Skip to yearly menu bar Skip to main content


Poster
in
Workshop: Tackling Climate Change with Machine Learning: Global Perspectives and Local Challenges

Topology Estimation from Voltage Edge Sensing for Resource-Constrained Grids

Mohini Bariya · Margaret Odero · Margaret Odero · Clare-Joyce Fomonyuy Ngoran

Keywords: [ Time-series analysis ] [ Climate justice ] [ Unsupervised and semi-supervised learning ]


Abstract:

Electric grids are the conduit for renewable energy delivery and will play a crucial role in mitigating climate change. To do so successfully in resource-constrained low- and middle-income countries (LMICs), increasing operational efficiency is key. Such efficiency demands evolving knowledge of the grid’s state, of which topology---how points on the network are physically connected---is fundamental. In LMICs, knowledge of distribution topology is limited and established methods for topology estimation rely on expensive sensing infrastructure, such as smart meters or PMUs, that are inaccessible at scale. This paper lays the foundation for topology estimation from more accessible data: outlet-level voltage magnitude measurements. It presents a graph-based algorithm and explanatory visualization using the Fielder vector for estimating and communicating topological proximity from this data. We demonstrate the method on a real dataset collected in Accra, Ghana, thus opening the possibility of globally accessible, cutting-edge grid monitoring through non-traditional sensing strategies coupled with ML.

Chat is not available.