Greenland fault detection in smart grid

Why is deep learning important for smart grid self-healing & fault mitigation?

Effective fault detection, classification, and localization are vital for smart grid self-healing and fault mitigation. Deep learning has the capability to autonomously extract fault characteristics and discern fault categories from the three-phase raw of voltage and current signals.

Is autonomous smart grid fault detection possible?

A case study is introduced as a preliminary study for autonomous smart grid fault detection. In addition, we highlight relevant directions for future research. Smart grid plays a crucial role for the smart society and the upcoming carbon neutral society.

Can deep neural networks improve smart grid reliability?

A study introduced a deep neural network (ANN and CNN)-based approach for detecting and isolating faults in microgrids without the need to shut down the entire system. These algorithms have the potential to improve the reliability and efficiency of smart grids [28, 29, 30].

Why is localization and classification important in smart grid?

Localization, classification, and fault detection are essential for addressing any problems immediately and resuming the smart grid as soon as possible. Simultaneously, the capacity to swiftly identify smart grid issues utilizing sensor data and easily accessible frequency and voltage data from PMU devices is a prerequisite of this task.

What are fault detection and classification techniques?

Fault detection, classification, and location techniques can be either data-driven or model-driven, as noted in [16, 17]. Data-driven techniques use data mining and machine learning algorithms to analyze large amounts of data to detect and classify faults.

What is transmission line fault detection & classification?

Transmission line (TL) fault detection and classification are crucial to fault root cause analysis and quick power grid recovery . Fault analysis has benefited greatly from the use of deep learning due to its ability to automatically extract representative features from large datasets .