Python Programming Fundamentals

Lee, Kent D.

doi:10.1007/978-1-84996-537-8

Cited by 7 publications

(2 citation statements)

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“…The whole network reduction process was carried out in DIgSILENT software [13]. Moreover, the simulation of the loss of transmission lines (under N-1 contingency criteria) and the corresponding power flow computations were performed in an automated way through a script developed in Pyhton [16]. Reported computational times in the examples are based on average estimates considering a computer with processor Intel Core i7-8665U, CPU @ 1.90 GHz, and 16.0 RAM.…”

Section: Simulation Resultsmentioning

confidence: 99%

Evaluation of Static Network Equivalent Models for N-1 Line Contingency Analysis

Ramirez-Gonzalez

Bossio

Sevilla

et al. 2022

2022 4th Global Power, Energy and Communication Conference (GPECOM)

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Although large scale simulation models may better represent the behavior of practical power systems, they are time-consuming and turn out to be impractical for some desired applications, particularly when the focus of the study is on only a small portion of the entire system and the use of a complete model would dramatically increase the computational effort and time. Therefore, network equivalent models can be used in this case to facilitate and accelerate the completion of the required analysis related to the specific subsystem. In this sense, and considering here small and large sample networks, the evaluation of static equivalents derived from popular Ward and REI reduction methods is presented in this paper. The performance of the reduced networks is assessed under the N-1 contingency criteria, and takes into account not only the base case condition but also post contingency response with the whole set of lines in the particular area of study. Obtained equivalents are compared in simulation in terms of deviations from the original system and average computational time needed to complete the involved power flow calculations.

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Section: Simulation Resultsmentioning

confidence: 99%

Evaluation of Static Network Equivalent Models for N-1 Line Contingency Analysis

Ramirez-Gonzalez

Bossio

Sevilla

et al. 2022

2022 4th Global Power, Energy and Communication Conference (GPECOM)

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“…During the introductory lecture on Python, the instructor spent about 20 min introducing the students to simple computational concepts and Python commands (such as basic arithmetic, variables, the print() function, etc.) as well as resources (python.org, using Google to answer specific questions, books in the library − ) that might be helpful and then asking the students to solve simple arithmetic problems while walking around the room to help students with either any Anaconda installation issues or coding questions.…”

Section: Implementation and Assessmentmentioning

confidence: 99%

Series of Jupyter Notebooks Using Python for an Analytical Chemistry Course

Menke

2020

J. Chem. Educ.

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UC Merced’s upper-division analytical chemistry course has been modified to include a series of Jupyter notebooks intended to introduce chemistry students to the Python computer language. These Jupyter notebooks were designed to cover a wide variety of topics common to quantitative and instrumental analysis. Assuming no prior programming experience, the notebooks are scaffolded to introduce students the Python’s syntax while applying Python to solving chemical problems. Jupyter and Python were chosen because they are free, widely available, and widely used.

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Actuators Used in Rapid Prototyping

Kanagachidambaresan

2021

Internet of Things

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Python Programming Fundamentals

Cited by 7 publications

References 0 publications

Evaluation of Static Network Equivalent Models for N-1 Line Contingency Analysis

Evaluation of Static Network Equivalent Models for N-1 Line Contingency Analysis

Series of Jupyter Notebooks Using Python for an Analytical Chemistry Course

Actuators Used in Rapid Prototyping

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