Using self-organizing map in identification of load-loss state

X. Luo, C. Singh, A. Patton
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引用次数: 3

Abstract

This paper presents a self-organizing map (SOM) based method for power system load-loss state classification. This classifier maps vectors of an N-dimensional space to a 2-dimensional net in a nonlinear way while preserving the topological order of the input vectors. Input features to SOM are real and reactive power at each load bus and available real power generation at each generation bus. After the training of the SOM, the generalization capability of the SOM can cope with various operating conditions which have not been encountered during the training phase and hence give a correct classification result. The effectiveness of the proposed method has been demonstrated on a 9-bus test system. This proposed method is useful for power system operation, power system reliability assessment and state screening.
利用自组织映射识别负载损失状态
提出了一种基于自组织映射(SOM)的电力系统负荷损耗状态分类方法。该分类器以非线性方式将n维空间的向量映射到2维网络,同时保持输入向量的拓扑顺序。SOM的输入特征是每个负载总线上的实功率和无功功率以及每个发电总线上可用的实际发电量。经过SOM的训练,SOM的泛化能力可以应对训练阶段没有遇到的各种操作条件,从而给出正确的分类结果。该方法的有效性已在一个9总线测试系统上得到验证。该方法可用于电力系统运行、可靠性评估和状态筛选。
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