Ranjan K.G.Prusty B.R.Jena D.2021-05-052021-05-052021Electric Power Systems Research , Vol. 191 , , p. -https://doi.org/10.1016/j.epsr.2020.106885https://idr.nitk.ac.in/handle/123456789/14652Outlier detection and correction of time-series referred to as preprocessing, play a vital role in forecasting in power systems. Rigorous research on this topic has been made in the past few decades and is still ongoing. In this paper, a detailed survey of different preprocessing methods is made, and the existing preprocessing methods are categorized. Also, the preprocessing capability of each method is highlighted. The well-established methods of each category applicable to univariate data are critically analyzed and compared based on their preprocessing ability. The result analysis includes applying the well-established methods to volatile time-series frequently used in power system applications. PV generation, load power, and ambient temperature time-series (clean and raw) of different time-step collected from various places/weather zones are considered for index-based and graphical-based comparison among the well-established methods. The impact of change in the crucial parameter(s) values and time-resolution of the data on the methods’ performance is also elucidated in this paper. The pros and cons of methods are discussed along with the scope for improvisation. © 2020Review