On-line parameter identification of power plant characteristics based on phasor measurement unit recorded data using differential evolution and bat inspired algorithm

Parameter estimation and dynamic modelling of power systems and their components are basis of design, planning and stability or security assessment in power systems. This study considers the estimation of power system model parameters by a global identification framework based on the maximum-likelihood principle. The proposed framework is formulated as a non-linear optimisation problem, which is solved by a hybrid method based on the bat-inspired algorithm and differential evolution method. The combination of these algorithms makes the hybrid method faster and it obtains closer to the global minimum than a pure global method. Since noise and model uncertainties are inherent parts of system identification, the effect of these factors on the performance of the proposed identification framework are studied. Results based on synthetic data in frequency domain show that the estimated parameters are close to the correct values even in the presence of significant measurement noise and considerable uncertainties.