Power and Energy

This data set gives information on the TGA and FTIR analysis of neat nano mica fillers, before and after surface salination with HMDS. This is used to gain information regarding the successful surface hydrophobic salination of nano mica platelets. The reduction in hydroxyl group density as visualized in FTIR gives insight on the stripping of polar hydrophilic hydroxyl groups. Similarly, the use of TGA analysis helps in further ascertaining the successful surface salination treatments. This can be obtained by observing the change in residual mass.

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The dataset includes active power measurements for a residential student building located in Bucharest, Romania, collected at 1 frame/second (or 0.5 frames/s) reporting rate over 12 consecutive months.

Always-on appliances include the refrigerator and the wireless router. Several other appliances are installed in the residential unit: washing machine, lighting fixtures, electrical iron, vacuum cleaner, various ICT charging devices, and air conditioning (seldom used).

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404 Views

The dataset includes active power measurements for a residential student building located in Bucharest, Romania, collected at 1 frame/ 2 second reporting rate over 12 consecutive months.

Always-on appliances include the refrigerator and the wireless router. Several other appliances are installed in the residential unit: washing machine, lighting fixtures, electrical iron, vacuum cleaner, various ICT charging devices, and air conditioning (seldom used).

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1253 Views

The dataset includes active power measurements for a residential student building located in Bucharest, Romania, collected at 1 frame/second reporting rate over 10 consecutive months.

Always-on appliances include the refrigerator and the wireless router. Several other appliances are installed in the residential unit: washing machine, lighting fixtures, electrical iron, vacuum cleaner, various ICT charging devices, and air conditioning (seldom used).

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661 Views

This is the full ChatGPT transcript for the IEEE Power Engineering Letter "On the Potential of ChatGPT to Generate Distribution Systems for Load Flow Studies using OpenDSS". The abstract for the letter is as follows:

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826 Views

This data provides an overview of the simulation of Hybrid Electric Vehicles (HEVs) using MATLAB. HEVs have emerged as a promising solution for reducing emissions and improving fuel efficiency. MATLAB offers a flexible platform to accurately model and analyze the complex dynamics of hybrid powertrains, including various components and control strategies. The simulation capabilities enable the evaluation of HEV performance under different conditions, optimization of designs, and exploration of factors impacting energy consumption.

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668 Views

The dataset is created on June 8, 2023, at 12:28 AM, consists of 760 annotated images in Pascal VOC format.

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To ensure brevity, the main manuscript provides an abridged version of the scenario evaluation methodology and results. First, this supplementary material provides a discussion of the DM-test procedure employed and its validity. A detailed discussion and specific references related to the procedure applied to test the statistical significance in the improvement of the multivariate scores are presented. Then, the CC-ERCO formulation details, related SG evaluation procedure, and additional results are presented and outlined in more detail, supplementing the main article.

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Field frequency data of three real event cases from SMD-Ls.

 

Case 1:  a fast event.

At 17:21:31 on March 18, 2020, the second-line circuit breaker in Shanan, Jibei, China tripped. The valid SMD-L data points in the AC network of North China are in place H, Z, X, and N. 

Case 2:  a slow event.

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Schematic diagram to illustrate the separation and identification of nonlinear flow characteristics for the proposed method. Due to a fact that steam turbines and generators have different dynamic characteristics at different operating points, the static nonlinear flow characteristic (e.g., yellow solid lines in (a)) is overwhelmed by input and output data with dynamic characteristics, such as the blue dots in (a) and their corresponding time series are shown in (b). The key to identifying the nonlinear flow characteristic is to separate it from the linear dynamic submodels at different ope

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