.png){kind=icon}
.png){kind=icon}
Return
Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects
.png){kind=icon}
PRE
AI
DOI：10.1016/j.rser.2023.114088.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Predictive maintenance is an essential aspect of microgrid operations as it enables identifying potential equipment failures in advance, reducing downtime, and increasing the overall efficiency of the system. Machine learning-based techniques have a great potential to be effective in improving the accuracy of failure predictions, detecting, and diagnosing faults in real-time, and monitoring the health and remaining useful life of microgrid components. The integration of these techniques with microgrid components can lead to reduced downtime, improved safety, overall efficiency, and sustainability. This work aims to explore the research scope of machine learning-based predictive maintenance in microgrid systems. The analysis provides a comprehensive review of the state-of-the-art machine learning techniques that could be used for microgrid predictive maintenance and highlights the gaps and challenges that need to be addressed. This study suggests future research directions in the field and frameworks to improve predictive maintenance using machine learning for microgrid industries.
Keywords:
Microgrid (MG)
Predictive maintenance (PdM)
Machine learning (ML)
Fault detection
Microgrid failure prediction
Journal
IF:
16.3
Papers:
1.6W
Citations:
18.5W