Smart Grid

Accurately detecting power line defects under diverse weather conditions is crucial for ensuring power grid reliability and safety. Existing power line inspection datasets, while valuable, often lack the diversity needed for training robust machine learning models, particularly for adverse weather scenarios like fog, rain, and nighttime conditions. This paper addresses this limitation by introducing a novel framework for generating synthetic power line images under diverse weather conditions, thereby enhancing the diversity and robustness of power line inspection systems.
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The dataset originates from a wind-solar hybrid power generation system located in a specific region of Northern China. It includes two electricity-related variables: wind power and photovoltaic (PV) power, with a temporal resolution of 1 hour. Additionally, the dataset provides representative wind and PV power generation curves for typical days across all four seasons: spring, summer, autumn, and winter.
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Smart grid, an application of Internet of Things (IoT) is modern power grid that encompasses power and communication network from generation to utilization. Home Area Network (HAN), Field or Neighborhood Area Network (FAN/NAN) and Wide Area network (NAN) using Wireless LAN and Wireless/Wired WAN protocols are employed from generation to utilization . Advanced Metering Infrastructure, a utilization side infrastructure facilitates communication between smart meters and the server where energy efficient protocols are mandate to support smart grid.
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Smart grid, an application of Internet of Things (IoT) is modern power grid that encompasses power and communication network from generation to utilization. Home Area Network (HAN), Field or Neighborhood Area Network (FAN/NAN) and Wide Area network (NAN) using Wireless LAN and Wireless/Wired WAN protocols are employed from generation to utilization . Advanced Metering Infrastructure, a utilization side infrastructure facilitates communication between smart meters and the server where energy efficient protocols are mandate to support smart grid .
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Electric Vehicles Charging Station with Photovoltaic Panels
This dataset contains the model and simulation output results in Matlab/Simulink of a three-phase grid-connected charging station with PV panels for electric vehicles realized in a work submitted to the 13th IEEE International Conference and Exposition on Electrical and Power Engineering EPEi 2024, Iasi, Romania, October 17-19, 2024 (https://www.epe.tuiasi.ro/).
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This dataset contains the Matlab code of the nonlinear state-space model of a power electronics-dominated grid. A power grid with 3 grid following converters is taken under consideration, following the publication:
F. Cecati, R. Zhu, M. Liserre and X. Wang, "Nonlinear Modular State-Space Modeling of Power-Electronics-Based Power Systems," in IEEE Transactions on Power Electronics, vol. 37, no. 5, pp. 6102-6115, May 2022, doi: 10.1109/TPEL.2021.3127746.
Abstract of the paper:
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This study is utilized for submodule open-circuit fault detection uncertainty analysis of modular multilevel converters. The dataset consists of 8 uncertainty factors and 15 system variables under four operation scenarios. The 1000 sets of uncertainty factor samples are generated randomly as initial configuration of the system. The 15 system variables are obtained by 1000 Monte Carlo simulations. We found that there are 153 residual samples exceeded the threshold of 0.8, which indicated a high false alarm rate.
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Anomaly detection in Phasor Measurement Unit (PMU) data requires high-quality, realistic labeled datasets for algorithm training and validation. Obtaining real field labelled data is challenging due to privacy, security concerns, and the rarity of certain anomalies, making a robust testbed indispensable. This paper presents the development and implementation of a Hardware-in-the-Loop (HIL) Synchrophasor Testbed designed for realistic data generation for testing and validating PMU anomaly detection algorithms.
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<p>The dataset forwas collected by UAVs equipped with camera heads to capture images of insulators on power transmission lines. These images have a resolution of 3872×2592 pixels. A total of 488 insulator defect images were selected, and the data was annotated using the LabelMe annotation software. This study's dataset annotated four types of labels: insulator, damaged, Flashover, and hammer. The insulator is a positive class label, and damaged, Flashover, and hammer are negative class labels.
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