人工神经网络在噪声混合局部放电识别中的应用

Canadian Conference on Electrical and Computer Engineering 2001. Conference Proceedings (Cat. No.01TH8555) Pub Date : 2001-05-13 DOI:10.1109/CCECE.2001.933765

Zhong Zheng, K. Tan

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引用次数: 0

摘要

为了测试局部放电在不同噪声条件下的识别能力，进行了系统的研究。在屏蔽噪声的高压实验室内，采用高速宽带数字测量系统，记录了不同类型的局部放电电流波形。研究了不同类型的噪声。然后将PD信号浸入具有一定信噪比的不同噪声中。采用分段时域数据压缩方法，提取混合波形的特征向量。采用反向传播算法，生成并优化了前馈三层人工神经网络程序。对混合波形进行了测试，研究了不同信噪比条件下各噪声类型对混合波形的影响。

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Application of artificial neural network in noise mixed partial discharge recognition

To test partial discharge (PD) recognition ability under different noise conditions, systemic research is carried out. In a noise-screened high voltage lab and using a high speed, wide-band digital measuring system, different kinds of PD current waveforms are recorded. Noises of different types are investigated. Then the PD signals are immersed into different noises with certain signal-noise ratios (SNR). By applying the segmented time domain data compression method, the features vectors of mixed waveforms are extracted. Employing a backpropagation algorithm, a feedforward triple-layered artificial neural network (ANN) program is generated and optimized. The mixed waveforms are tested and influence of each noise types in different SNR conditions are studied.

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Canadian Conference on Electrical and Computer Engineering 2001. Conference Proceedings (Cat. No.01TH8555)

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