This report provides supplementary material and additional information to complement the data in the paper “Nonlinear Control to Improve Grid Integration of Grid-Forming Converters”.
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This dataset contains three regional subsets comprising the electricity load and corresponding weather conditions collected from Australia, Panama, and Austria. Each subset includes time-synchronized records of power consumption along with key meteorological variables such as temperature, humidity, wind speed, and other locally available weather indicators. The data were gathered from regional grid operators and national meteorological sources to reflect the actual operating and environmental conditions of each region.
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The dataset contains voltage and current waveforms measured at the input terminals of a photovoltaic (PV) inverter during controlled arc-fault experiments.
The arcs were generated using a PV arc-fault generator powered by a solar battery through the inverter, and the corresponding signals were recorded using Tektronix DPO4034B oscilloscopes.
The measurements were performed under laboratory conditions to simulate different arc behaviors and to support the development of machine-learning and signal-processing techniques for arc-fault detection in photovoltaic systems.
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Forced oscillation events are rare, and detailed system recordings are not publicly accessible. They are mostly anonymously shared with the community, which makes it challenging to study and analyze the different scenarios of forced oscillation events. In this dataset library, an extensive set of time-stamped forced oscillation datasets called ”Dataset for Power System Forced Oscillation Responses are created by using a realistic power grid model to leverage data availability with a wide range of forced oscillation frequencies and sources.
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This dataset contains runtime and energy consumption measurements for multiple lightweight large language models (LLMs) under varying inference configurations. Experiments were conducted on a CPU–GPU hybrid system equipped with an NVIDIA GeForce RTX 3060 (8 GB VRAM) GPU and an Intel i7 CPU. The evaluated models include GPT-2, Falcon-rw-1B, LLaMA-3.2-1B, and DeepSeek-R1-Distill-Qwen-1.5B, along with their quantized variants.
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Thermoelectric generators (TEGs), based on the Seebeck effect, offer a promising solution for energy harvesting in environments with thermal gradients. Despite their structural simplicity and modular scalability, TEGs face limitations in conversion efficiency, which are compounded under dynamic conditions.
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It includes the discharge waveform characterizing the RVF gliding arc, the 30-minute discharge waveform used for energy consumption calculation, and Excel data showing the changes in discharge voltage according to the oxygen-to-nitrogen ratio.
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The dataset provides 14 key attributes: Year, Month, Day, Solar Irradiance (max), Average Air Temperature, Maximum Air Temperature, Minimum Air Temperature, Relative Humidity, Average Wind Speed, Maximum Wind Speed, Precipitation, Wind Direction, Sunshine Duration, and Total Energy Production. These parameters enable comprehensive analysis of climatic impacts on solar power generation in a semi-arid region with high solar potential.
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This data is related to some data for a digital-twin-based optimization framework for the design of single-to-multiple wireless power transfer (WPT) systems incorporating heterogeneous circuit topologies—such as mixed series and parallel configurations—in the receiver modules. The proposed method integrates a high-fidelity analysis module, which combines electromagnetic field simulation with equivalent circuit modeling, and an in-house gradient-based optimization module.
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