Post-measurement processing of electric power quality data

This paper addresses the issues of post-measurement processing of data collected in a power quality assessment study. Three broad types of post-measurement processing objectives are considered: to enhance accuracy; to estimate data; and to reduce the volume of the collected data. The methods used to enhance accuracy are bad data identification and rejection. Averaging is discussed as a method to "filter" measurement error. The methods used for data estimation are state estimation techniques in both the time and frequency domain. The methods used to reduce the volume of the collected data are based on the calculation of marginal and conditional probabilities and expectations. The integrated use of Bese techniques in an instrumentation system for power quality assessment is discussed. The main suggested application is for the measurement of harmonics.