[UC06] GIS Grid Connectivity Validation & Correction

Data Quality Improvement

With "clean" data, a utility can perform insightful analysis requiring asset situational awareness and better address the challenges of managing the energy transition and improving grid reliability.

Utilities often have error-ridden connectivity data describing the interconnections of the components and assets of their distribution grid, which can lead to errors such as topological errors or islanded assets (separated from all sources). Any grid analysis with this “dirty” data can lead to incorrect conclusions, rendering the analysis useless and potentially detrimental. To correct data employees are often tasked with manually sorting through connectivity data to identify and correct errors.

This data problem represents two significant issues: the labour and time required by data professionals to manually sift through data and the organization’s inability to gather valuable insights from their grid data. Not only is the labour of a data professional a high cost on the utility, but it also takes away an experienced skillset that could be used to tackle more complex data challenges. In addition, this manual process results in a much longer timeline to correct the data. Without “clean” data, the utility cannot perform insightful analysis, thus hampering their abilities to manage countless data-driven challenges, such as managing the energy transition and improving grid reliability.


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