Power Quality survey factor analysis using Multivariable Linear Regression (MVLR)

During the past two decades, there has been a considerable number of power quality (PQ) monitoring programs completed throughout the world. The information collected during these surveys can provide a detailed picture of the expected electrical environment help utilities to plan their future networks in relation to power quality performance. The mass of data gathered for a sample of sites of a large-scale power quality (PQ) survey of this nature has the potential to reveal good and bad influences on power quality if an appropriate procedure for analysis can be determined. If it is known which characteristics are more important in determining the levels of particular PQ disturbances, it could be expected that other sites with similar characteristics would also present similar PQ levels. This paper aims to assess the levels of PQ disturbances and factors that influence good or bad PQ levels in a large scale PQ survey. For this, the Australian National power quality benchmark survey data has been analysed and compared for the dominant factors in long term PQ data. In this analysis, multivariable linear regression (MVLR) has been identified as a useful tool for factor analysis of complex power quality data.