Stacking ensemble based fault diagnosis approach for improved operation of photovoltaic arrays

ABSTRACT

The operation of photovoltaic (PV) systems, like any other system, in fault free environment ensures maximum performance. Hence, accurate and timely fault identification of PV system demands greater importance. Present investigation proposes a stacking ensemble-based PV array fault diagnosis method, which is integrated with automatic feature engineering and selection technique, handling of imbalanced dataset for unbiased classification. The proposed ensemble utilizes decision tree (DT), random forest (RF), extra trees (EXT), extreme gradient boosting machine (XGBoost) as base learners, and light gradient boosting machine (LightGBM) as both base and meta-learner. To validate the proposed technique, a test system of 4.8kW_p capacity has been built using the MATLAB/Simulink environment incorporating one-year real-time irradiance and module temperature data. Irradiance, module temperature, voltage, current, and power are collected as primary raw data which are then concocted using the autofeat python library for automatic feature engineering and selection. Subsequently, the proposed fault diagnosis strategy is built in python 3.8.5 using the engineered and class label balanced dataset, prepared using the synthetic minority over-sampling technique (SMOTE). Results demonstrate promising performance of the proposed ensemble technique with 97.04% accuracy in classifying the different faults in the PV array, which accounts for approximately 1–3% improvement over other individual machine learning (ML) models.

KEYWORDS:

• Automatic feature engineering and selection
• machine learning
• PV array fault diagnosis
• SMOTE
• stacking ensemble

Abbreviations

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Acknowledgments

The authors would like to thank National Institute of Technology Agartala and Ministry of Education (MoE), Government of India for their support for successful completion of the present work.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Credit authorship contribution statement

Dhritiman Adhya: Conceptualization, investigation, software, validation, writing original draft, format analysis, data curation. Soumesh Chatterjee: Conceptualization, methodology, resources. Ajoy Kumar Chakraborty: Visualization, project administration, supervision.

Data availability statement

The authors can share the relevant data on reasonable request.

Author statement/contributions

Dhritiman Adhya: Investigation, conceptualization, software, validation, writing original draft, format analysis, data curation. Soumesh Chatterjee: Conceptualization, methodology, resources. Ajoy Kumar Chakraborty: Visualization, project administration, supervision.

Statement relating to ethics and integrity

The manuscript has not been submitted for publication or published elsewhere.

Additional information

Funding

There are no such funds/grants involved in this research work.