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首页> 外文期刊>IEEE Transactions on Industrial Electronics >MLP/RBF Neural-Networks-Based Online Global Model Identification of Synchronous Generator
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MLP/RBF Neural-Networks-Based Online Global Model Identification of Synchronous Generator

机译：基于MLP / RBF神经网络的同步发电机在线全局模型辨识

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摘要

This paper compares the performances of a multilayer perceptron neural network (MLPN) and a radial basis function neural network (RBFN) for online identification of the nonlinear dynamics of a synchronous generator in a power system. The computational requirement to process the data during the online training, local convergence, and online global convergence properties are investigated by time-domain simulations. The performances of the identifiers as a global model, which are trained at different stable operating conditions, are compared using the actual signals as well as the deviation signals for the inputs of the identifiers. Such an online-trained identifier with fixed optimal weights after the global convergence test is needed to provide information about the plant to a neurocontroller. The use of the fixed weights is to provide against a sensor failure in which case the training of the identifiers would be automatically stopped, and their weights frozen, but the control action, which uses the identifier, would be able to continue.
机译：本文比较了多层感知器神经网络（MLPN）和径向基函数神经网络（RBFN）的性能，用于在线识别电力系统中同步发电机的非线性动力学。通过时域仿真研究了在线训练，局部收敛和在线全局收敛特性期间处理数据的计算要求。使用实际信号以及用于标识符输入的偏差信号来比较在不同的稳定操作条件下训练的标识符作为全局模型的性能。在全局收敛性测试之后，这种具有固定最佳权重的在线训练标识符需要向神经控制器提供有关植物的信息。固定砝码的使用可防止传感器故障，在这种情况下，将自动停止对标识符的训练，并冻结其砝码，但是使用标识符的控制操作将能够继续进行。

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