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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
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Trajectory Tracking Control of Transformer Inspection Robot Using Distributed Model Predictive Control.

Lai Wei1, Guofei Xiang1, Congjun Ma1

  • 1College of Electrical Engineering, Sichuan University, Chengdu 610065, China.

Sensors (Basel, Switzerland)
|November 25, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a distributed model predictive control (DMPC) for inspection robots in transformers. The DMPC method enhances trajectory tracking accuracy and robustness while simplifying computations for robots in confined spaces.

Keywords:
distributed model predictive controltrajectory tracking controlunderwater robot

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Mechanical Engineering

Background:

  • Tracking inspection robot trajectories within transformer oil presents significant challenges.
  • Existing control methods may lack the precision and efficiency required for confined environments.

Purpose of the Study:

  • To develop an effective motion control strategy for inspection robots operating inside transformers.
  • To enhance trajectory tracking accuracy and robustness in challenging underwater environments.
  • To reduce computational complexity for real-time robot control applications.

Main Methods:

  • Established robot kinematics and dynamics models in transformer oil using Lagrange equations.
  • Applied nonlinear model predictive control (NMPC) combined with distributed control theory.
  • Decoupled robot motion into five independent subsystems for distributed model predictive control (DMPC).

Main Results:

  • The DMPC-based robot motion control system demonstrated high tracking accuracy.
  • The system exhibited significant robustness in simulated transformer oil environments.
  • Reduced computational complexity was achieved compared to traditional control methods.

Conclusions:

  • The proposed DMPC method offers an effective solution for robot motion control in narrow, fluid-filled environments.
  • This approach enhances the feasibility of autonomous inspection tasks within complex machinery like transformers.
  • The DMPC strategy provides a robust and computationally efficient control framework for underwater robotics.