Performance Comparison of Clipping Detection Techniques in AC Power Time Series

Kirsten Perry, Matthew Muller, Kevin Anderson

Research output: Contribution to conferencePaperpeer-review

3 Scopus Citations


In this research, a variety of methods were developed to detect clipping periods in AC power time series. AC power data streams associated with 36 unique systems across the USA were collected, and data points representing clipping periods were manually labeled by experts. Using this data set for training and validation, novel logic-based and machine learning (ML) approaches were developed to classify time series values as clipping or non-clipping. These approaches were compared to the RdTools method for detecting clipping periods. The logic-based and ML XGBoost approaches achieved F-scores of 85.0 and 77.6, respectively, when cross-validated against the manually labeled data, as compared to the current RdTools approach (F-score of 56.4), indicating a significant improvement at detecting clipping periods. Additionally, the effects of each clipping filter when evaluating system degradation rates were assessed, using 31 unique systems across the USA. Results indicate that estimated system degradation rate can vary based on the type of clipping filter used, by up to 0.6% degradation rate for some cases.

Original languageAmerican English
Number of pages6
StatePublished - 20 Jun 2021
Event48th IEEE Photovoltaic Specialists Conference, PVSC 2021 - Fort Lauderdale, United States
Duration: 20 Jun 202125 Jun 2021


Conference48th IEEE Photovoltaic Specialists Conference, PVSC 2021
Country/TerritoryUnited States
CityFort Lauderdale

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

NREL Publication Number

  • NREL/CP-5K00-80164


  • clipping
  • machine learning
  • modeling
  • photovoltaic
  • rdtools
  • solar


Dive into the research topics of 'Performance Comparison of Clipping Detection Techniques in AC Power Time Series'. Together they form a unique fingerprint.

Cite this