Performance and energy efficiency of the analysis of the growing synchrophasor in a highperfomance computing environment Synchronized phasor (synchrophasor) measurements for power systems are is very useful tool for engineers in analyzing dynamic events in the grid which is not possible with traditional SCADA measurements and was codified in IEEE C37.118 Synchrophasor for Power Systems and IEEE 6025511812018 Synchrophasor for power systems  Measurements standards. Online and retrospective processing of data collected from hundreds and thousands of sources of synchrophasor measurements is a complex computational task. A Python based framework for digital signal processing with its parallel implementation was developed to use it in analysis of growing synchrophasor measurements. An implementation shows satisfactory performance and parallel scaling. The energyefficient regimes of its parallel execution were found. Ways of further optimization are identified. The results of numerical experiments are presented.

Dynamic Power Systems Phasor Estimation Using Kalman Filter Algorithms. In the electrical power system, the accuracy of phasor estimation represents essential and critical issue due to the dependability of many fields on the characteristic of the estimated signals. Therefore, several algorithms have been suggested to estimate the main aspects of these signals. This paper presents a comparative evaluation of dynamic phasor estimation algorithms, namely the linear Kalman and extended Kalman filter. Many tests have been made on the dynamic filters were developed in the Simulink environment of MATLAB, The tests include amplitude step, phase step, frequency step, total vector error and computation time. Test and simulation results are provided to highlight each algorithm suitability and limitations to estimate the phasor of the power system.