Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

790
Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk​, phase angle δk​, real power Pk​, and reactive power Qk​. Two of these four variables are inputs, while the power flow program computes...
790
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

715
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
715
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

2.8K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
2.8K
Statgraphics01:10

Statgraphics

370
Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
370
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

578
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
578
Multimachine Stability01:25

Multimachine Stability

535
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
535

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Including Empirical Prior Information in the Reliable Change Index.

Applied psychological measurement·2025
Same author

Solving variables with Monte Carlo simulation experiments: A stochastic root-solving approach.

Psychological methods·2024
Same author

The Impact of Measurement Model Misspecification on Coefficient Omega Estimates of Composite Reliability.

Educational and psychological measurement·2024
Same author

Partially and Fully Noncompensatory Response Models for Dichotomous and Polytomous Items.

Applied psychological measurement·2020
Same journal

Exploring psychological tradeoffs: Developing and demonstrating an R Shiny app for Pareto optimization.

Behavior research methods·2026
Same journal

The performance of Bayesian fit measures in detecting misspecified multilevel structural equation modeling.

Behavior research methods·2026
Same journal

Psychometric functions from multiple responses : Dedicated to the memory of Colin L. Mallows.

Behavior research methods·2026
Same journal

Low-cost, open-source, full-stack software and Arduino-based hardware for control of commercially available animal behavior systems.

Behavior research methods·2026
Same journal

PyNeon: A Python package for the analysis of Neon multimodal mobile eye-tracking data.

Behavior research methods·2026
Same journal

Talking surveys: How photorealistic embodied conversational agents shape response quality, engagement, and satisfaction.

Behavior research methods·2026
See all related articles

Related Experiment Video

Updated: Jan 11, 2026

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization
05:37

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

Published on: August 22, 2025

583

Spower: A general-purpose Monte Carlo simulation power analysis program.

R Philip Chalmers1

  • 1Department of Psychology, York University, Toronto, Canada. chalmrp@yorku.ca.

Behavior Research Methods
|November 18, 2025
PubMed
Summary
This summary is machine-generated.

Spower is a versatile R package for Monte Carlo simulations, enhancing power analyses with customizable criteria and reporting sampling uncertainty. It offers flexibility for researchers to define custom experiments or use predefined ones.

Keywords:
Monte Carlo simulationPower analysisR packageSoftwareStochastic root solving

More Related Videos

A Rapid Method for Modeling a Variable Cycle Engine
04:58

A Rapid Method for Modeling a Variable Cycle Engine

Published on: August 13, 2019

7.9K
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

1.3K

Related Experiment Videos

Last Updated: Jan 11, 2026

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization
05:37

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

Published on: August 22, 2025

583
A Rapid Method for Modeling a Variable Cycle Engine
04:58

A Rapid Method for Modeling a Variable Cycle Engine

Published on: August 13, 2019

7.9K
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

1.3K

Area of Science:

  • Statistics
  • Computational Biology
  • Psychometrics

Background:

  • Accurate statistical power analysis is crucial for research design and interpretation.
  • Existing tools may lack flexibility or comprehensive simulation capabilities.
  • Monte Carlo simulations offer a robust method for power analysis.

Purpose of the Study:

  • To introduce Spower, an R package for general-purpose Monte Carlo simulation experiments.
  • To provide a flexible tool for conducting various types of power analyses.
  • To enhance research design by offering detailed insights into sampling uncertainty.

Main Methods:

  • Development of an R package, Spower, as a Monte Carlo simulation tool.
  • Implementation of five distinct power analysis criteria: prospective/post hoc, a priori, compromise, sensitivity, and criterion.
  • Inclusion of customizable functions for population generation and analysis.
  • Integration of simulation counterparts of G*Power 3.1 subroutines for comparability.
  • Provision of subroutines for estimation precision improvement and visualization.

Main Results:

  • Spower offers a comprehensive suite of tools for power analysis through Monte Carlo simulation.
  • The package supports customizable experiments, allowing researchers to tailor analyses.
  • It quantifies sampling uncertainty for various power analysis criteria.
  • Compatibility with G*Power 3.1 subroutines ensures ease of transition and extensibility.

Conclusions:

  • Spower provides researchers with a powerful and flexible R package for conducting advanced power analyses.
  • The tool aids in improving study design and the precision of statistical estimates.
  • Its customizable nature and inclusion of established methods promote robust research practices.