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首页> 外文期刊>Industrial Electronics, IEEE Transactions on >Wound-Rotor Induction Generator Inter-Turn Short-Circuits Diagnosis Using a New Digital Neural Network
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Wound-Rotor Induction Generator Inter-Turn Short-Circuits Diagnosis Using a New Digital Neural Network

机译:使用新型数字神经网络的绕线感应发电机匝间短路诊断

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摘要

This paper deals with a new transformation and fusion of digital input patterns used to train and test feedforward neural network for a wound-rotor three-phase induction machine windings short-circuit diagnosis. The single type of short-circuits tested by the proposed approach is based on turn-to-turn fault which is known as the first stage of insulation degradation. Used input/output data have been binary coded in order to reduce the computation complexity. A new procedure, namely addition and mean of the set of same rank, has been implemented to eliminate the redundancy due to the periodic character of input signals. However, this approach has a great impact on the statistical properties on the processed data in terms of richness and of statistical distribution. The proposed neural network has been trained and tested with experimental signals coming from six current sensors implemented around a setup with a prime mover and a 5.5 kW wound-rotor three-phase induction generator. Both stator and rotor windings have been modified in order to sort out first and last turns in each phase. The experimental results highlight the superiority of using this new procedure in both training and testing modes.
机译:本文针对用于训练和测试绕线转子三相感应电机绕组短路诊断的前馈神经网络,对数字输入模式进行了新的变换和融合。通过提出的方法测试的单一类型的短路是基于匝间故障的,这被称为绝缘劣化的第一阶段。使用的输入/输出数据已进行二进制编码,以降低计算复杂度。为了消除由于输入信号的周期性特性引起的冗余,已经实施了一种新的过程,即相同等级的集合的加法和均值。但是,这种方法对处理数据的统计属性的丰富性和统计分布影响很大。所提议的神经网络已通过来自六个电流传感器的实验信号进行了训练和测试,这些电流传感器围绕具有原动机和5.5 kW绕线转子三相感应发电机的装置实施。定子绕组和转子绕组都经过修改,以便在每个阶段中分出第一匝和最后一匝。实验结果凸显了在培训和测试模式下使用此新程序的优越性。

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