Identification of flicker sources is necessary to find who is responsible for the measured flicker and improve power quality. This paper puts forward a new method for identifying flicker sources with minimum measurement units. Contrary to the previous works where flicker sources are considered a single-frequency signal, the autoregressive moving average (ARMA) is used to model active and reactive power variations. First, the envelope of the network voltage at the considered busbars is derived by the Hilbert transform. Then, appropriate flicker indices are extracted from the power spectral density (PSD) of the voltage envelope. A novel two-level structure of a set of ANNs is proposed, which needs a low number of voltage measurement units to locate the flicker sources. Using the captured data from different simulations of various scenarios, the Artificial Neural Networks (ANNs) are trained to categorize flicker sources.

必须确定闪烁源，以找出谁负责测量的闪烁并改善电源质量。提出了一种以最小测量单位识别闪烁源的新方法。与之前将闪烁源视为单频信号的工作相反，自回归移动平均值（ARMA）用于建模有功和无功功率变化。首先，通过希尔伯特（Hilbert）变换得出所考虑的母线处的网络电压的包络线。然后，从电压包络的功率谱密度（PSD）中提取适当的闪烁指数。提出了一种新型的人工神经网络的两级结构，该结构需要少量的电压测量单元来定位闪烁源。使用从各种场景的不同模拟中捕获的数据，