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Application of binary particle swarm optimization in feature selection for transient stability assessment

机译:二元粒子群算法在暂态稳定评估特征选择中的应用

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For the disadvantage of traditional method in the feature selection of power system transient stability assessment, a new method is proposed based on binary particle swarm optimization (BPSO) algorithm. This method can find the model feature set that can reflect the physical natural characteristic of power system transient stability directly or indirectly and illustrate the system dynamic characteristic better. The proposed approach can reduce the input dimension by using Euclidean distance as the fitness function. The test on 8-machine 36-bus system of EPRI reveals the validity of the method mentioned.
机译:针对传统方法在电力系统暂态稳定评估特征选择中的缺点,提出了一种基于二进制粒子群优化算法的新方法。该方法可以找到可以直接或间接反映电力系统暂态稳定性的物理自然特性的模型特征集,从而更好地说明系统的动态特性。所提出的方法可以通过使用欧几里得距离作为适应度函数来减小输入维数。在EPRI的8机36总线系统上的测试表明了所提方法的有效性。

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