ETAP PS vs. Jupyter Notebook

ETAP is highly recommended for evaluating static conditions in electrical power systems, whether in high or medium complexity networks. For example, study of load flow, analysis of frequency harmonics.

On the other hand, I would not recommend ETAP for the simulation of highly complex control systems that require a dynamic analysis of the variables.

I've created a number of daisy chain notebooks for different workflows, and every time, I create my workflows with other users in mind. Jupiter Notebook makes it very easy for me to outline my thought process in as granular a way as I want without using innumerable small. inline comments.

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• Excellent facility for the design of electrical systems, since it has multiple options and configuration.
• Truly intuitive, it is easy to adapt to the user interface, so the learning curve is very fast.
• It offers a wide variety of electrical studies that can be carried out, therefore it is very versatile for multiple applications.

• Simple and elegant code writing ability. Easier to understand the code that way.
• The ability to see the output after each step.
• The ability to use ton of library functions in Python.
• Easy-user friendly interface.

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• Perhaps the interface is somewhat confusing the first time you use the program.
• It offers a wide configuration, which can be overwhelming.
• Sometimes the program can freeze depending on the size of the electrical grid that is being simulated. This also depends on the software requirements.

• Need more Hotkeys for creating a beautiful notebook. Sometimes we need to download other plugins which messes [with] its default settings.
• Not as powerful as IDE, which sometimes makes [the] job difficult and allows duplicate code as it get confusing when the number of lines increases. Need a feature where [an] error comes if duplicate code is found or [if a] developer tries the same function name.

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ETAP PS is really easy to use. When recreating the power system to be simulated, it is easy to obtain a result close to reality due to the multiple components offered in the interface. As for the electrical studies available, they are simple to execute and require really little configuration to make them work, always offering a wide variety of options to adjust the program to the simulation of the desired condition.

Jupyter is highly simplistic. It took me about 5 mins to install and create my first "hello world" without having to look for help. The UI has minimalist options and is quite intuitive for anyone to become a pro in no time. The lightweight nature makes it even more likeable.

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Sometimes when the program crashes for a random reason, it is difficult to find a direct solution to the problem. I think better documentation is needed for this type of case. Still, more and more people are sharing their work on the web, making it easier to orient yourself when these issues occur.

I haven't had a need to contact support. However, all required help is out there in public forums.

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Simulink allows to analyze and simulate different variables with respect to time in an electric power system, but it is more focused on visual programming On the other hand, ETAP is better designed for static simulation of power systems, offering options and studies in a very more direct and easier to execute.

With Jupyter Notebook besides doing data analysis and performing complex visualizations you can also write machine learning algorithms with a long list of libraries that it supports. You can make better predictions, observations etc. with it which can help you achieve better business decisions and save cost to the company. It stacks up better as we know Python is more widely used than R in the industry and can be learnt easily. Unlike PyCharm jupyter notebooks can be used to make documentations and exported in a variety of formats.

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• Improves the reliability of the electrical system.
• Study of the system operations, allowing a better adjustment of the facilities that are in production.
• Provides results that allow you to select solutions for energy saving.

• Positive impact: flexible implementation on any OS, for many common software languages
• Positive impact: straightforward duplication for adaptation of workflows for other projects
• Negative impact: sometimes encourages pigeonholing of data science work into notebooks versus extending code capability into software integration

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