基于时域、多分辨率分析和规则基的电能质量扰动分类。
Power quality disturbance classification based on time domain, rule based and wavelet multi - resolution decomposition.
提出了一种基于S变换和扩张神经网络的电能质量扰动分类方法。
A new approach combining S-transform and Extension Neural Network (ENN) to classify power quality disturbances is proposed in this paper.
同时，文中还对目前尚未解决的复合电能质量扰动分类问题进行了初步的探讨。
Furthermore, classification of complex power quality disturbances which has not been resolved is discussed in the thesis.
针对电能质量扰动具有非稳态、突发性的特点，提出了一种电能质量短期扰动分类和检测的新方法。
Aimed at the characteristics of power quality disturbance, a new method is introduced for detecting. classifying and quantifying the short duration variations of power quality.
该文方法为动态电能质量扰动的检测、识别与分类提供了新的思路。
The new method gives a entirely new way for detection, identification and classification of dynamic power quality disturbances.
利用所构造的复数正交紧支对称小波，分别研究了电网电能质量扰动的检测、定位与分类问题。
From this, detection, location and classification of power quality's disturbances are analyzed respectively, by the symmetry complex compactly-supported orthonormal wavelet constructed.
该方法利用小波和小波包各自的时频分解特点，实现了暂态电能质量扰动的自动检测和分类。
Using the different time-frequency characteristics of wavelet and wavelet packet, the proposed approach can avoid the noise and complete the detection and classification of transient power quality.
为提高电压扰动信号分类识别的精度，提出了一种基于数学形态学与动态时间扭曲的新算法。
A novel algorithm based on the mathematical morphology and the dynamic time warping was proposed for improving the accuracy of voltage disturbance classification.
提出了一种对电能质量扰动进行检测、定位、辨识与分类的有效方法。
An effective method to detect, localize, identify, and classify power quality disturbance is proposed.
针对电能质量的短时扰动的分类问题，提出了一种基于广义s变换和模糊模式识别的短时电能质量的分类方法。
A new approach of classification of short duration power quality disturbances using generalized S-transform and fuzzy pattern recognition is proposed.
提出了电能质量扰动的专家概率分类器模型，用于常见电能质量扰动的分类。
An expert based probability classification model is employed to investigate generic power quality disturbances.
提出了电能质量扰动的专家概率分类器模型，用于常见电能质量扰动的分类。
An expert based probability classification model is employed to investigate generic power quality disturbances.
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