Condition Monitoring and Maintenance Management with Grid-Connected Renewable Energy Systems

Condition Monitoring and Maintenance Management with Grid-Connected Renewable Energy Systems

Článek WoS

The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of action. But with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow, monitoring and maintenance are a few of the prime concerns. These problems were addressed widely in the literature, but most of the research has drawbacks due to long detection time, and high misclassification error. Hence to overcome these drawbacks, and to develop an accurate monitoring approach, this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic (PV) system and highlighted along with a brief overview on existing fault detection methodology. Based on the drawback a data-driven machine learning approach has been used for the identification of fault and indicating the maintenance unit regarding the operation and maintenance requirement. Further, the system was tested with a 4 kWp grid-connected PV system, and a decision tree-based algorithm was developed for the identification of a fault. The results identified 94.7% training accuracy and 14000 observations/sec prediction speed for the trained classifier and improved the reliability of fault detection nature of the grid-connected PV operation.

The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of action. But with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow, monitoring and maintenance are a few of the prime concerns. These problems were addressed widely in the literature, but most of the research has drawbacks due to long detection time, and high misclassification error. Hence to overcome these drawbacks, and to develop an accurate monitoring approach, this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic (PV) system and highlighted along with a brief overview on existing fault detection methodology. Based on the drawback a data-driven machine learning approach has been used for the identification of fault and indicating the maintenance unit regarding the operation and maintenance requirement. Further, the system was tested with a 4 kWp grid-connected PV system, and a decision tree-based algorithm was developed for the identification of a fault. The results identified 94.7% training accuracy and 14000 observations/sec prediction speed for the trained classifier and improved the reliability of fault detection nature of the grid-connected PV operation.

Fault detection; machine learning; fault ride through

Fault detection; machine learning; fault ride through

ALAM, M.; HAQUE, A.; KHAN, M.; SOBAHI, N.; MEHEDI, I.; KHAN, A.

2022

29.03.2022

Tech Science Press

HENDERSON

1546-2218

CMC-Computers Materials & Continua

72

2

Spojené státy americké

3999

4017

19

https://www.techscience.com/cmc/v72n2/47234

http://hdl.handle.net/11012/204172

Condition Monitoring and Maintenance Management with Grid-Connected Renewable Energy Systems