Synchrophasor Estimation Based on Quadrature Amplitude Modulation Using Artificial Ecosystem Optimization Algorithm

The need to monitor power quality and detect faults in the power grid has increased due to diverse generation sources and growing loads, which create supply-demand imbalances. Synchrophasor measurements are used to address these issues and assess system stability. This study proposes using Quadrature Amplitude Modulation (QAM) for synchrophasor measurement. Initially, the power signal’s frequency is estimated using the Artificial Ecosystem Optimization (AEO) algorithm. This estimated frequency serves as a reference for the QAM method. The power signal is then decomposed into positive and negative components based on this frequency. A moving average filter, acting as a low-pass filter, is applied to remove high-frequency noise and retain components aligned with the estimated frequency. As a result, the amplitude and phase of the desired frequency component are extracted. The method’s effectiveness is evaluated based on the IEEE Std. C37.118.1 for both M and P classes. Results show that the proposed approach achieves phasor estimation errors within the acceptable limits defined by the standard. Moreover, its performance at 80 dB SNR is compared with recent methods from literature, demonstrating its superiority in synchrophasor estimation, particularly under steady- state and harmonic conditions.