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

Multimachine Stability01:25

Multimachine Stability

633
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:
633
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

934
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
934
Wind Turbine Machine Models01:24

Wind Turbine Machine Models

731
In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
731
Turbine-Governor Control01:17

Turbine-Governor Control

1.2K
Turbine-governor control is crucial for maintaining power system stability by balancing turbine mechanical power output with electrical load demand. This mechanism ensures that generator frequency and rotor speed are within acceptable limits during load variations. Turbine-generator units store kinetic energy due to their rotating masses; this energy is released to meet the load requirement when the load increases. The electrical torque of turbines rises to meet the demand, whereas the...
1.2K
Load-frequency control01:28

Load-frequency control

828
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
828
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

1.1K
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...
1.1K

You might also read

Related Articles

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

Sort by
Same author

SIFA: A two-stage adaptive ensemble framework for solar irradiance forecasting using a wrapper-based feature selection and chaotic manta ray optimization.

Scientific reports·2026
Same author

Adaptive cruise control for electric vehicles using hybrid-mode MPC.

Scientific reports·2026
Same author

Utilizing deep learning models for early detection and classification of fruit diseases: towards sustainable agriculture and enhanced food quality.

Scientific reports·2026
Same author

A robust framework for evaluating green mines towards sustainable development.

Scientific reports·2025
Same author

Synergistic impacts of organic modifications and foliar phytostimulants on soil properties, growth, yield, and chemical constituents of borage (borago officinalis L.).

BMC plant biology·2025
Same author

Magnetic and magnetotelluric data integration to determine the origin of Siwa Oasis Lakes, Western Desert, Egypt.

Scientific reports·2025
Same journal

Winter-associated downregulation of ovarian NR5A2 correlates with impaired follicle development in the striped hamster (Cricetulus barabensis).

Scientific reports·2026
Same journal

Both underestimation and overestimation of sleep duration predict mortality in older men with sleep disturbances.

Scientific reports·2026
Same journal

Predicting the flood susceptibility under land use and climate change scenarios using deep learning algorithms.

Scientific reports·2026
Same journal

Progress towards sustainable development and the urban-rural divide: an analysis of municipalities in Japan.

Scientific reports·2026
Same journal

Satellite-based analysis of precipitation across algeria's hydrographic watersheds (1983-2022) and their relationship with climate indices.

Scientific reports·2026
Same journal

Study on acoustic emission and infrared radiation characteristics of coal combination with different tectonic coal thickness.

Scientific reports·2026
See all related articles
  1. Home
  3. Machine Learning Models For Smart Grid Stability Prediction: A Comparative Analysis.
  1. Home
  3. Machine Learning Models For Smart Grid Stability Prediction: A Comparative Analysis.

Related Experiment Video

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.2K

Machine learning models for smart grid stability prediction: a comparative analysis.

Ahmed M Ali1, Omar K Dawoud2,3, Osama A Ghoneim3

  • 1Faculty of Computers and Informatics, Zagazig University, Zagazig, Sharqiyah, 44519, Egypt. aabdelmonem@fci.zu.edu.eg.

Scientific Reports
|April 17, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a machine learning (ML) approach for smart grid (SG) stability prediction. Light Gradient Boosting Machine achieved over 99.9% accuracy, enhancing grid reliability and energy efficiency.

Keywords:
Data preprocessingMachine learningOptimizationRenewable energySmart grid

Related Experiment Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.2K

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • The integration of renewable energy sources and fluctuating demand challenges smart grid (SG) stability.
  • Traditional methods for monitoring grid instability are often reactive and insufficient for modern energy systems.

Purpose of the Study:

  • To develop and evaluate a machine learning (ML) methodology for accurate classification and prediction of smart grid stability.
  • To enhance the effectiveness and reliability of smart grid operations through intelligent monitoring.

Main Methods:

  • Utilized fourteen distinct machine learning models for classification and prediction tasks.
  • Applied advanced feature engineering and selection techniques (filter, wrapper, embedded) to optimize model performance and reduce dimensionality.
  • Employed Bayesian optimization (TPE) and metaheuristic optimization (GWO) for hyperparameter tuning to maximize ML model accuracy.
  • Incorporated explainable AI methods to ensure model trustworthiness and transparency.

    • Main Results:

    • The Light Gradient Boosting Machine model demonstrated exceptional predictive performance, achieving near-perfect accuracy.
    • The TPE-optimized model reached an accuracy of 99.95%, while the GWO-optimized model achieved 99.90%.
    • Feature selection and hyperparameter optimization significantly improved model accuracy and efficiency.

    Conclusions:

    • The proposed ML methodology offers a robust solution for predicting smart grid stability, surpassing traditional approaches.
    • The study highlights the potential of advanced ML techniques, particularly Light Gradient Boosting Machine, in improving smart grid resilience and supporting energy efficiency.
    • Explainable AI integration ensures the reliability and trustworthiness of the predictive models for practical application.