HSPdigital-CA Mecatronica, Facultad de Ingenieria, Universidad Autonoma de Queretaro, Campus San Juan del Rio, Col. San Cayetano, San Juan del Rio, Qro. 76807, Mexico.
Sensors (Basel). 2013 Apr 25;13(5):5507-27. doi: 10.3390/s130505507.
Power quality disturbance (PQD) monitoring has become an important issue due to the growing number of disturbing loads connected to the power line and to the susceptibility of certain loads to their presence. In any real power system, there are multiple sources of several disturbances which can have different magnitudes and appear at different times. In order to avoid equipment damage and estimate the damage severity, they have to be detected, classified, and quantified. In this work, a smart sensor for detection, classification, and quantification of PQD is proposed. First, the Hilbert transform (HT) is used as detection technique; then, the classification of the envelope of a PQD obtained through HT is carried out by a feed forward neural network (FFNN). Finally, the root mean square voltage (Vrms), peak voltage (Vpeak), crest factor (CF), and total harmonic distortion (THD) indices calculated through HT and Parseval's theorem as well as an instantaneous exponential time constant quantify the PQD according to the disturbance presented. The aforementioned methodology is processed online using digital hardware signal processing based on field programmable gate array (FPGA). Besides, the proposed smart sensor performance is validated and tested through synthetic signals and under real operating conditions, respectively.
由于越来越多的干扰负载连接到电力线上，以及某些负载对其存在的敏感性，电能质量干扰（PQD）监测已成为一个重要问题。在任何实际电力系统中，都有多个干扰源，其干扰幅度不同，出现时间也不同。为了避免设备损坏并估计损坏程度，必须对其进行检测、分类和量化。在这项工作中，提出了一种用于检测、分类和量化电能质量干扰的智能传感器。首先，使用希尔伯特变换（HT）作为检测技术；然后，通过前馈神经网络（FFNN）对通过 HT 获得的 PQD 包络进行分类。最后，根据所呈现的干扰，通过 HT 和 Parseval 定理计算的均方根电压（Vrms）、峰值电压（Vpeak）、峰值因数（CF）和总谐波失真（THD）指数以及瞬时指数时间常数来量化 PQD。上述方法使用基于现场可编程门阵列（FPGA）的数字硬件信号处理进行在线处理。此外，通过合成信号和实际运行条件分别验证和测试了所提出的智能传感器的性能。
