Carní Domenico Luca, Lamonaca Francesco
Department of Computer Engineering, Modeling, Electronics and Systems, University of Calabria, 87046 Rende, Italy.
Sensors (Basel). 2024 Apr 12;24(8):2474. doi: 10.3390/s24082474.
The new power generation systems, the increasing number of equipment connected to the power grid, and the introduction of technologies such as the smart grid, underline the importance and complexity of the Power Quality (PQ) evaluation. In this scenario, an Automatic PQ Events Classifier (APQEC) that detects, segments, and classifies the anomaly in the power signal is needed for the timely intervention and maintenance of the grid. Due to the extension and complexity of the network, the number of points to be monitored is large, making the cost of the infrastructure unreasonable. To reduce the cost, a new architecture for an APQEC is proposed. This architecture is composed of several Locally Distributed Nodes (LDNs) and a Central Classification Unit (CCU). The LDNs are in charge of the acquisition, the detection of PQ events, and the segmentation of the power signal. Instead, the CCU receives the information from the nodes to classify the PQ events. A low-computational capability characterizes low-cost LDNs. For this reason, a suitable PQ event detection and segmentation method with low resource requirements is proposed. It is based on the use of a sliding observation window that establishes a reasonable time interval, which is also useful for signal classification and the multi-sine fitting algorithm to decompose the input signal in harmonic components. These components can be compared with established threshold values to detect if a PQ event occurs. Only in this case, the signal is sent to the CCU for the classification; otherwise, it is discarded. Numerical tests are performed to set the sliding window size and observe the behavior of the proposed method with the main PQ events presented in the literature, even when the SNR varies. Experimental results confirm the effectiveness of the proposal, highlighting the correspondence with numerical results and the reduced execution time when compared to FFT-based methods.
新的发电系统、接入电网的设备数量不断增加以及智能电网等技术的引入，凸显了电能质量（PQ）评估的重要性和复杂性。在这种情况下，需要一种自动PQ事件分类器（APQEC）来检测、分割和分类电力信号中的异常，以便及时对电网进行干预和维护。由于网络的扩展和复杂性，需要监测的点数众多，这使得基础设施成本过高。为了降低成本，提出了一种APQEC的新架构。该架构由多个本地分布式节点（LDN）和一个中央分类单元（CCU）组成。LDN负责电力信号的采集、PQ事件检测和分割。相反，CCU从节点接收信息以对PQ事件进行分类。低成本的LDN具有低计算能力的特点。因此，提出了一种资源需求低的合适的PQ事件检测和分割方法。它基于使用滑动观察窗口来建立合理的时间间隔，这对于信号分类和多正弦拟合算法将输入信号分解为谐波分量也很有用。可以将这些分量与既定阈值进行比较，以检测是否发生PQ事件。仅在这种情况下，信号才会被发送到CCU进行分类；否则，它将被丢弃。进行了数值测试，以设置滑动窗口大小，并观察所提出方法在文献中呈现的主要PQ事件情况下的行为，即使信噪比发生变化。实验结果证实了该提议的有效性，突出了与数值结果的一致性以及与基于快速傅里叶变换（FFT）的方法相比执行时间的减少。
