论文标题
电力质量事件识别和分类使用基于小波的在线顺序极限学习机网络
Power Quality Event Recognition and Classification Using an Online Sequential Extreme Learning Machine Network based on Wavelets
论文作者
论文摘要
降低的系统可靠性和更高的维护成本可能是电力质量差的结果，这可能会干扰正常设备的性能，加快衰老的速度，甚至导致完全失败。这项研究实施并测试了基于小波的在线顺序极端学习机（OS-ELM）分类器的原型，以检测瞬态条件下的功率质量问题。为了创建分类器，将OSELM-NETWORK模型和离散小波变换（DWT）方法组合在一起。首先，使用离散的小波变换（DWT）多分辨率分析（MRA）在各种分辨率下提取扭曲信号的特征。然后，OSELM通过瞬态持续时间和能量特征对检索到的数据进行分类，以确定干扰的种类。建议的方法需要更少的内存空间和处理时间，因为它可以最大程度地减少大量失真信号的特性而不会改变信号的原始质量。 Several types of transient events were used to demonstrate the classifier's ability to detect and categorize various types of power disturbances, including sags, swells, momentary interruptions, oscillatory transients, harmonics, notches, spikes, flickers, sag swell, sag mi, sag harm, swell trans, sag spike, and swell spike.
Reduced system dependability and higher maintenance costs may be the consequence of poor electric power quality, which can disturb normal equipment performance, speed up aging, and even cause outright failures. This study implements and tests a prototype of an Online Sequential Extreme Learning Machine (OS-ELM) classifier based on wavelets for detecting power quality problems under transient conditions. In order to create the classifier, the OSELM-network model and the discrete wavelet transform (DWT) method are combined. First, discrete wavelet transform (DWT) multi-resolution analysis (MRA) was used to extract characteristics of the distorted signal at various resolutions. The OSELM then sorts the retrieved data by transient duration and energy features to determine the kind of disturbance. The suggested approach requires less memory space and processing time since it can minimize a large quantity of the distorted signal's characteristics without changing the signal's original quality. Several types of transient events were used to demonstrate the classifier's ability to detect and categorize various types of power disturbances, including sags, swells, momentary interruptions, oscillatory transients, harmonics, notches, spikes, flickers, sag swell, sag mi, sag harm, swell trans, sag spike, and swell spike.
