想,从分析影响输电阻塞的系统线路传输功率、系统总负荷、系统实际出力等相关因素入手,应用层次分析法建立比较判断矩阵,以确定各因素阻塞影响的权重。基于此,建立一种基于神经网络的输电阻塞预测模型,还提出了一个新的阻塞指标,即阻塞度,以美国加利福尼亚州电力市场的数据验证了该模型的正确性和实用性。
The traditional research on transmission congestion mainly focuses on the processing after transmission congestion, that is, congestion management, which belongs to the passive elimination of congestion. Based on the idea of proactively preventing transmission congestion, this paper starts with the analysis of the transmission power, total system load and actual output of the system which affect the transmission congestion. The analytic hierarchy process is used to establish the comparison judgment matrix to determine the weight of each factor. Based on this, a transmission congestion prediction model based on neural network is established, and a new obstruction index is proposed, which is the obstruction degree. The correctness and practicability of this model are verified by the data of the California electricity market in the United States.