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NetworkX – Network Analysis in Python

networkx.org

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Re: NetworkX – Network Analysis in Python

#51

Post by clwg »

jonathaneunice wrote: post #10

Been a few years since I put NetworkX through its paces, but the several times I have tried it, found remarkably weak support for graph layout and display. NetworkX analytic routines may be strong, but attractively displaying graph-structured problems remarkably more interactive and attractive via d3.js, GraphViz, etc. At least for my problems, communicating graph structures, and having nodes and edges that represent…

If I want to visualize a graph, particularly large ones I just dump it out to gexf[0] format and load it into gephi[1]. It kicks back some legacy formatting errors but it doesn't really impact the graph. Gephi also supports temporal based graph analysis which is nice.

[0] https://networkx.org/documentation/stable/reference/readwrit... [1] https://gephi.org/

Re: NetworkX – Network Analysis in Python

#53

Post by zubiaur »

ok_dad wrote: post #21
zubiaur wrote: post #4

Love it. Using it for load balancing of substation transformers. The grid is a graph.

Interesting you should say that, as I am trying to start a project where I need to make an electric grid graph, but I am not sure where to find the node/edge data for substations and transmission lines that include their specs and capacities. Is that stuff open source somewhere, like with the ISOs, or do you need to build it from scratch?

Take a look here: https://electricgrids.engr.tamu.edu/electric-grid-test-cases...

I’m working on the distribution side, I take my graph data from the inputs for power flow: cyme, synergi etc

Re: NetworkX – Network Analysis in Python

#54

Post by tangue »

Back in the days I’ve discovered NetworkX and Gephi in a Coursera course and was really surprised about how simple it managed to represent visually such a hard problem (I’ve never been able to find this course again it started with Erdos number that’s the only thing I remember)

Re: NetworkX – Network Analysis in Python

#55

Post by standfest »

mickeyp wrote: post #13
jonathaneunice wrote: post #10

Been a few years since I put NetworkX through its paces, but the several times I have tried it, found remarkably weak support for graph layout and display. NetworkX analytic routines may be strong, but attractively displaying graph-structured problems remarkably more interactive and attractive via d3.js, GraphViz, etc. At least for my problems, communicating graph structures, and having nodes and edges that represent…

Laying out a graph so it's "friendly to humans" is a seriously hard problem. I've built complex DAG workflow engines using networkx and its layout tools and they worked just fine. But, yeah, I guess it depends on what you need? Export to .dot -> open in your favourite viewer.

I usually write small functions for postprocessing the proposed layouts from the default algorithms. So far this was always more than sufficient.

Re: NetworkX – Network Analysis in Python

#56

Post by ok_dad »

rwlincoln wrote: post #47
maliker wrote: post #38

Earlier quoted context omitted.

That data is privately held by utilities, and the high voltage transmission infrastructure is highly confidential (CEII/NERC CIP). If you just need sample data, I'd recommend checking out the test data provided with power flow simulators like OpenDSS for distribution systems [1] or MATPOWER for generation + transmission [2]. The IEEE test systems are what are used in research, they have the component specs you're loo…

MATPOWER and OpenDSS test cases are also available in CSV format: https://github.com/casecsv https://github.com/cktcsv

Thanks to you and the next commenter up, these synthetic data sets are perfect for my current use case.

Re: NetworkX – Network Analysis in Python

#57

Post by [deleted] »

low_tech_love wrote: post #35
jonathaneunice wrote: post #10

Been a few years since I put NetworkX through its paces, but the several times I have tried it, found remarkably weak support for graph layout and display. NetworkX analytic routines may be strong, but attractively displaying graph-structured problems remarkably more interactive and attractive via d3.js, GraphViz, etc. At least for my problems, communicating graph structures, and having nodes and edges that represent…

I recently found Cosmograph and have been using it for a project, it’s quite good for the specific things it does. https://cosmograph.app/

[deleted]

Re: NetworkX – Network Analysis in Python

#58

Post by diroussel »

jncfhnb wrote: post #36
soundarana wrote: post #34

Earlier quoted context omitted.

Not in the field, but don't forget the grid is dynamic and has feedback loops, control algorithms and humans in the loop. It's closer to an unstable chaotic system which needs constant balancing and tweaking.

Is that true? Surely things regress to a stable mean most of the time of a few archetypes, no?

Yes. It’s generally stable in most localities. “Instabilities” in this system are browns outs power failures and other events. There are stabilising features within most electricity grids, but they can only cope so much. In general forward planning is down so the amount of dynamic adjustment needed is within allowable range.

But to be honest i don’t know how modern grids have adapted with many more micro generators than in the old days.

Re: NetworkX – Network Analysis in Python

#59

Post by jncfhnb »

scrlk wrote: post #39
jncfhnb wrote: post #27

Earlier quoted context omitted.

I have tried to talk to engineers about contingency analyses for what would happen if a unit went down, and they tend to have very wishy washy answers. Or giant tediously compiled reports that can model exactly one change. Any idea why that is?

Good question. Are you talking about generation, transmission or distribution? From my experience as an electrical engineer working for a distribution network: * The traditional approach to network planning: take your edge cases (e.g. winter peak demand), and apply your engineering knowledge and intuition to manually study the most onerous outage conditions. * This will vary on where you are in the world, but network…

I meant transmission but I’m interested in both.

Can you say a bit more about built in slack? You mean like Distribution Automation switches to backfeed an area? This has felt sort of rare to me

Re: NetworkX – Network Analysis in Python

#60

Post by jncfhnb »

sceadu wrote: post #41
jncfhnb wrote: post #27

Earlier quoted context omitted.

I have tried to talk to engineers about contingency analyses for what would happen if a unit went down, and they tend to have very wishy washy answers. Or giant tediously compiled reports that can model exactly one change. Any idea why that is?

A single unit would correspond to a N-1 case when doing a transmission system study. There are ways of automating steady state analysis for this case to do a full sweep across the nearby system (either looking at k hops away, all parts in a zone (where a zone has a specific meaning in this context), or using a utility provided set of assets for the analysis). This pretty much consists of running a load flow for each…

I mean transmission. And by wishy washy I mean they have the reports but it’s not compiled into any sort of useful system so they are not able to quickly answer questions about it.

Most seem to outsource this analysis. I’m curious if you have a sense for how common it is for a transmission utility to really own this kind of analytics?

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