Synchrophasor Estimation Based on Quadrature Amplitude Modulation Using Artificial Ecosystem Optimization
Abstract
The necessity of controlling and observing the grid's power quality and detecting possible faults has arisen due to the generation diversity and increasing loads which causes demand and supply gaps. Synchrophasor measurements are performed to detect these problems and monitor the system's stability. For this purpose, the Quadrature Amplitude Modulation (QAM) method is proposed for synchrophasor measurement in this study. First, the frequency value of the power signal is estimated using the Artificial Ecosystem algorithm (AEO). This estimated value is assigned as the reference value for the QAM method, and the power signal is decomposed into negative and positive parts at this frequency value. These components are then filtered using a moving average filter, which is a low-pass filter that eliminates high-frequency components to obtain components with the frequency estimated by the AEO. Consequently, the necessary frequency component's amplitude and phase information are acquired. The effectiveness of this suggested approach is examined for the IEEE Std. C37.118.1 standard's M and P classes. According to the obtained results, the proposed method performs phasor estimation below the error levels specified in IEEE Std. C37.118.1.
Keywords
Quadrature amplitude modulation, Phaso, Rocof, Synchrophasor, Total vector error, optimization
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