Wavelets detect and locate time of disturbances sucessfully, but for measurement of power/energy they also have to estimate and classify them accurately. This paper investigates the factors on choice of a certain wavelet function and qualitatively shows how the number of coefficients of the wavelets is an important number that affects output decomposition and energy distribution leakage. Wavelets provide an output in terms of the time-frequency scale. The frequency bandwidth characteristics of these individual wavelet levels provide better understanding of the wavelets. The sampling frequency and the number of data points are important parameters and must be carefully selected to avoid the frequency of interest falling into the end regions.
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Parameswariah et al. (2002) studied this question.
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