Open Access
ARTICLE
Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic Systems
Nebras M. Sobahi1,*, Ahteshamul Haque2, V S Bharath Kurukuru2, Md. Mottahir Alam1, Asif Irshad Khan3
1 Department of Electrical and Computer Engineering (ECE), King Abdulaziz University, Jeddah 21589, Saudi Arabia
2 Advance Power Electronics Research Lab, Department of Electrical Engineering, Jamia Millia Islamia, New Delhi, India
3 Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah
21589, Saudi Arabia
* Corresponding Author: Nebras M. Sobahi. Email:
Computers, Materials & Continua 2023, 74(3), 5757-5776. https://doi.org/10.32604/cmc.2022.028340
Received 08 February 2022; Accepted 31 May 2022; Issue published 28 December 2022
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.
Keywords
Cite This Article
N. M. Sobahi, A. Haque, V. S. B. Kurukuru, M. M. Alam and A. I. Khan, "Data-driven approach for condition monitoring and improving power output of photovoltaic systems,"
Computers, Materials & Continua, vol. 74, no.3, pp. 5757–5776, 2023. https://doi.org/10.32604/cmc.2022.028340