@Article{cmc.2022.028340,
AUTHOR = {Nebras M. Sobahi, Ahteshamul Haque, V S Bharath Kurukuru, Md. Mottahir Alam, Asif Irshad Khan},
TITLE = {Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic Systems},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {74},
YEAR = {2023},
NUMBER = {3},
PAGES = {5757--5776},
URL = {http://www.techscience.com/cmc/v74n3/50984},
ISSN = {1546-2226},
ABSTRACT = {Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic (PV)
systems. In light of this requirement, this paper provides a path for evaluating
the operating condition and improving the power output of the PV system
in a grid integrated environment. To achieve this, different types of faults
in grid-connected PV systems (GCPVs) and their impact on the energy loss
associated with the electrical network are analyzed. A data-driven approach
using neural networks (NNs) is proposed to achieve root cause analysis
and localize the fault to the component level in the system. The localized
fault condition is combined with a parallel operation of adaptive neurofuzzy inference units (ANFIUs) to develop a power mismatch-based control
unit (PMCU) for improving the power output of the GCPV. To develop the
proposed framework, a 10-kW single-phase GCPV is simulated for training
the NN-based anomaly detection approach with 14 deviation signals. Further,
the developed algorithm is combined with the PMCU implemented with the
experimental setup of GCPV. The results identified 98.2% training accuracy
and 43000 observations/sec prediction speed for the trained classifier, and
improved power output with reduced voltage and current harmonics for the
grid-connected PV operation.},
DOI = {10.32604/cmc.2022.028340}
}