Eugene
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Rajdeep, You are quite right about the decision of what to model being fuzzy. It is always a judgement call. I often find the decision driven by schedule more than anything else.
My initial comments were aimed at communications systems. There, stability is important but it is not necessarily a key integration issue. (Probably the biggest stability risk in a receiver chain for example is unstable interactions between common mode feedback loops, and I would like to find a good way to assess that risk.)
I think the situation is different for power systems, especially if you are connecting a lot of power supplies to a common bus that is also controlled with a feedback loop. In that case, stability is definitely a risk that should be assessed and I think behavioral models are fully capable of doing the job. When you have one supply driving others, the others present a constant power load to the main supply. About an operating point, the constant power load looks like a negative resistance, which can cause instability. Furthermore, usually the load supplies have input filters to keep them from corrupting the main bus with switching noise. If the aggregate impedance of those filters dips below the bus impedance at any frequency, you can have system level oscillations at that frequency. If you can identify the worst case configurations, you can simply analyze those modes separately. However, if want to be sure you did not miss the true worst case condition that may arise as you simulate full system operation, you should include all the dynamics in your system level simulation.
If you have a large number of supplies, brute force simulation may be impractical and you may have to build state space averaged models. If you want to run the system through all loading conditions, you may have to use dual-mode state space averaged models, models that automatically switch between continuous and discontinuous conduction modes.
You are also right about simulating a large dynamic system with feedback loops being hard, especially if you use behavioral state space averaged models, and esepcially if they are dual-mode models. You may have to solve some convergence problems.
One other thing to keep track of when assessing distributed power systems is common mode emi filters. They can have a large differential mode component that can affect stability too.
The bottom line is risk. If you can be reasonably sure you have identified and assessed the worst case conditions individually and perhaps even in a way that lets you analyze the blocks separately, you can ignore dynamics when simulating system level functionality. If not, then I would go for the dynamic system level model.
Also, if you are dealing with a large power bus, you should also consider simulating fault tolerance. For example, if you apply a short and a fuse blows, you will see a large voltage spike that could damage components. This is very difficult to model accurately because the spike involves parastic inductances. But it may be worth checking to make sure the system recovers, assuming nothing is damaged. Fault simulations should include dynamics.
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