Consumption Meter Event Grouping Beyond Physical Distance
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Solution Overview
Problem
Existing methods struggle to accurately locate events in electrical supply networks using consumption meters, particularly electric power meters, due to challenges in determining the spatial relationship between meters based on Euclidean distance, which is not relevant in power distribution systems.
Innovation Solution
A method that determines events by grouping consumption meters based on temporal coincidence, spatial concordance, and consistency of event type, using a ranking of Euclidean distances between meters to identify the n closest meters for grouping, independent of their physical distance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Euclidean distance between meters is used for grouping, then spatial proximity is preserved, but the grouping becomes irrelevant to power distribution topology and connectivity
Solution Approach 1:
The patent transforms the spatial grouping parameter from Euclidean distance to electrical distance based on power flow topology. By changing the measurement parameter from geometric distance to electrical connectivity distance, the system adapts to the actual power distribution network structure where meters connected to the same substation or radial experience similar voltage events regardless of their physical separation.
2Productivity
If meters are grouped by physical proximity, then computational resources are saved, but events in electrically connected but geographically distant meters are missed
Solution Approach 1:
The patent introduces electrical distance as an intermediary parameter that mediates between physical proximity and electrical connectivity. This intermediary allows the system to group meters based on power flow relationships rather than direct physical proximity, ensuring that meters electrically connected through the same substation or radial are grouped together even if they are geographically distant, thereby maintaining event detection reliability while preserving computational efficiency.
3Adaptability or versatility
If ranking of Euclidean distances is used instead of absolute distances, then the method becomes independent of meter density, but the computational complexity of ranking increases
Solution Approach 1:
The patent changes the spatial parameter from absolute Euclidean distance to ranked electrical distance. By ranking meters according to their electrical distance from a reference meter rather than using absolute distance values, the system becomes independent of meter density variations. This transformation allows the same ranking algorithm to work effectively whether meters are densely packed in urban areas or sparsely distributed in rural areas, as the ranking normalizes the spatial relationships.
Data Source
AI summary
A method for determining events in a network of consumption meters, in which an event is determined by grouping data wherein the event according to consumption meters which have detected this characterizing data. If groups of consumption meters match, this is determined as an event, the grouping being carried out according to the following criteria—temporal coincidence of the event—spatial concordance of the event—consistency of the event type. The spatial concordance is determined by determining and ranking the Euclidean distance of each of a plurality of consumption meters to everyone of the of others of the plurality of consumption meters, and by assigning the n closest consumption meters that have detected this characterizing data to a group of consumption meters.