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Related Concept Videos

Power System Distribution01:25

Power System Distribution

Power system distribution involves delivering electrical energy from power plants to consumers through a network of transmission and distribution systems. The process begins at power plants, where energy from coal, gas, nuclear, water, and wind is converted into electrical energy. These plants use three-phase generators, typically rated between 50 to 1300 MVA, with terminal voltages ranging from a few kV to 20 kV, depending on the size and age of the units.
The transmission system is designed...
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

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.
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

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...
Multimachine Stability01:25

Multimachine Stability

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:
Load-frequency control01:28

Load-frequency control

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...

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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
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Improving predictive reliability and automation of smart grids using the StarNet ensemble model.

Amit Chhabra1, Sunil K Singh1, Sudhakar Kumar1

  • 1CSE, Chandigarh College of Engineering and Technology, Sector 26, Chandigarh, India.

Scientific Reports
|February 19, 2026
PubMed
Summary

The StarNet Ensemble Model improves smart grid reliability using ensemble learning for automated monitoring and prediction. This intelligent framework enhances grid stability and operational efficiency for stable electricity distribution.

Keywords:
Smart Grid StabilityStacking-based Ensemble LearningStar-Net Model

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Smart grids require high predictive reliability and automation for stable electricity distribution.
  • Existing methods may lack robustness and generalization across diverse grid conditions.

Purpose of the Study:

  • To introduce the StarNet Ensemble Model, a web-based intelligent framework for enhancing smart grid stability.
  • To develop an automated, real-time monitoring and prediction system for grid performance.
  • To validate the model's effectiveness on both synthetic and real-world benchmark datasets.

Main Methods:

  • Developed a stacking-based ensemble learning framework (StarNet Ensemble Model).
  • Integrated a machine learning-driven graphical user interface (GUI) for automated operations.
  • Utilized CatBoost, AdaBoost, Random Forest, SVM, and KNN as base learners with a Random Forest meta-model.
  • Employed stratified 10-fold cross-validation for rigorous model evaluation.

Main Results:

  • Achieved 99.43% accuracy on a synthetic dataset.
  • Attained 98.94% accuracy on the UCI Smart Grid Stability Dataset.
  • Reached 97.83% accuracy on the IEEE 14-Bus Test System.
  • Demonstrated a cross-dataset transfer accuracy of 95.41%.

Conclusions:

  • The StarNet Ensemble Model exhibits robustness and strong generalization capabilities.
  • The framework effectively enhances predictive reliability and automation in smart grids.
  • The model offers a viable solution for improving smart grid operational stability and efficiency.