HVDC Transmission Line Fault Identification: A Learning Based UAV Control Strategy

Abstract

Electricity Transmission plays an imperative role in smooth provision of power to the consumers. High voltage direct current (HVDC) system has a lead over high voltage alternate current (HVAC) system in various aspects. As DC transmissions lines transmit electricity over long distance, it is crucial to find the malfunctioning part of the line in case of faults occurrence. In this work, a decision-making based unmanned aerial vehicle (UAV) control strategy is presented for identifying fault location in HVDC transmission lines. The technique is developed on two layered control systems, i.e., command station (Leader Agent) and UAV agents (Local Agents) control. The Markov decision process (MDP) based reward policy for both agents is defined mathematically and has been implemented in MATLAB to depict their behavior. The resulting policy is optimized through the value iteration algorithm based on reward functions and transition probabilities.

Keywords

• Fault identification
• high voltage DC (HVDC)
• multiagent system (MAS)
• markov decision process (MDP)
• unmanned aerial vehicle (UAV)