Automatic processing of dynamic responses via wavelet transform for the development of dynamic equivalent models

The ever-increasing requirements for electricity, the emergence of microgrids and the escalating penetration of distributed generators has reinvigorated the interest in dynamic equivalencing of distribution networks, due to its significance in power system analysis. Scope of this paper is to propose an efficient method for event detection and filtering of dynamic responses based on the wavelet transform (WT), in order to improve the quality of signals used for the derivation of dynamic equivalent models. The accuracy of the proposed method is tested using artificially created noisy responses, by applying Gaussian, Laplace, and Student’s-t noise distributions. Comparisons with other filtering techniques are also performed and the impact of all methods on the derivation of accurate equivalent model parameters is quantified and analyzed. The performance of the proposed method is also evaluated by using RMS responses obtained from a large-scale distribution network model as well as by analysing laboratory measurements; results verify the efficiency and applicability of the WT-based processing procedure, by achieving a parameter estimation error well below 1%. It is noteworthy that the average computational burden throughout the process remains under 0.39 s.