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8 results for “HVDC”
HVDC-WISE Deliverable 2.1 Appendix 3 Survey of Existing and Planned DC grids
<p>This excel sheet provides an overview as of 31st March 2023 of VSC and LCC links (to the best of the authors knowledge). This acts as as an Appendix to Deliverable 2.1 "Resilience Needs and Objectives" of the HVDC-WISE Horizon Europe project. The information contained in this excel workbook was used as part of the analysis on existing and planned HVDC grids.</p>
NEW REFERENCE HVDC DIVIDER FOR THE CALIBRATION OF TESTING EQUIPMENT UP TO 1600 kV
<p>An international metrology project named “EMPIR 19ENG02 FutureEnergy” started in June 2020, is developing, building, and evaluating measurement equipment and systems for the UHV level. This paper describes the work on a new reference UHVDC voltage divider for the calibration of testing equipment up to at least 1600 kV. This divider has a modular design and target expanded measurement uncertainty of 200 μV/V. The need for such measurement systems results from new measurement methods for future energy transmission grids. After the construction of the UHVDC voltage divider, the targeted measurement uncertainty of 200 μV/V was successfully at 1200 kV during a measurement campaign at PTB. The linearity and temperature dependence of the UHVDC divider modules as well as the overall results of this new divider are included in this work. This work will lead to new calibration and measurement capabilities (CMC) for traceability of the designed divider systems for participating national metrology institutes (NMI).</p>
Dataset for Validation of a Qualification Procedure Applied to the Verification of Partial Discharge Analysers Used for HVDC or HVAC Networks
<p>Data set for the publication named: "Validation of a Qualification Procedure Applied to the Verification of Partial Discharge Analysers Used for HVDC or HVAC Networks"</p>
Data set for Metrological Qualification of PD Analysers for Insulation Diagnosis of HVDC and HVAC Grids
<p>Dataset for the publication named: "Metrological Qualification of PD Analysers for Insulation Diagnosis of HVDC and HVAC Grids"</p>
Dataset: On the Stability of a Modular 1000 kV HVDC divider design
<p>In order to calibrate high voltage dividers in testing laboratories and HVDC converter substations, a new measurement infrastructure for on-site calibration up to 1000 kV was established in 2013. A 1000 kV wide-band modular HVDC divider was built divided into stackable 200 kV HV modules for agile on-site assessment, with protection against damage from transients, and for measuring of ripple voltage. Nine 200 kV modules were built by the five NMIs and stacked to one 1000 kV divider and one 800 kV voltage divider. The scale factors of the two dividers were determined in a comparison campaign in June 2013, resulting in a combined expanded uncertainty better than 20 μV/V at 1000 kV, and better than 15 μV/V for 200 kV. Traceability of five modular systems have since been<br> maintained using three methods; where the first method is calibration of the HV resistance of each module and LV arm, calculating the scale factors. The second method uses the Hamon transfer method, applied by RISE with five 200 kV modules. The third method is by comparison of the scale factor which includes the LV arm against a lower voltage reference divider. This paper presents the stability and robustness of all divider systems from June 2013 to spring 2022. The long-term stability is presented for the resistance of both the high voltage and the low voltage arm as well as the scale factor. The result shows an unprecedented stability of a HV divider system.</p>
Dataset for New Synthetic Partial Discharge Calibrator for Qualification of Partial Discharge Analyzers for Insulation Diagnosis of HVDC and HVAC Grids
<p>Data set for the publication named: "New Synthetic Partial Discharge Calibrator for Qualification of Partial Discharge Analyzers for Insulation Diagnosis of HVDC and HVAC Grids"</p>
Data for "Reducing operational costs of offshore HVDC energy export systems through optimized maintenance"
<p>The files in this record contain the scripts, input data and results for the maintenance model described in the above publication (will be added later).</p> <p>To use the scripts, you need the software MATLAB (min. version 2016b).</p> <p>Steps to run the model:</p> <p>1. run "ini.m"</p> <p>2. run "szenarien_~"</p> <p>3. save the results in the directory "Ergebnisse"</p> <p>4. run scripts located in the directory "Auswertungen"</p>
Supplementary data: "Revealing interactions between HVDC cross-area flows and frequency stability with explainable AI"
<p>This repository contains processed data and result files for the paper <a href="https://arxiv.org/abs/2204.10727">Revealing interactions between HVDC cross-area flows and frequency stability with explainable AI</a>. The code for producing the processed data and the results is <a href="https://github.com/sebastianptz/XAI-for-HVDC">available at github</a>.</p> <p><strong>Data</strong></p> <p>The data folder contains the feature and target data used to train the ML model:</p> <ul> <li>stability_input: A Folder containing training and test sets for the stability model for each area.</li> <li>flow_input: A Folder containing training and test sets for the flow model for each link.</li> <li><em>raw_input_data.h5</em><strong> </strong>:<strong> </strong>The aggregated external features without additional engineered features.</li> <li><em>input_forecast.h5 </em>and<em> input_actual.h5: </em>The day-ahead available (forecast) and ex-post available (actual) data of external features including the engineered features.</li> <li><em>indicators.h5 </em>: The grid frequency stability indicators.</li> <li><em>documentation_of_data_download</em>: Information files concerning the ENTSO-E raw data and its aggregation.</li> <li>HVDClinks: A Folder containing preprocessed time series for scheduled and unscheduled HVDC flows</li> </ul> <p><strong>Data sources</strong></p> <p>Most of the data is derived from ENTSO-E Transparency Platform data [1]. The grid stability indicators (indicators<em>.h5</em>) are based on publicly available data from the German Transmission System Operators (TSOs) [2].</p> <p><strong>Results</strong></p> <p>The stability_results and the flow_results folder contain the results of hyperparameter optimization, model prediction and interpretation via SHAP for the respective models.</p> <ul> <li><em>cv_results_gtb_full.csv</em> : Performance results for each combination in the hyperparameter optimization.</li> <li><em>cv_best_params_gtb_full.csv</em> : Hyperparameters used in the final (optimized) model.</li> <li><em>shap_values_gtb_full.npy</em> : First-order SHAP values calculated on different data sets: The train set, the randomized test set and the continuous test set.</li> <li><em>y_pred.h5/y_pred_links.h5</em> : Predictions of daily profile predictor and Machine Learning models.</li> </ul> <p><strong>Disclaimer</strong></p> <p>The data might be subject to copyright or related rights. Please consult the primary data owner.</p> <p> </p> <p> </p> <div> </div>
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