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Related Concept Videos

Second Order systems I01:20

Second Order systems I

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A servo system exemplifies a second-order system, featuring a proportional controller and load elements that ensure the output position aligns with the input position. The relationship between these components is described by a second-order differential equation. Applying the Laplace transform under zero initial conditions yields the transfer function, showing how inputs are converted to outputs in the system.
By reinterpreting the system, one can derive the closed-loop transfer function, which...
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Second Order systems II01:18

Second Order systems II

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In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
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Multi-input and Multi-variable systems01:22

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Consider a turbine operating under steady-flow conditions. The control volume is drawn around the turbine, with fluid entering at one point and exiting at another. The turbine extracts energy from the fluid, which performs mechanical work (shaft work).
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Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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Virtual inertia regulation based high-order observer for weak microgrids using deep sequential learning.

Shuguang Li1, Shupeng Song1, Mai The Vu2

  • 1School of Computer Science and Technology, Shandong Technology and Business University, Yantai 264005, China.

ISA Transactions
|October 12, 2025
PubMed
Summary
This summary is machine-generated.

This study enhances low-inertia hybrid microgrids with sustainable energy resources using an adaptive virtual frequency controller. The novel approach improves system inertia and damping, outperforming conventional methods in dynamic stability.

Keywords:
Energy storage systems (ESS)High-order extended disturbance observer (HOEDO)Hybrid micro-grid systems (HMGSs)Sequential action-value learning (SAVL)Sustainable energy resources (SERs)Virtual frequency control (VFC)

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Area of Science:

  • Electrical Engineering
  • Control Systems
  • Renewable Energy Integration

Background:

  • High penetration of Sustainable Energy Resources (SERs) in Hybrid Micro-grid Systems (HMGS) reduces inertia due to power electronic interfaces.
  • Low inertia in HMGS leads to frequency instability and challenges in grid management.

Purpose of the Study:

  • To introduce an adaptive virtual frequency controller (VFC) for Energy Storage Systems (ESSs) in low-inertia HMGS.
  • To enhance the dynamic stability and responsiveness of HMGS with high SER penetration.

Main Methods:

  • Implementation of a high-order extended disturbance observer (HOEDO) for disturbance estimation and compensation.
  • Application of deep sequential action-value learning (SAVL) for unsupervised controller parameter adaptation.
  • Emulation of virtual rotor dynamics in control loops for improved responsiveness.

Main Results:

  • The adaptive VFC effectively improves system inertia and damping characteristics.
  • The proposed scheme demonstrates superior performance compared to conventional virtual inertia control methods.
  • The controller exhibits faster dynamic response and suppressed fluctuations under varying conditions.

Conclusions:

  • The adaptive VFC, utilizing HOEDO and deep SAVL, offers a robust solution for stabilizing low-inertia HMGS.
  • This approach enhances grid resilience and facilitates higher integration of SERs.
  • The method provides a significant advancement over existing control strategies for microgrid stability.