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Related Concept Videos

Control of Power Flow

Control of Power Flow

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

The Power Flow Problem and Solution

The Power Flow Problem and Solution

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...

Fast Decoupled and DC Powerflow

Fast Decoupled and DC Powerflow

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:

Load-frequency control

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...

Maximum Power Flow and Line Loadability

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.

Turbine-Governor Control

Turbine-Governor Control

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...

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Related Experiment Video

Updated: Sep 17, 2025

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

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

Published on: February 14, 2025

A model reference based adaptive controller for power flow management in microgrid systems.

Kruthi Jayaram¹, H A Vidya², J Ramprabhakar²

¹Department of Electrical & Electronics Engineering, BNM Institute of Technology, Bangalore, 560070, India. kruthijayaram11@gmail.com.

Scientific Reports

|July 2, 2025

Summary

This summary is machine-generated.

This study introduces an efficient controller for DC Microgrid systems to optimize power flow from solar and wind sources. Model Reference Adaptive Controller significantly reduces errors compared to PI controllers, ensuring reliable energy delivery.

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Area of Science:

Electrical Engineering
Renewable Energy Systems
Control Systems

Background:

DC Microgrids are crucial for integrating distributed energy resources (DERs).
Efficient power flow management is essential for stability and optimal energy harvesting.
Existing controllers may have limitations in managing hybrid renewable energy systems.

Purpose of the Study:

To develop and analyze an efficient controller for DC Microgrid systems.
To enhance optimum power flow management between DERs.
To regulate constant DC bus voltage using solar, wind, and battery storage.

Main Methods:

Implementation of a Proportional-Integral (PI) controller with real-time laboratory data.
Analysis of demand and supply conditions using hybrid renewable energy systems.
Application and evaluation of a Model Reference Adaptive Controller (MRAC).

Main Results:

The PI controller exhibited error indices around 30%.
The MRAC demonstrated a significant reduction in error indices.
MRAC enhanced control over the grid, meeting customer demands effectively.

Conclusions:

MRAC offers superior performance over PI controllers for DC Microgrids.
Efficient control strategies are vital for maximizing energy harvest from renewable sources.
The developed controller ensures stable voltage and reliable power supply in DC Microgrids.