IEEE Trans Neural Netw Learn Syst. 2016 Aug;27(8):1631-42. doi: 10.1109/TNNLS.2014.2360879. Epub 2014 Oct 13.
This paper presents the Chebyshev neural network (ChNN) as an improved artificial intelligence technique for power system protection studies and examines the performances of two ChNN learning algorithms for fault classification of series compensated transmission line. The training algorithms are least-square Levenberg-Marquardt (LSLM) and recursive least-square algorithm with forgetting factor (RLSFF). The performances of these algorithms are assessed based on their generalization capability in relating the fault current parameters with an event of fault in the transmission line. The proposed algorithm is fast in response as it utilizes postfault samples of three phase currents measured at the relaying end corresponding to half-cycle duration only. After being trained with only a small part of the generated fault data, the algorithms have been tested over a large number of fault cases with wide variation of system and fault parameters. Based on the studies carried out in this paper, it has been found that although the RLSFF algorithm is faster for training the ChNN in the fault classification application for series compensated transmission lines, the LSLM algorithm has the best accuracy in testing. The results prove that the proposed ChNN-based method is accurate, fast, easy to design, and immune to the level of compensations. Thus, it is suitable for digital relaying applications.
本文提出了切比雪夫神经网络（ChNN）作为一种改进的人工智能技术，用于电力系统保护研究，并研究了两种 ChNN 学习算法在串联补偿线路故障分类中的性能。训练算法是最小二乘 Levenberg-Marquardt（LSLM）和带有遗忘因子的递归最小二乘算法（RLSFF）。这些算法的性能是基于它们在将故障电流参数与线路故障事件相关联的泛化能力来评估的。该算法响应速度快，因为它仅利用了保护端测量的三相电流的故障后样本，对应半周期持续时间。在仅使用生成的故障数据的一小部分进行训练后，这些算法已经在具有广泛系统和故障参数变化的大量故障情况下进行了测试。根据本文进行的研究，虽然 RLSFF 算法在串联补偿线路故障分类应用中用于训练 ChNN 时速度更快，但 LSLM 算法在测试中的准确性最高。结果证明，所提出的基于 ChNN 的方法准确、快速、易于设计，并且不受补偿水平的影响。因此，它适用于数字保护应用。
