The dataset is designed for training, fine-tuning, and benchmarking machine learning models by providing synchronized point-on-wave voltage and current measurements across a diverse set of topologies and voltage levels.
Abstract
The integration of inverter-based renewable energy sources into electric grids challenges conventional power system protection. Machine learning-based solutions can address these challenges by utilizing available data in modern smart grids. However, the lack of open datasets prevents reproducibility and fair comparisons between different approaches and their results, which hinders further progress. Therefore, this paper presents EvEMTBench, a synthetic dataset of faults and events generated using electromagnetic transient simulations. The physical plausibility of the power system simulation is ensured by validating the simulation parameters against established literature and providing comprehensive documentation of the simulation procedure. The dataset is designed for training, fine-tuning, and benchmarking machine learning models by providing synchronized point-on-wave voltage and current measurements at 9600 Hz across a diverse set of topologies and voltage levels. The inclusion of a wide range of fault and operating events allows the utilization of EvEMTBench for different tasks like incipient fault detection, fault localization, or event detection.
A robust Machine Learning (ML)-based framework for accurately locating electrical faults in wind farm collector networks and achieves the highest accuracy, with prediction errors not exceeding 2%.
Miguel R. Fonseca, M. Davi, M. Oleskovicz· IEEE Access· 0 citations
The findings of this research underscore the potential of data-driven techniques in improving the robustness and flexibility of AC protection systems in contemporary microgrids.
Abdul Basit Taj, Sarosh Ali Shah Syed, Adnan Umar Khan et al.· International Journal of Ene...· 0 citations
A standardization-oriented framework that turns evaluation assumptions into explicit, reproducible evidence and provides a basis for more comparable, auditable evaluation and future certification-oriented assessment of machine-learning protection functions is proposed.
Julian Oelhaf, Georg Kordowich, Paula Andrea Pérez-Toro et al.· International Journal of Ele...· 0 citations
With the large-scale integration of new energy into power systems, the intermittency and volatility caused by the high penetration of renewable energy generation (such as wind power and photovoltaic power) require diagnostic systems to possess strong uncertainty-handling capabilities. Consequently, the complexity and uncertainty of power grid operation have increased significantly, posing unprecedented challenges to the safe, stable and economic operation of the power system. The traditional fault diagnosis and handling methods, which are based on fixed models and manual experience, can no longer adapt to the dynamic and complex operating characteristics of the new energy power grid, making it urgent to explore intelligent technical solutions. Traditional power grid fault diagnosis approaches rely mainly on expert experience and physical models. Model-based methods locate faults through state estimation and power flow calculation, whose accuracy heavily depends on model precision and parameter identification. However, under complex operating conditions such as high new energy penetration, grid topology changes, and frequent fluctuations in power supply and demand, establishing an accurate mathematical model that can cover all operating scenarios is extremely challenging—model mismatches often occur, leading to reduced fault diagnosis accuracy. Expert systems, on the other hand, integrate the operational experience of power grid engineers into rule bases, offering transparent reasoning processes that are easy to understand and verify. Yet, they suffer from inherent limitations: knowledge acquisition is time-consuming and labor-intensive, it is difficult to update rules in a timely manner with the iteration of grid technology, and they lack self-learning ability, making it impossible to adapt to new fault types and complex operating environments brought by new energy integration. When dealing with massive real-time data generated by the power grid (including new energy output data, load data, equipment monitoring data, and environmental data) and complex system environments, the following prominent problems frequently arise, which further restrict the efficiency and reliability of power grid operation and fault handling.
Rui-Ze Ji· Highlights in Science Engine...· 1 citation
A new stacking-ensemble hybrid machine learning model that will combine a one-dimensional convolutional neural network with a bidirectional long short-term memory (CNN-BiLSTM) module, a Random Forest classifier, and an XGBoost gradient booster as base learners under the guidance of a logistic regression meta-learner is suggested.
A. Gopalakrushna· Materials Research Proceedin...· 0 citations
The reliable operation of smart distribution grids depends heavily on the timely detection and accurate classification of faults. Traditional fault detection methods, such as impedance and threshold-based analysis, provide essential diagnostic insights but are limited under noisy and dynamic operating conditions. This study proposes a hybrid framework combining MATLAB/Simulink-based modeling with Artificial Intelligence (AI) to enhance fault detection and classification in smart distribution grids. Fault scenarios including single line-to-ground (SL_G), line-to-line (L_L), double line-to-ground (LL_G), three-phase (LLL), and no-fault conditions were simulated to generate a labeled dataset of 9201 samples. The dataset was then used to train two AI models: a Multilayer Perceptron (MLP) and an Extreme Gradient Boosting (XGBoost) classifier. The MLP achieved the highest classification accuracy of 96.3%, while XGBoost reached 95.5% with reduced training complexity and faster execution. Comparative analysis demonstrated that AI-based approaches significantly outperform traditional methods in accuracy, adaptability, and computational efficiency. These results highlight the potential of integrating AI into modern power system protection schemes to achieve rapid, accurate, and cost-effective fault detection.
Nizar Jabar Faqishafyee, Halbast Rashid Ismael, A. Hasan· Academic Journal of Internat...· 0 citations
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