Managing Heterogeneous Datasets for Dynamic Risk Analysis of Large-Scale Infrastructures

Managing Heterogeneous Datasets for Dynamic Risk Analysis of Large-Scale Infrastructures

Blog Article

Risk assessment lochby venture pouch and management are some of the major tasks of urban power-grid management.The growing amount of data from, e.g., prediction systems, sensors, and satellites has enabled access to numerous datasets originating from a diversity of heterogeneous data sources.While these advancements are of great importance for more accurate and trustable risk analyses, there is no guidance on selecting the best information available for power-grid risk analysis.

This paper addresses this gap on the basis of existing standards in risk assessment.The key contributions of this research are twofold.First, it proposes a method for reinforcing data-related risk analysis steps.The use of this method ensures that risk analysts will methodically identify and assess the available data for informing the risk analysis key parameters.Second, it develops a method (named the three-phases method) based on metrology for selecting the best datasets according to their informative potential.

The method, thus, formalizes, in a traceable and reproducible manner, the process for choosing one dataset to inform a parameter in detriment of another, which can lead to more accurate risk analyses.The method is applied to a case study of vegetation-related risk analysis in power grids, a common challenge faced by power-grid operators.The application demonstrates that a dataset originating from an initially less valued data source may be preferred to a dataset originating from a higher-ranked data source, the content of which is outdated or of too low quality.The results confirm that the method enables a dynamic optimization of dataset selection upfront of any risk analysis, supporting the application of dynamic risk 15-eg2373cl analyses in real-case scenarios.