Power System Event Classification Based on Machine Learning

Okumuş, Halil İbrahim; Nuroğlu, Fatih Mehmet

doi:10.1109/ubmk.2018.8566324

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…A major hurdle in applying deep learning models to power system problems is usually the lack of sufficient and high-quality datasets for training, as it is well-known that more eventful data usually lead to better classification performance [21,44,77]. The accessibility of real-world power grid PMU measurement data is limited due to the regulation CEII [19] for national security and sensitivity concerns.…”

Section: Synthetic Time-series Generationmentioning

confidence: 99%

A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy Grids

Zheng¹,

Xu²,

Trinh³

et al. 2021

Preprint

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The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change. With deepening penetration of renewable energy resources and electrified transportation, the reliable and secure operation of the electric grid becomes increasingly challenging. In this paper, we present PSML, a first-of-its-kind open-access multi-scale time-series dataset, to aid in the development of data-driven machine learning (ML) based approaches towards reliable operation of future electric grids. The dataset is generated through a novel transmission + distribution (T+D) co-simulation designed to capture the increasingly important interactions and uncertainties of the grid dynamics, containing electric load, renewable generation, weather, voltage and current measurements at multiple spatio-temporal scales. Using PSML, we provide state-of-the-art ML baselines on three challenging use cases of critical importance to achieve: (i) early detection, accurate classification and localization of dynamic disturbance events; (ii) robust hierarchical forecasting of load and renewable energy with the presence of uncertainties and extreme events; and (iii) realistic synthetic generation of physical-law-constrained measurement time series. We envision that this dataset will enable advances for ML in dynamic systems, while simultaneously allowing ML researchers to contribute towards carbon-neutral electricity and mobility.

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Section: Synthetic Time-series Generationmentioning

confidence: 99%

A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy Grids

Zheng¹,

Xu²,

Trinh³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In this case, data-driven approaches seem to be more suitable, since they are directly based on the measurement data reflecting the actual status of electrical quantities in power grids. Machine learning-based event classification has been proposed and developed in the past two decades exhibiting promising results [2][5] [6] [7]. Even regardless of data quality issues, a major hurdle to apply these machine-learning based classifiers is usually a lack of a sufficient data set for training.…”

Section: Introductionmentioning

confidence: 99%

“…It is well-known that more eventful data usually lead to a better classification accuracy [5] [6]. However, it is extremely difficult, if not infeasible, to acquire enough eventful data in practice [5].…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Networks-Based Synthetic PMU Data Creation for Improved Event Classification

Zheng

Wang

Kalathil

et al. 2021

IEEE Open J. Power Energy

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A two-stage machine learning-based approach for creating synthetic phasor measurement unit (PMU) data is proposed in this paper. This approach leverages generative adversarial networks (GAN) in data generation and incorporates neural ordinary differential equation (Neural ODE) to guarantee underlying physical meaning. We utilize this approach to synthetically create massive eventful PMU data, which would otherwise be difficult to obtain from the real world due to the critical energy infrastructure information (CEII) protection. To illustrate the utility of such synthetic data for subsequent datadriven methods, we specifically demonstrate the application of using synthetic PMU data for event classification by scaling up the real data set. The addition of the synthetic PMU data to a small set of real PMU data is shown to have improved the event classification accuracy by 2 to 5 percent.

show abstract

A multi-scale time-series dataset with benchmark for machine learning in decarbonized energy grids

Zheng

Trinh

et al. 2022

Sci Data

View full text Add to dashboard Cite

The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change. With deepening penetration of renewable resources, the reliable operation of the electric grid becomes increasingly challenging. In this paper, we present PSML, a first-of-its-kind open-access multi-scale time-series dataset, to aid in the development of data-driven machine learning (ML)-based approaches towards reliable operation of future electric grids. The dataset is synthesized from a joint transmission and distribution electric grid to capture the increasingly important interactions and uncertainties of the grid dynamics, containing power, voltage and current measurements over multiple spatio-temporal scales. Using PSML, we provide state-of-the-art ML benchmarks on three challenging use cases of critical importance to achieve: (i) early detection, accurate classification and localization of dynamic disturbances; (ii) robust hierarchical forecasting of load and renewable energy; and (iii) realistic synthetic generation of physical-law-constrained measurements. We envision that this dataset will provide use-inspired ML research in safety-critical systems, while simultaneously enabling ML researchers to contribute towards decarbonization of energy sectors.

show abstract

Power System Event Classification Based on Machine Learning

Cited by 4 publications

References 11 publications

A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy Grids

A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy Grids

Generative Adversarial Networks-Based Synthetic PMU Data Creation for Improved Event Classification

A multi-scale time-series dataset with benchmark for machine learning in decarbonized energy grids

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