A Monte Carlo‐like approach to uncertainty estimation in electric power quality measurements

The assessment of the quality of the electric power supply, as well as that of the electric loads, is becoming a critical problem, especially when the liberalization of the electricity market is involved. Power quality can be evaluated by means of a number of quantities and indices whose measurement is not straightforward and is generally attained by means of digital signal processing techniques based on complex algorithms. The assessment of the uncertainty of the results of such measurements is a critical, open problem. This paper proposes a general purpose approach, based on the Monte Carlo method that, starting from the estimated contributions to the uncertainty of each device in the measurement chain, estimates the probability density distribution of the measurement result, and therefore, its standard uncertainty. This approach has been experimentally validated for the active power measurement and applied to the estimation of the uncertainty of the measurement of more complex power quality indices.