A synchrophasor system emulator - software approach and real-time simulations

Zheng, Gang; Howell, F.; Wang, Lei

doi:10.1109/pesgm.2015.7286305

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…This time series dataset includes data on various electrical grid scenarios, including no fault and short circuit occurrences. Several fault case scenarios have been simulated using ePMU DSA tools [44] and Matlab Simulink [30,39] to produce this dataset. To accumulate data, the Phasor Measurement Unit (PMU) has been positioned on the transmission line simulation model.…”

Section: Datasetmentioning

confidence: 99%

Ensemble learning based transmission line fault classification using phasor measurement unit (PMU) data with explainable AI (XAI)

Bin Akter,

Sarkar Pias,

Rahman Deeba

et al. 2024

PLoS ONE

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A large volume of data is being captured through the Phasor Measurement Unit (PMU), which opens new opportunities and challenges to the study of transmission line faults. To be specific, the Phasor Measurement Unit (PMU) data represents many different states of the power networks. The states of the PMU device help to identify different types of transmission line faults. For a precise understanding of transmission line faults, only the parameters that contain voltage and current magnitude estimations are not sufficient. This requirement has been addressed by generating data with more parameters such as frequencies and phase angles utilizing the Phasor Measurement Unit (PMU) for data acquisition. The data has been generated through the simulation of a transmission line model on ePMU DSA tools and Matlab Simulink. Different machine learning models have been trained with the generated synthetic data to classify transmission line fault cases. The individual models including Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (K-NN) have outperformed other models in fault classification which have acquired a cross-validation accuracy of 99.84%, 99.83%, and 99.76% respectively across 10 folds. Soft voting has been used to combine the performance of these best-performing models. Accordingly, the constructed ensemble model has acquired a cross-validation accuracy of 99.88% across 10 folds. The performance of the combined models in the ensemble learning process has been analyzed through explainable AI (XAI) which increases the interpretability of the input parameters in terms of making predictions. Consequently, the developed model has been evaluated with several performance matrices, such as precision, recall, and f1 score, and also tested on the IEEE 14 bus system. To sum up, this article has demonstrated the classification of six scenarios including no fault and fault cases from transmission lines with a significant number of training parameters and also interpreted the effect of each parameter to make predictions of different fault cases with great success.

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Section: Datasetmentioning

confidence: 99%

Ensemble learning based transmission line fault classification using phasor measurement unit (PMU) data with explainable AI (XAI)

Bin Akter,

Sarkar Pias,

Rahman Deeba

et al. 2024

PLoS ONE

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A Review on Providing Realistic Electric Grid Simulations for Academia and Industry

Birchfield

Overbye

2023

Curr Sustainable Renewable Energy Rep

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Purpose of Review Engineering analysis and design for large-scale electric power grids require advanced modeling and simulation capabilities for a variety of studies, with two of the key study types being steady-state power flow and time-domain stability. In order to promote innovation in this area, during a time of rapid change, much recent work has been done on enhancing the availability of grid models and simulation datasets for the benefit of both academia and industry. The purpose of this paper is to review these new developments. Recent Findings Over the last several years, there have been many different developments in electric grid power flow and stability analysis. In power flow, key new changes include (1) the inclusion of geographic coordinates, (2) the addition of geomagnetic disturbance analysis, (3) the direct inclusion of weather data, and (4) new optimal power flow (OPF) and security-constrained OPF algorithms, some of which utilize machine learning. Key developments in stability are (1) many new models particularly for inverter-based resources, (2) wider availability of interactive stability simulations, and (3) greater use of wide-area visualization in both power flow and stability. Summary The paper shows the range of software platforms available for large-scale electric grid for power flow and stability simulations, along with associated data formats. It also considers modeling enhancements, including the ability to capture more detailed dynamics and coupling to inter-related infrastructure. The paper also summarizes the availability of test case datasets, both real and synthetic.

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A universal synchrophasor based test platform

Liang

2017

2017 IEEE Electrical Power and Energy Conference (EPEC)

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A synchrophasor system emulator - software approach and real-time simulations

Cited by 4 publications

References 7 publications

Ensemble learning based transmission line fault classification using phasor measurement unit (PMU) data with explainable AI (XAI)

Ensemble learning based transmission line fault classification using phasor measurement unit (PMU) data with explainable AI (XAI)

A Review on Providing Realistic Electric Grid Simulations for Academia and Industry

A universal synchrophasor based test platform

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