Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
versions View all 2 versions
addClaim

COCOON pv_2_3 input dataset and results

Authors: Rodríguez del Nozal, Álvaro; Universidad de Sevilla;

COCOON pv_2_3 input dataset and results

Abstract

Power Systems State Estimation Results - COCOON Project This repository contains a dataset and a set of results derived from a cybersecurity-oriented analysis carried out within the framework of the European COCOON project. The data focus on power system state estimation applied to a photovoltaic (PV) plant, with the objective of assessing the resilience of monitoring functions against cyberattacks, specifically False Data Injection (FDI). In line with COCOON’s vision of strengthening cyber-physical security in Electric Power and Energy Systems, the analysis evaluates both the detection and identification of malicious data manipulations, contributing to improved trustworthy information exchange between TSOs, DSOs, aggregators, and distributed renewable energy sources. This repository contains the results of the power systems state estimation developed by the University of Seville (USE), focusing on the monitoring and security of PV infrastructures. Description of the PV Plant The analysis is based on a PV plant layout consisting of two Medium Voltage (MV) feeders, each equipped with 3 inverters. The inverters are connected to the MV feeder through transformers, and the plant evacuates energy to the main grid via an electrical substation. The following files in the root directory define the system's topology and monitoring setup: pv_2_3.svg: A graphical representation of the PV plant. Measurements are assumed to be available at all points marked with red squares in this diagram. pv_2_3.json: A data file containing the technical parameters and connectivity of the network. meas.zip: A compressed archive containing the measurements considered for the state estimation analysis. Repository Structure The code and results are organized into multiple folders, each representing a specific test case or scenario. Each folder follows a consistent internal structure: /test_case_name/ │ ├── data/ # JSON files containing results from simulation tests ├── figs/ # Graphical representations (plots, charts, etc.) of the results └── README.md # Interpretation and explanation of the specific test case Folder Details: data/: Contains .json files with the output data generated from the simulations. These files can be used for further analysis or validation. figs/: Includes visualizations of the simulation results (PDF format). These plots help in understanding trends, anomalies, and overall system behavior. README.md: Each test case includes its own documentation offering key findings and relevant notes for understanding the results. Analysis Methodology All test cases are analyzed from two perspectives to evaluate system resilience: Detection Phase: Focuses on determining whether a cyberattack has occurred. This involves evaluating the system's ability to distinguish between normal operation and compromised scenarios. Identification Phase: Aims to identify which specific measurement(s) have been manipulated by the attacker. This allows for a deeper understanding of the attack vector and its impact on the system. For both aspects, relevant metrics are provided to quantify the performance of the detection and identification processes, as well as their effects on the accuracy of the state estimation. If further details are required or any questions need clarification, please contact Álvaro Rodríguez at arnozal@us.es.

Related Organizations
  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average