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Load-frequency control01:28

Load-frequency control

239
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...
239
Control of Power Flow01:30

Control of Power Flow

305
There are several methods to control power flow in power systems:
305
Multimachine Stability01:25

Multimachine Stability

218
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:
218
Power Factor Correction01:20

Power Factor Correction

246
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.
246
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

167
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.
167
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

272
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:
272

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Related Experiment Video

Updated: Aug 29, 2025

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

591

Enhanced Harmonics Reactive Power Control Strategy Based on Multilevel Inverter Using ML-FFNN for Dynamic Power Load

Harun Jamil1, Faiza Qayyum2, Naeem Iqbal2

  • 1Department of Electronics Engineering, Jeju National University, Jejusi 63243, Korea.

Sensors (Basel, Switzerland)
|September 9, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an intelligent control scheme using a multi-layer feed forward neural network (ML-FFNN) to improve renewable energy systems. The ML-FFNN effectively suppresses harmonics and compensates reactive power at the point of common coupling.

Keywords:
feed forward neural networkpassive filtersrenewable energy resourcesine pulse width modulationsynchronous reference frame control

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Area of Science:

  • Electrical Engineering
  • Renewable Energy Systems
  • Control Theory

Background:

  • The global shift to renewable energy sources (RES) necessitates improved efficiency and stability at the point of common coupling (PCC).
  • Harmonics, voltage unbalance, and lack of inertia in microgrids cause power quality issues and instability at the PCC.
  • Traditional passive filters and Synchronous Reference Frame (SRF) control methods have limitations in addressing these challenges.

Purpose of the Study:

  • To develop and evaluate an intelligent control scheme for harmonic suppression and reactive power compensation in renewable energy systems.
  • To enhance the stability and power quality at the point of common coupling (PCC).
  • To minimize errors in voltage regulation using a multi-layer feed forward neural network (ML-FFNN).

Main Methods:

  • Implementation of a multi-layer feed forward neural network (ML-FFNN) for intelligent control.
  • Utilizing sine pulse width modulation (SPWM) for multilevel inverter gate signals.
  • Employing Synchronous Reference Frame (SRF) control as a baseline for comparison.
  • Simulations conducted in MATLAB Simulink to validate the proposed method.

Main Results:

  • The proposed ML-FFNN-based control scheme demonstrated superior performance in harmonic suppression and reactive power compensation compared to SRF methods.
  • The intelligent control technique achieved significant reductions in Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE).
  • MATLAB Simulink simulations confirmed the effectiveness and efficiency of the proposed control strategy.

Conclusions:

  • The developed ML-FFNN-based control technique offers a more effective solution for harmonic and reactive power control in renewable energy systems.
  • This intelligent approach enhances power quality and stability at the point of common coupling (PCC).
  • The study highlights the potential of advanced neural network control for future microgrid and renewable energy integration.