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Method to Generate Synthetic Large Scale Power Grid Network Data Based on Pattern Identifying and Spatial Correlation for Simulation and Performance Test

IP.com Disclosure Number: IPCOM000243816D
Publication Date: 2015-Oct-19

Publishing Venue

The IP.com Prior Art Database

Abstract

This article provides a method to do analysis on given real infrastructure network data, and able to identify the pattern and spacial correlation with insights, and then the method can generate synthetic large scale infrastructure network data based on the analysis and target context.

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Method to Generate Synthetic Large Scale Power Grid Network Data Based on Pattern Identifying and Spatial

Method to Generate Synthetic Large Scale Power Grid Network Data Based on Pattern Identifying and Spatial

Correlation for Simulation and Performance Test

In many industries, it usually requires large scale of infrastructure network data.

e.g In city planning industry, infrastructure network simulation helps doing city infrastructure plan based on asset location and attributes in simulation.

e.g In energy industry, large scale of infrastructure network is significant in power grid network risk and health analysis.

e.g In water industry, large water pipe network benefits a lot in situation awareness

Current technology usually leverage following methods:

-Analysis software source code, identify a set of data which satisfies given testing criterion, and use simple symbolic evaluation to randomly derive discrete data.

-Extend small amount of real customer data, duplicate real data and linearly connect the duplicated data together within larger network scope.

-Build data based on flat data distribution but not considering cumulative impact between data nodes.

Here are also some problems:

- Software source code scan is not reasonable and complicated.

- Linearly duplicate and merge data won't increase data complexity, and no context consider .

- Flat data distribution probability based data generation is not reasonable and not close to reality. For example: flat data distribution of power grid :

- A cable is able to connect to switch and busbar only when it's upstream is a substation transformer.

- This pattern can not be found if only do flat data distribution probability calculation. Incorrect data will be produced as :

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- Cable - Switch - Busbar -

Claim 1: Build graph model based on physical network connectivity and logical impact analysis.

Claim 2: Identify connectivity pattern based on

a. upstream nodes and historical record

b. network topology

c. comulative occurence probability

d. context

Claim 3: Reasonably generate synthetic large scale infrastructure network data based on contexts match Here is the architecture overview of our disclosure.

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Components details description:

Context model contains 3 types information:

- Geospatial context

Geospatial context define the location and geosptial information, which makes the network can be projected on a map.

- Natrual environment context

Natural environment context defines natural properties li...