Wavelet-based Automatic Processing of Dynamic Responses for the Development of Dynamic Load Models

The ever-increasing requirements for electricity, the emergence of microgrids and the escalating penetration of distributed generators has reinvigorated the interest in load modelling, due to its significance in power system analysis. Scope of this paper is to propose an efficient method for event detection and noise reduction of dynamic responses based on Wavelet Transform, aiming to improve this way the quality of data used for the derivation of load models. The accuracy of the proposed method is assessed using artificially created noisy responses. In the presented analysis, three different types of noise, namely Gaussian, Laplace, and Student’s t noise, are considered. Comparisons with other filtering techniques are conducted. The impact of all examined methods on the derivation of accurate load model parameters is quantified and analyzed.