| 引用本文: | 周沙,景亮.基于矩特征与概率神经网络的局部放电模式识别[J].电力系统保护与控制,2016,44(3):98-102. |
| ZHOU Sha,JING Liang.Pattern recognition of partial discharge based on moment features and probabilistic neural network[J].Power System Protection and Control,2016,44(3):98-102 |
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| 摘要: |
| 局部放电信号检测时易受随机噪声干扰，会影响到局部放电模式识别的识别率和识别速度。为了提高局部放电模式识别的识别率和识别速度，提出了一种基于矩特征与概率神经网络的局部放电模式识别器。该识别器首先从放电类型的三维谱图中提取矩特征，然后，将矩特征作为概率神经网络的输入对局部放电模式进行识别。采集了尖板放电和球板放电两种放电类型，将所提识别器与反传神经网络、贝叶斯分类器、极限学习机进行了对比。实验结果表明，所提基于矩特征和概率神经网络的局部放电模式识别器的分类效果令人满意。 |
| 关键词: 局部放电 模式识别 局部放电相位分析 矩特征 概率神经网络 |
| DOI：10.7667/PSPC150708 |
| 分类号: |
| 基金项目:国家自然科学基金(61304261) |
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| Pattern recognition of partial discharge based on moment features and probabilistic neural network |
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ZHOU Sha, JING Liang
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School of Electric and Information Engineering, Jiangsu University, Zhenjiang 212013, China
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| Abstract: |
| The detection of partial discharge (PD) signal is generally noised. It is bad for the recognition rate and velocity of partial discharge recognition. The recognizer based on moment feature and probabilistic neural network (PNN) is proposed to improve the recognition rate and velocity of partial discharge recognition. The moment feature is extracted from the three-dimensional spectra of PD signal. The moment feature is regarded as the input of PNN. Then, the partial discharge recognition is done. The proposed recognizer is compared to Back Propagation (BP) neural network, Bayesian classifier and extreme learning machine (ELM) on the needle-plane discharge and the ball-plane discharge. Experiment results show that the recognition of the proposed recognizer based on moment feature and PNN is satisfactory. |
| Key words: partial discharge pattern recognition phase-resolved partial discharge moment-feature probabilistic neural networks |
