The variation in amplitude and phase of voltage and current signals of a power system under dynamics is often the basis for inaccurate phasor estimation. Further, the frequency estimator derived from estimated phasor is being affected under such dynamic characteristics. This paper presents a method to compute the magnitude and phase angle of the signal during the dynamics using an artificial neural network. First the modeling of signal during dynamics is presented then a multi-layered feed-forward neural network algorithm is developed to estimate the unknown parameters of the model. Subsequently, the magnitude and phase angle and frequency of the signal are calculated using the estimated parameters. The performance of the proposed method is evaluated using standard test signals as per the synchrophasor standard.

动态条件下电力系统的电压和电流信号的幅度和相位变化通常是相量估计不准确的基础。此外，从估计相量导出的频率估计器正受到这种动态特性的影响。本文提出了一种使用人工神经网络在动态过程中计算信号幅度和相位角的方法。首先介绍了动态过程中的信号建模，然后开发了一种多层前馈神经网络算法来估计模型的未知参数。随后，使用估计的参数计算信号的幅度，相位角和频率。根据同步相量标准，使用标准测试信号评估了所提出方法的性能。