The traditional power system that mainly consists of fossil energy has experienced a dramatic transformation with the large-scale integration of various renewable energies (e.g., wind and solar energies) and power electronic components. The new power system is based on the premise of carrying the internal requirements of achieving carbon peak and carbon neutrality, which aims to construct a low-carbon, safe and controllable, flexible and efficient, intelligent and friendly, open and interactive modern power system.
Due to the inherent strong randomness and uncertainties characteristics of renewable energy, frequent load transfer in a short time will be a common scenario in the future with the increase of the proportion of renewable energy. As a result, the system power flow will be redistributed in a short time, and some lines and equipment tend to be overloaded. Also. the distributed generation mode of renewable energy will inevitably lead to the rapid growth of operation equipment of new power systems.
Therefore, it is necessary to combine digital technology and artificial intelligence to realize efficient, accurate, and timely perception and intelligent decision-making of power grid equipment status, so as to meet the new challenges brought by the operation and maintenance of new power system equipment. Meanwhile, with the continuous expansion of the power system scale and the increasing complexity of the structure, a large amount of alarm information flows into the dispatching center in a short time, which far exceeds the processing capacity of the operators. To adapt to the rapid and accurate identification of faults under various simple and complex accident situations, the power system fault diagnosis system is crucial for decision-making reference.
This Research Topic aims to collate original papers about state perception and fault diagnosis of new power systems, which aims to provide a platform to promote up-to-date research and share promising ideas in the related fields. Review articles describing the state of the art are also welcomed. Potential topics include but are not limited to the following:
• Battery state-of-health (SoH) and state-of-charge (SoC) estimation,
• Life cycle prediction and management,
• Graph neural network,
• Data mining technology,
• Deep learning and machine learning,
• Physical model-based and data-driven fault diagnosis,
• Fault-tolerant control and reconfiguration,
• Artificial intelligence methods for fault detection and isolation,
• Parameters identification,
• Wind/solar output power forecast.
The traditional power system that mainly consists of fossil energy has experienced a dramatic transformation with the large-scale integration of various renewable energies (e.g., wind and solar energies) and power electronic components. The new power system is based on the premise of carrying the internal requirements of achieving carbon peak and carbon neutrality, which aims to construct a low-carbon, safe and controllable, flexible and efficient, intelligent and friendly, open and interactive modern power system.
Due to the inherent strong randomness and uncertainties characteristics of renewable energy, frequent load transfer in a short time will be a common scenario in the future with the increase of the proportion of renewable energy. As a result, the system power flow will be redistributed in a short time, and some lines and equipment tend to be overloaded. Also. the distributed generation mode of renewable energy will inevitably lead to the rapid growth of operation equipment of new power systems.
Therefore, it is necessary to combine digital technology and artificial intelligence to realize efficient, accurate, and timely perception and intelligent decision-making of power grid equipment status, so as to meet the new challenges brought by the operation and maintenance of new power system equipment. Meanwhile, with the continuous expansion of the power system scale and the increasing complexity of the structure, a large amount of alarm information flows into the dispatching center in a short time, which far exceeds the processing capacity of the operators. To adapt to the rapid and accurate identification of faults under various simple and complex accident situations, the power system fault diagnosis system is crucial for decision-making reference.
This Research Topic aims to collate original papers about state perception and fault diagnosis of new power systems, which aims to provide a platform to promote up-to-date research and share promising ideas in the related fields. Review articles describing the state of the art are also welcomed. Potential topics include but are not limited to the following:
• Battery state-of-health (SoH) and state-of-charge (SoC) estimation,
• Life cycle prediction and management,
• Graph neural network,
• Data mining technology,
• Deep learning and machine learning,
• Physical model-based and data-driven fault diagnosis,
• Fault-tolerant control and reconfiguration,
• Artificial intelligence methods for fault detection and isolation,
• Parameters identification,
• Wind/solar output power forecast.
