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When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
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This study introduces an improved Deep Deterministic Policy Gradient (DDPG) algorithm to enhance spacecraft performance-fault relationship graphs. The novel method accurately predicts relationships, enabling faster fault diagnosis and repair by space robots.

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Area of Science:

  • Artificial Intelligence
  • Robotics
  • Aerospace Engineering

Background:

  • Spacecraft control systems generate complex performance-fault relationship graphs.
  • Efficiently diagnosing and repairing faults is crucial for mission success.
  • Existing methods may lack the speed and accuracy needed for real-time fault analysis.

Purpose of the Study:

  • To develop an advanced method for predicting spacecraft performance-fault relationships.
  • To improve the accuracy and speed of fault localization and repair using space robots.
  • To enhance the Deep Deterministic Policy Gradient (DDPG) algorithm for this specific application.

Main Methods:

  • Constructed a spacecraft performance-fault relationship graph for the control system.
  • Proposed a novel relationship prediction method combining representation learning and deep reinforcement learning.
  • Utilized an improved Deep Deterministic Policy Gradient (DDPG) algorithm with a deep neural network for value and strategy functions.

Main Results:

  • The proposed model demonstrated high prediction speed and accuracy on the spacecraft control system graph.
  • The reinforcement learning agent achieved optimal interaction within the learning environment.
  • The model successfully inferred optimal relationship paths between entities in the graph.

Conclusions:

  • The improved DDPG-based approach significantly enhances spacecraft performance-fault relationship prediction.
  • This method enables space robots to locate and repair spacecraft faults more efficiently.
  • The model's perceptual decision-making and value judgment abilities are key to its success.