CAJ | 학술논문

针对电能质量扰动定位和识别分类的需求，提出了一种基于 HHT 的电能质量扰动定位与分类的新方法。采用HHT算法对电能质量扰动信号进行变换，获得瞬时幅值、Hilbert谱和边际谱，并利用Hilbert谱对扰动信号进行定位。从瞬时幅值、Hilbert谱和边际谱中提取特征量，为决策分类树提供判断依据以便进行分类识别。仿真实验结果表明，采用 HHT 算法与决策分类树相结合的电能质量扰动定位与分类不需训练，提取的特征量少而有效，分类识别的效果较好，具有良好的抗噪性能。
침대전능질량우동정위화식별분류적수구，제출료일충기우 HHT 적전능질량우동정위여분류적신방법。채용HHT산법대전능질량우동신호진행변환，획득순시폭치、Hilbert보화변제보，병이용Hilbert보대우동신호진행정위。종순시폭치、Hilbert보화변제보중제취특정량，위결책분류수제공판단의거이편진행분류식별。방진실험결과표명，채용 HHT 산법여결책분류수상결합적전능질량우동정위여분류불수훈련，제취적특정량소이유효，분류식별적효과교호，구유량호적항조성능。
According to the demands of using power quality disturbance for localization, recognition and classification, this paper proposes a new method of using power quality disturbance for localization and classification based on HHT. Firstly, power quality disturbance signals are transformed by using the HHT algorithm to get instantaneous amplitudes, Hilbert spectra and marginal spectra. Secondly, disturbance signals can be located by using instantaneous frequency to record the beginning time and the ending time of disturbance. Thirdly, characteristic parameters can be extracted from instantaneous amplitude, Hilbert spectrum and marginal spectrum, serving as evidence for using the decision-making tree for recognition and classification. The simulation results show that the margin of error can be reduced by using Hilbert spectrum for localization. By combining the HHT algorithm and the decision-making tree, no extra training is needed in using power quality disturbance for recognition and classification. Other advantages include extracting effective but fewer characteristic parameters, more desirable effects of recognition and classification, as well as its favorable noise-proof capability.