Transient event classification using pmu data with deep learning techniques and synthetically supported training-set


IET Generation, Transmission and Distribution, vol.17, no.6, pp.1287-1297, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 17 Issue: 6
  • Publication Date: 2023
  • Doi Number: 10.1049/gtd2.12734
  • Journal Name: IET Generation, Transmission and Distribution
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, Environment Index, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Page Numbers: pp.1287-1297
  • Keywords: deep learning, Gramian angular field, power quality, power system event classification, power system events, synthetic data generation
  • Gazi University Affiliated: Yes


This paper presents a research work which focuses on generating synthetic data to enrich the training-set of a deep learning (DL) based classification system to classify power system transient events using PMU frequency measurements. The synthetically improved training-set is shown to increase the classification performance compared to the case when only the actual-data training-set is used. The proposed classification system helps to reveal high-frequency transient variation information out of PMU measurements collected at a relatively much lower rate, especially when a small set of training-data exists. Synthetic PMU frequency data is generated based on the DFT analysis statistics on the limited-size PMU frequency data. Generation of the synthetic data is achieved by re-synthesis of the PMU frequency data using inverse DFT, which imitates the DFT frequency and phase behaviour for each event type separately. Then the DL structure is trained to classify the power system transients using the synthetically enriched train set. The proposed method of generating synthetically supported training-set has lower computational complexity compared to the existing methods in the literature and helps to obtain improved classification results. It can be used to increase the classification performances of power quality devices performing transient event analysis, especially for those with access to a limited set of training-set.