How can I use GPU on Google Colab after exceeding usage limit?

If you use GPU regularly, runtime durations will become shorter and shorter and disconnections more frequent. The cooldown period before you can connect to another GPU will extend from hours to days to weeks. Google tracks everything. They not only know your accounts' usage but also the usage of accounts that appear related to your account and will adjust usage limits accordingly if they even suspect someone of trying to abuse the system. They will never give you an explicit reason why the runtime disconnected or why you can't connect to GPU other than the generic message about "usage limits". Neither will they ever give users a straightforward way to track their usage because all that would do is make it easier for people to skirt the restrictions. If your account is basically blacklisted they will never actually tell you your account is blacklisted because that creates more headaches for them. You'll just get the same message about usage limits when trying to connect forever. They prefer to have users confused and guessing because that keeps all the power with Google. And for a free service, who's to say there's anything wrong with that.

Edit: For Colab Pro they likely won't fatally restrict an account for over-usage but they can significantly restrict it by extending the cooldown period to 3-5 days, reducing runtime durations from 24 hrs to 6-8 hrs, etc. Keep in mind this is for people running multiple accounts multiple times a week for the maximum duration. If you're just using a single account once or twice a week you shouldn't have a problem.

Colab's free version works on a dynamic usage limit, which is not fixed and size is not documented anywhere, that is the reason free version is not a guaranteed and unlimited resources.
Basically, the overall usage limits and timeout periods, maximum VM lifetime, GPU types available, and other factors vary over time. Colab does not publish these limits, in part because they can (and sometimes do) vary quickly.

GPUs and TPUs are sometimes prioritized for users who use Colab interactively rather than for long-running computations, or for users who have recently used less resources in Colab. As a result, users who use Colab for long-running computations, or users who have recently used more resources in Colab, are more likely to run into usage limits and have their access to GPUs and TPUs temporarily restricted. Users interested in having higher and more stable usage limits can use Colab Pro.

[THIS IS NOT A FIX BUT A WORKAROUND]

There is no specific way to fix this, I would suggest if you have more than one Google account, to copy your notebook or code to Colab using the other Google account and continue working.

Seems there is no concrete information available.

What are the usage limits of Colab?

What are the usage limits of Colab? Colab is able to provide free resources in part by having dynamic usage limits that sometimes fluctuate, and by not providing guaranteed or unlimited resources. This means that overall usage limits as well as idle timeout periods, maximum VM lifetime, GPU types available, and other factors vary over time. Colab does not publish these limits, in part because they can (and sometimes do) vary quickly.

GPUs and TPUs are sometimes prioritized for users who use Colab interactively rather than for long-running computations, or for users who have recently used less resources in Colab. As a result, users who use Colab for long-running computations, or users who have recently used more resources in Colab, are more likely to run into usage limits and have their access to GPUs and TPUs temporarily restricted. Users with high computational needs may be interested in using Colab’s UI with a local runtime running on their own hardware. Users interested in having higher and more stable usage limits may be interested in Colab Pro.