Synthesize Phasor Measurement Unit Data Using Large-Scale Electric Network Models

Xu, Tong; Li, Hanyue; Birchfield, Adam B.; Overbye, Thomas J.

doi:10.1109/naps46351.2019.9000196

Cited by 2 publications

(2 citation statements)

References 16 publications

(20 reference statements)

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“…However, for the cases when it is not possible to obtain and transfer all data produced by a PMU, it is necessary to generate synthetic data to train a DL-based classification system which uses PMU data to analyse the power system. Many methods have been proposed in the literature to generate synthetic data for such circumstances [20][21][22][23][24][25][26][27][28][29][30]. In [20], a Generative Adversarial Networks (GAN)-based PMU data generation method is proposed to improve the classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

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

Gok

Salor

Taplamacıoğlu

2023

IET Generation Trans & Dist

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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 trainingset 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 limitedsize 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.

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Section: Introductionmentioning

confidence: 99%

“…In [27], another method is suggested to generate PMU data from the synthetic network by considering realistic noise and distortion effects. A solar PMU data generator is developed by using a stochastic model in [28]. Moreover, refs.…”

Section: Introductionmentioning

confidence: 99%