论文将支持向量机引入到动态电能质量分类问题中。
This paper presents a Support Vector Machine (SVM) method for classification of dynamic power quality disturbances.
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.
文章第二部分对电能质量问题进行了分类，分别对各种电能质量问题进行了介绍。
The second part of the thesis classifies the problems of power quality. This part introduces all power quality problems.
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.
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.
本文讨论了电能质量问题的分类、产生原因、危害和解决方法。
The classification of power quality problems, their causing reasons, harm, and management methods are discussed in the paper.
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.
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.
利用ANN对输入特征矢量进行识别，完成电能质量的自动分类。
Then ANN was used for automatic conversion of the power quality signals the feature vectors.
文章从电能质量问题的分类、产生原因及改善电能质量问题的措施等几个方面进行了阐述。
The paper makes an analysis from several aspects, such as the problem classification of electric power energy quality, its reason and the improving measures.
本文主要研究了应用规则基专家系统对电能质量信号进行分类。
This thesis is mainly concerned with problems of power quality disturbance signal's analysis and classifies by rule-based expert system.
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.
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.
最后，提出了一种基于子频带特征提取和二叉树结构支持向量机相结合的电能质量多分类方法。
Finally, a method based on sub-band feature extraction and Support Vector Machine with Binary Tree Architecture (SVM-BTA) is presented for power quality disturbances multi-classification.
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.
为了采取合理的措施提高电能质量，必须建立电能质量监测分析系统，对其进行正确地检测、评估和分类。
To improve the electric power quality, a monitoring and analyzing system must be established to detect, estimate and classify different disturbances.
本文介绍了现代电能质量的定义、分类以及动态电能质量现象的描述。
This paper firstly introduces the definition and classify of modern power quality and the phenomena of dynamic voltage quality.
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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