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

126
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...
126
Multimachine Stability01:25

Multimachine Stability

141
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:
141
Turbine-Governor Control01:17

Turbine-Governor Control

167
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...
167
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

83
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
83
Power Factor Correction01:20

Power Factor Correction

156
The power transmission to a factory involves the transfer of apparent power, a combination of active and reactive power. The power factor measures how effectively electrical power is converted into useful work output. The ratio of the real power (KW) that does the work to the apparent power (KVA) supplied to the circuit.
156
Control of Power Flow01:30

Control of Power Flow

253
There are several methods to control power flow in power systems:
253

You might also read

Related Articles

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

Sort by
Same author

CNN-based compensation of faulty planar phased-array radiation patterns.

Scientific reports·2026
Same author

A nonlinear observer-based control strategy for hybrid energy storage systems to improve voltage disturbance rejection in DC microgrids.

Scientific reports·2025
Same author

Optimized voltage vector selection for dual-star induction motor: Robust predictive direct torque control-based hysteresis-free approach.

ISA transactions·2025
Same author

Seasonal quantile forecasting of solar photovoltaic power using Q-CNN-GRU.

Scientific reports·2025
Same author

Lyapunov-based neural network model predictive control using metaheuristic optimization approach.

Scientific reports·2024
Same author

Optimizing electric vehicle powertrains peak performance with robust predictive direct torque control of induction motors: a practical approach and experimental validation.

Scientific reports·2024

Related Experiment Video

Updated: Jun 6, 2025

Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

2.4K

Enhanced power system stabilizer tuning using marine predator algorithm with comparative analysis and real time

Intissar Hattabi1, Aissa Kheldoun2, Rafik Bradai3

  • 1SET Laboratory, Electrical and Control Department, Faculty of Technology, Blida 1 University, 09000, Blida, Algeria.

Scientific Reports
|November 23, 2024
PubMed
Summary

The Marine Predator Algorithm (MPA) effectively tunes power system stabilizers (PSS) to reduce low-frequency oscillations. MPA-tuned PSS significantly outperforms other methods, enhancing power system stability.

More Related Videos

Optimization, Test and Diagnostics of Miniaturized Hall Thrusters
12:22

Optimization, Test and Diagnostics of Miniaturized Hall Thrusters

Published on: February 16, 2019

8.9K
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

251

Related Experiment Videos

Last Updated: Jun 6, 2025

Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

2.4K
Optimization, Test and Diagnostics of Miniaturized Hall Thrusters
12:22

Optimization, Test and Diagnostics of Miniaturized Hall Thrusters

Published on: February 16, 2019

8.9K
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

251

Area of Science:

  • Electrical Engineering
  • Control Systems
  • Computational Intelligence

Background:

  • Low-frequency oscillations (LFOs) are a significant concern in modern power systems, potentially leading to instability.
  • Power System Stabilizers (PSS) are crucial control devices for mitigating these oscillations.
  • Metaheuristic algorithms offer advanced optimization capabilities for tuning PSS parameters.

Purpose of the Study:

  • To implement and evaluate the Marine Predator Algorithm (MPA) for optimizing Power System Stabilizer (PSS) parameters.
  • To enhance the damping of low-frequency oscillations in various power system test models.
  • To compare the performance of MPA with other leading metaheuristic algorithms.

Main Methods:

  • The Marine Predator Algorithm (MPA) was employed for tuning PSS parameters.
  • Simulations were conducted on Single Machine Infinite Bus (SMIB), WSCC, and New England 10-machine 39-bus power systems.
  • Performance was evaluated against Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and others using various fitness functions.
  • Real-time digital simulation (CU-SLRT Std) and Hardware-in-the-Loop (HIL) implementation validated the results.

Main Results:

  • MPA-optimized PSS demonstrated superior performance in damping low-frequency oscillations compared to other algorithms.
  • Improvements reached up to 98.62% over PSO, 71.79% over WOA, and 78.04% over African Vulture Optimization Algorithm (AVOA).
  • Validation on multiple test systems and through HIL confirmed the robustness and effectiveness of the MPA approach.

Conclusions:

  • The Marine Predator Algorithm (MPA) is a highly effective method for tuning PSS parameters to improve power system stability.
  • MPA offers significant advantages in damping low-frequency oscillations, outperforming established optimization techniques.
  • The study validates MPA's practical applicability through real-time and HIL implementations.