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Machine Learning for PV System Operational Fault Analysis: Literature Review

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Intelligent Technologies and Applications (INTAP 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1616))

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Abstract

This review paper aims to discover the research gap and assess the feasibility of a holistic approach for photovoltaic (PV) system operational fault analysis using machine learning (ML) methods. The analysis includes the detection and diagnosis of operational faults in a PV system. Even if standard protective devices are installed in PV systems, they fail to clear various faults because of low current during low mismatch levels, high impedance fault, low irradiance, etc. This failure will increase the energy loss and endanger the PV system’s reliability, stability, and security. As a result of the ML method’s ability to handle a non-linear relationship, distinguishing features with similar signatures, and their online application, they are getting attractive in recent years for fault detection and diagnosis (FDD) in PV systems. In this paper, a review of literature on ML-based PV system FDD methods is provided. It is found that considering their simplicity and performance accuracy, Artificial Neural networks such as Multi-layer Perceptron are the most promising approach in finding a central PV system FDD. Besides, the review paper has identified main implementation challenges and provides recommendations for future work.

Supported by NTNU.

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Abbreviations

AC :

Alternative Current

ANN :

Artificial Neural Network

CNN :

Convolutional Neural Network

DA :

Discriminant Analysis

DC :

Direct Current

DL :

Deep Learning

DT :

Decision Tree

DWT :

Discrete Wavelet Transform

EL :

Ensemble Learning

FDD :

Fault Detection and Diagnosis

G :

Irradiance at Array

GCPVS :

Grid Connected PV System

GFDI :

Ground Fault Detection and Interruption

I :

Current

\(I_{MPP}\) :

Current at Maximum Power Point

IGBT :

Insulated Gate Bipolar Transistor

KELM :

Kernel Based Extreme Learning Machine

LSTM :

Long Short Term Memory

MIMO :

Multiple Input Multiple Output

ML :

Machine Learning

MLP :

Multi-layer Perceptron

MPPT :

Maximum Power Point Tracker

OCPD :

Over Current Protection Devices

RBF :

Radial Basis Function

RF :

Random Forest

SAPVS :

Stand Alone PV System

SCADA :

Supervisory Control and Data Acquisition

SOC :

State of Charge

STC :

Standard Test Condition

SVM :

Support Vector Machine

T :

Module Temperature

TL :

Transfer Learning

V :

Voltage

\(V_{MPP}\) :

Voltage at Maximum Power Point

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Acknowledgements

We would like to thank our Electrical Power Engineering department at NTNU for funding this work as part of a PhD. project.

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Authors and Affiliations

  1. Norwegian University of Science and Technology, Trondheim, Norway

    Tarikua Mekashaw Zenebe, Ole-Morten Midtgård, Steve Völler & Ümit Cali

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  1. Tarikua Mekashaw Zenebe
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  2. Ole-Morten Midtgård
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Correspondence to Tarikua Mekashaw Zenebe .

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Editors and Affiliations

  1. University of Agder (UiA), Grimstad, Norway

    Filippo Sanfilippo

  2. University of Agder (UiA), Grimstad, Norway

    Ole-Christoffer Granmo

  3. Norwegian University of Science and Technology (NTNU), Gjøvik, Norway

    Sule Yildirim Yayilgan

  4. The Islamia University of Bahawalpur (IUB), Bahawalpur, Pakistan

    Imran Sarwar Bajwa

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Zenebe, T.M., Midtgård, OM., Völler, S., Cali, Ü. (2022). Machine Learning for PV System Operational Fault Analysis: Literature Review. In: Sanfilippo, F., Granmo, OC., Yayilgan, S.Y., Bajwa, I.S. (eds) Intelligent Technologies and Applications. INTAP 2021. Communications in Computer and Information Science, vol 1616. Springer, Cham. https://doi.org/10.1007/978-3-031-10525-8_27

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  • DOI: https://doi.org/10.1007/978-3-031-10525-8_27

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