PQDM
Power Quality Data Mining
R
Power Quality
Data Mining
Measurement
R package for the analysis of long-term power quality measurements
Description: PQDM is an R package for the analysis of long-term power quality measurements. It implements published methods for the evaluation of weekly 95th percentiles derived from 10-minute values: validating the data and its availability, filling gaps, and quantifying the quality reserve with respect to limits, seasonal variations and trends. Each function refers to the publication that describes its method.
Status: under development (version 0.5.1)
License: GPL (≥ 3)
Methods
| Function | Purpose | Publications |
|---|---|---|
pq_validate() |
Check weekly 95th percentiles and their data availability | Domagk 2022, Domagk 2023 |
pq_fill() |
Fill gaps within time series by linear interpolation | Domagk 2022 |
pq_quality_reserve() |
Quality reserve with respect to user-supplied limits | Gasch 2016, Abdelrahman 2016 |
pq_season() |
Identification and quantification of seasonal variations | Domagk 2015 |
pq_trend() |
Trend components and recent trend gains | Domagk 2022 |
Installation
# install.packages("remotes")
remotes::install_github("mdomagk/PQDM", build_vignettes = TRUE)Usage
library(PQDM)
pq_weekly_example |>
pq_validate() |> # data availability per time series
pq_fill() |> # fill gaps by linear interpolation
pq_season() # seasonal variationsThe vignette vignette("PQDM") explains the data format and the workflow.
Citation
If you use PQDM, please cite the publication of the method you applied (see above). The package itself can be cited via citation("PQDM") in R.