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ANN applications in fault locators

机译:ANN在故障定位器中的应用

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Recent development indicate that Artificial Neural Networks (ANNs) may be appropriate for assisting dispatchers in operating electric- power systems. The fault location algorithm being a key element in the digital relay for power transmission line protection, this paper discusses the potential applicability of ANN techniques for determination of fault location and fault resistance on EHV transmission lines with remote end in-feed. Most of the applications make use of the conventional Multi Layer Preceptron (MLP) model based on back propagation algorithm. However, this model suffers from the problem of slow learning rate. A modified ANN learning technique for fault location and fault resistance determination is presented in this paper, A reasonably small NN is built automatically without guessing the size, depth, and connectivity pattern of the NN in advance. Results of study on a 400 kv transmission line are presented for illustration purposes. Performance of the modified ANN is compared with the analytical algorithms and conventional MLP algorithm for different combinations of Pre-fault loading condition, fault resistance and fault location. The results are found to be encouraging.
机译:最近的发展表明,人工神经网络(ANN)可能适合于协助调度员操作电力系统。故障定位算法是电力传输线路保护数字继电器中的关键要素,本文讨论了ANN技术在具有远端馈电的超高压输电线路中确定故障位置和故障电阻的潜在适用性。大多数应用程序都使用基于反向传播算法的常规多层感知器(MLP)模型。但是,该模型存在学习速度慢的问题。本文提出了一种用于故障定位和故障抗力确定的改进的人工神经网络学习技术,可以自动构建一个相当小的神经网络,而无需事先猜测神经网络的大小,深度和连通性模式。出于说明目的,介绍了在400 kV输电线路上的研究结果。将改进的人工神经网络的性能与解析算法和常规MLP算法进行比较,以针对故障前加载条件,故障抗​​力和故障位置的不同组合。发现结果令人鼓舞。

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