Missing Data Analysis and Imputation Method for Medium Voltage Distribution Network Feeders
Name: JOÃO MARCUS RAMOS BACALHAU
Publication date: 05/11/2020
Advisor:
Name |
Role |
|---|---|
| JUSSARA FARIAS FARDIN | Advisor * |
Examining board:
| Name |
Role |
|---|---|
| DANIEL CRUZ CAVALIÉRI | External Examiner * |
| JUSSARA FARIAS FARDIN | Advisor * |
| LUCAS FRIZERA ENCARNAÇÃO | Internal Examiner * |
Summary: The energy sectors investment aims to ensure a continuous, reliable, and quality supply of electrical energy imposed by the electricity regulatory agency with maximum economic-financial balance. This dissertation discusses the challenges of processing data from medium voltage distribution feeders to use on the distribution network planning. The analysis of missing data and outliers is made on the three-phase voltage, current, and power factor of 459 time series of real feeders. Furthermore, it is proposed a method of preprocessing, and missing data imputation using the unbalanced characteristic between
phases, interpolation, and the normalized scaled standard weekday curve. The results show that most missing data are three-phase, however, with a significant amount of single and dual-phase loss that can be filled by the proportion between phases. Hence, the challenge is to fill multiple weeks of missing three-phase data, and for that, the use of the standard curve for each day of the week is proposed. The method proposed is a promising alternative for data imputation in medium-voltage feeders. The technique is tested using
real feeder data degraded by its missing data probability function, and compared with the Na ̈ıve approach.
Keywords: Network expansion; Distribution system planning; Data imputation; Feeder; Data analysis; Missing value; Incomplete data; Imputation; Time series data.
