【作者】 张毅刚； 郁惟镛； 黄成军； 左问；

【Author】 Zhang Yigang Yu Weiyong Huang Chengjun Zuo Wen (Power School,Shanghai Jiao Tong University Shanghai,P.R.CHINA 200240)

【机构】 上海交通大学电力工程系；

【摘要】 基于小波变换的马尔科夫模型(HMM)由于考虑了小波系数之间的相关性,而且在去噪时不存在待定的自由参数,具有更强的自适应性。本文将 HMM 方法改进并扩展至小波包域,用于去除发电机局部放电信号中的白噪声。我们采用了实测的局放信号验证方法的有效性。结果证明,对比传统的门限去噪算法,基于小波包的 HMM 方法有更好的去噪效果,而与基于小波变换的 HMM 方法相比,建立的模型更能体现信号的特征,对于信号分析乃至进一步的模式式别有着更大的价值。更多还原

【Abstract】 Wavelet-domain hidden markov models(HMMs)have recently been introduced and applied to signal and image processing.The advantage of the method is that the HMMs measure the dependency between the wavelet coefficients and have no free parameters in denoising.In this paper,the Wavelet-Package-based HMMs method is developed to reduce partial discharge(PD)white noise.The effectiveness of the method is demonstrated by using numerical simulations and real-world data of neutral point current of generator.Compared with Shrinkage method,the result shows that the HMMs method is better in enhancing signal-to-noise ratio and reserves more PD impulses.更多还原