Abstract
Phasor Measurement Units (PMU) are very costly according to energy regulator and utility companies. Utility operators work on alternative solutions to reduce the error rate and operation costs of PMU. In this paper, we sought to optimize the PMU to reduce the level of error using Second-Order Kalman Filter (SOKF). Consequently, this optimization is based on minimizing the number of errors when receiving the signal from access points or from the main access point. We derived a simple mathematical model to estimate the phase coming from the PMU.PMUs provide Global Positioning System (GPS) time stamped synchronized measurements of voltage and currents with the phase angle of the system at certain points along the grid system. Those synchronized data measurements were extracted in form of amplitude and phase from various locations of the power grid to monitor and control the power system conditions. A PMU device is a crucial part of the power equipment in terms of the cost and operative point of view. However, such ongoing development and improvement to PMUs' principal work is essential to the network operators to enhance the grid quality and the operating expenses. A MATLAB model was created to implement the proposed method in the presence of Gaussian and non-Gaussian noise. It is based on an Asyncrhophasor technique resulting in a phase error minimization when receiving the signal from access point or from the main access point. The results show the proposed SOKF method outperforming the existing model as tested using Mean Square Error (MSE). The SOKF method was replaced with a synchronization unit into the PMU structure to clarify the significance of the proposed new PMU. This paper's proposed method leads to lower costs and less complex techniques to optimize the performance of PMU using SOKF