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The integrated power system of ships exhibits superior advantages in terms of efficiency, power density, and flexible controllability. With the further advancement of power electronics technology and DC supporting equipment, the Medium Voltage Direct Current Integrated Power System (MVDC-IPS) is poised for promising development prospects. However, its complex structure and operational environment also present numerous challenges, such as system modeling and simulation, system stability analysis, energy management, and fault diagnosis. In recent years, the rapid development of new-generation artificial intelligence (AI) technologies, represented by advanced machine learning techniques such as deep learning, reinforcement learning, and transfer learning, has provided novel approaches and methodologies for addressing the aforementioned difficulties in MVDC-IPS and its intelligent development. This paper focuses on the current state of AI applications in the stability assessment of MVDC-IPS and offers a forward-looking perspective on future development directions, aiming to provide theoretical references for promoting the intelligent upgrade of ship integrated power systems.

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Review of the Application Status of Artificial Intelligence Technology in Medium Voltage DC Integrated Power Systems

  • 会议论文

  • 发表日期：

    2026年

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  • Lepei Zhou,
  • Xianhua Tan,
  • Zhen Tian,
  • Xiaoming Zha

摘要

The integrated power system of ships exhibits superior advantages in terms of efficiency, power density, and flexible controllability. With the further advancement of power electronics technology and DC supporting equipment, the Medium Voltage Direct Current Integrated Power System (MVDC-IPS) is poised for promising development prospects. However, its complex structure and operational environment also present numerous challenges, such as system modeling and simulation, system stability analysis, energy management, and fault diagnosis. In recent years, the rapid development of new-generation artificial intelligence (AI) technologies, represented by advanced machine learning techniques such as deep learning, reinforcement learning, and transfer learning, has provided novel approaches and methodologies for addressing the aforementioned difficulties in MVDC-IPS and its intelligent development. This paper focuses on the current state of AI applications in the stability assessment of MVDC-IPS and offers a forward-looking perspective on future development directions, aiming to provide theoretical references for promoting the intelligent upgrade of ship integrated power systems.