Electric Utility

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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The dataset includes the regulatory, utility and DGENCO data for distribution planning for a utility under regulated monopoly. The regulatory data includes benchmark EENSC values, and elasticity values. The utility data involves bus, and prospective branch data. The load data corresponds to increase in load with respect to planning stages. The branch data corresponds to all candidate branch locations of all types. The DGENCO data corresponds the candidate DG locations with maximum size of each Distributed Generation.
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This dataset is shared for capacitor C and ESR estimation using convolution neural network. The dataset is collected in a experimental modular moultilevel converter, which includes the capacitor voltage at low and medium frequency band, and the arm current. Wavelet transform is used to transfer the time series data to images, which present the inherent data features to image patterns. In a degraded capacitor, the C decreases and the ESR increases, which result in different image patterns.
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This repository contains datasets and code with a novel numerical approach aimed at finding a distribution network expansion plan (DNEP) that prevents future congestion and voltage issues. This approach is tested using the modified IEEE 33-bus network. Electricity demand and PV production data for a leap year with a 1-minute resolution was generated using the CREST model from the Loughborough University and is provided as a dataset of future high-load and high-production scenario.
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This database contains Synthetic High-Voltage Power Line Insulator Images.
There are two sets of images: one for image segmentation and another for image classification.
The first set contains images with different types of materials and landscapes, including the following landscape types: Mountains, Forest, Desert, City, Stream, Plantation. Each of the above-mentioned landscape types consists of 2,627 images per insulator type, which can be Ceramic, Polymeric or made of Glass, with a total of 47,286 distinct images.
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The unstable working points of the induction machine in a sensorless controller are commonly known problems. This paper proposes a new stabilization method for the model-based speed observer of an induction machine. Considering the proposed solution, the rotor speed can be reconstructed based on the mathematical model of a machine by using both adaptive and non-adaptive schemes. The presented method is generalized to the classical nonlinear system in the vector form and can be applied to induction machines. The Lyapunov theorem is used during the design procedure for stability purposes.
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The dataset encompasses a diverse array of electrical signals representing Power Quality Disturbances (PQD), both in single and combined forms, meticulously generated in adherence to the IEEE 1159 guideline. Crucially, the dataset includes both raw data and corresponding labels, facilitating supervised learning tasks and enabling the development and evaluation of classification algorithms.
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