Modular Neural Network for Fault Detection and Classification in Photovoltaic System

N. Vidhya, P. Rajadurai

doi:https://doi.org/10.59232/DST-V1I1P103

Research Article | Open Access | Download Full Text

Volume 1 | Issue 1 | Year 2022 | Article Id: DST-V1I1P103 DOI: https://doi.org/10.59232/DST-V1I1P103

Modular Neural Network for Fault Detection and Classification in Photovoltaic System

N. Vidhya, P. Rajadurai

Received	Revised	Accepted	Published
22 May 2022	25 Jun 2022	02 Jul 2022	12 Jul 2022

Citation

N. Vidhya, P. Rajadurai. “Modular Neural Network for Fault Detection and Classification in Photovoltaic System.” DS Journal of Digital Science and Technology, vol. 1, no. 1, pp. 17-22, 2022.

Abstract

Photovoltaic (PV) has become an active and rapidly increasing area of academic and industrial development. Photovoltaic cells in concentrated solar power systems convert solar energy directly to electricity, offering them a top contender for next-generation green power generation. Single and multiple faults occur in gridconnected photovoltaic systems. Severe faults such as high impedance faults, open circuit faults, Partial Shading (PS) and low location mismatch. in this paper, a Principal Component Analysis (PCA) based Modular Neural Network is proposed to identify and classify this type of fault. Applied PCA is employed for feature extraction, which reduces dataset sizes and eliminates the potential of singularity. MNN is capable of detecting small fault movements enhancingthedetection of PV models.the proposed method improves the detecting performances by reducing the Missed Detection Rate (MDR) and False Alarm Rate (FAR) in the PV system. the result shows that the PCA-based MNN accurately detect single and multiple errors and the proposed method accurately identifies the fault type with an accuracy of 98.90%. Our proposed method compared with existing methods such as ANN, and KNN.

Keywords

Photovoltaic array, Fault detection, Fault classification, Principal component analysis, Modular neural network.

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