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

Load-frequency control01:28

Load-frequency control

611
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...
611
Current Growth And Decay In RL Circuits01:30

Current Growth And Decay In RL Circuits

4.5K
The current growth and decay in RL circuits can be understood by considering a series RL circuit consisting of a resistor, an inductor, a constant source of emf, and two switches. When the first switch is closed, the circuit is equivalent to a single-loop circuit consisting of a resistor and an inductor connected to a source of emf. In this case, the source of emf produces a current in the circuit. If there were no self-inductance in the circuit, the current would rise immediately to a steady...
4.5K
Phase-lead and Phase-lag Controllers01:22

Phase-lead and Phase-lag Controllers

522
Understanding the working function of different types of controllers can be illustrated with practical analogies, such as adjusting a stereo's volume equalizer. Cranking up the bass involves a phase-lead controller, which functions as a high-pass filter, while increasing the treble uses a phase-lag controller, which acts as a low-pass filter. PD controllers, similar to high-pass filters, enhance the system's response to high-frequency components. PI controllers, akin to low-pass...
522
Transient and Steady-state Response01:24

Transient and Steady-state Response

507
In control systems, test signals are essential for evaluating performance under various conditions. The ramp function is effective for systems undergoing gradual changes, while the step function is suitable for assessing systems facing sudden disturbances. For systems subjected to shock inputs, the impulse function is the most appropriate test signal.
These test signals are integral in designing control systems to exhibit two key performance aspects: transient response and steady-state...
507
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

724
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:
724
Linear time-invariant Systems01:23

Linear time-invariant Systems

863
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
863

You might also read

Related Articles

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

Sort by
Same author

Toward adaptive control power sharing and bus voltage regulation for DC microgrids.

Scientific reports·2026
Same author

AI-enhanced techno-economic and environmental optimization for nearly zero-energy building retrofitting: a case study of university campus.

Scientific reports·2026
Same author

Advanced active disturbance rejection control for enhancing frequency stability in low-inertia power grids linked with virtual inertia applications.

Heliyon·2025
Same author

Integrating sustainability into higher education challenges and opportunities for universities worldwide.

Heliyon·2024
Same author

The role of biofuels for sustainable MicrogridsF: A path towards carbon neutrality and the green economy.

Heliyon·2023
Same author

Energy Management and Control in Multiple Storage Energy Units (Battery-Supercapacitor) of Fuel Cell Electric Vehicles.

Materials (Basel, Switzerland)·2022

Related Experiment Video

Updated: Jan 13, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

2.1K

Reinforcement learning driven adaptive active frequency drift for fast and reliable islanding detection.

Ahmed G Abo-Khalil1,2, Khairy Sayed3,4, Nsilulu T Mbungu5,6

  • 1Dept. of Sustainable and Renewable Energy Engineering, University of Sharjah, Sharjah, United Arab Emirates. aabokhalil@sharjah.ac.ae.

Scientific Reports
|January 7, 2026
PubMed
Summary

This study introduces an AI-driven adaptive method for islanding detection in photovoltaic systems, significantly reducing the non-detection zone and improving detection speed for enhanced grid stability and safety.

Keywords:
Active frequency drift (AFD)Artificial intelligenceGrid-Connected PV systemsIEEE std. 1547IEEE std. 929Islanding detectionNon-Detection zone (NDZ)

More Related Videos

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

1.0K
A High Performance Impedance-based Platform for Evaporation Rate Detection
06:39

A High Performance Impedance-based Platform for Evaporation Rate Detection

Published on: October 17, 2016

6.8K

Related Experiment Videos

Last Updated: Jan 13, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

2.1K
Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

1.0K
A High Performance Impedance-based Platform for Evaporation Rate Detection
06:39

A High Performance Impedance-based Platform for Evaporation Rate Detection

Published on: October 17, 2016

6.8K

Area of Science:

  • Electrical Engineering
  • Artificial Intelligence
  • Renewable Energy Systems

Background:

  • Islanding detection is crucial for grid-connected photovoltaic (PV) systems to ensure power quality, safety, and stability.
  • Conventional Active Frequency Drift (AFD) methods have limitations, including a large non-detection zone (NDZ) and fixed parameters unsuitable for dynamic grid conditions.
  • A significant research gap exists in adaptive perturbation parameter adjustment for AFD methods in response to real-time grid dynamics.

Purpose of the Study:

  • To develop a novel AI-driven adaptive AFD method for PV systems.
  • To eliminate the NDZ and enhance system stability by dynamically optimizing perturbation parameters.
  • To address the limitations of existing AFD methods in adapting to changing grid and load conditions.

Main Methods:

  • Implemented a Reinforcement Learning (RL) approach to optimize the chopping fraction (Cf) and an enhanced correction factor (Cr').
  • The RL agent was trained using a reward-based strategy for fast and accurate islanding detection.
  • The Cr' was adaptively updated based on the rate of change of frequency (df/dt) and Cf to minimize NDZ.

Main Results:

  • Achieved islanding detection times of 0.12-0.17 s, a significant improvement over standard AFD (0.2-0.5 s).
  • Reduced the NDZ to below 1%, compared to 10-15% for conventional AFD methods.
  • Maintained total harmonic distortion (THD) within ≤ 2%, ensuring high power quality.

Conclusions:

  • The proposed AI-driven adaptive AFD method effectively eliminates the NDZ and enhances detection speed and robustness in PV systems.
  • Experimental validation across various PV configurations confirms the method's scalability and reliability.
  • This technique offers a promising solution for smart grids, ensuring compliance with IEEE Std. 929 islanding detection requirements while maintaining power quality and stability.