Deployment of AI-based RBF network for photovoltaics fault detection procedure

Muhammad Hussain; Mahmoud Dhimish; Violeta Holmes; Peter Mather; Muhammad Hussain; Mahmoud Dhimish; Violeta Holmes; Peter Mather

doi:10.3934/ElectrEng.2020.1.1

AIMS Electronics and Electrical Engineering

2020, Volume 4, Issue 1: 1-18. doi: 10.3934/ElectrEng.2020.1.1

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Research article Special Issues

Deployment of AI-based RBF network for photovoltaics fault detection procedure

Department of Engineering and Technology, Laboratory of Photovoltaics, University of Huddersfield, Huddersfield, HD1 3DH, United Kingdom

Received: 09 October 2019 Accepted: 15 November 2019 Published: 26 November 2019

In this paper, a fault detection algorithm for photovoltaic systems based on artificial neural networks (ANN) is proposed. Although, a rich amount of research is available in the field of PV fault detection using ANN, this paper presents a novel methodology based on only two inputs for the training, validating and testing of the Radial Basis Function (RBF) network achieving unprecedented detection accuracy of 98.1%. The proposed methodology goes beyond data normalisation and implements a ‘mapping of inputs’ approach to the data set before exposing it to the network for training. The accuracy of the proposed network is further endorsed through testing of the network in partial shading and overcast conditions.
- renewable energy,
- photovoltaics,
- fault detection,
- artificial intelligence,
- RBF network
Citation: Muhammad Hussain, Mahmoud Dhimish, Violeta Holmes, Peter Mather. Deployment of AI-based RBF network for photovoltaics fault detection procedure[J]. AIMS Electronics and Electrical Engineering, 2020, 4(1): 1-18. doi: 10.3934/ElectrEng.2020.1.1

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Abstract

In this paper, a fault detection algorithm for photovoltaic systems based on artificial neural networks (ANN) is proposed. Although, a rich amount of research is available in the field of PV fault detection using ANN, this paper presents a novel methodology based on only two inputs for the training, validating and testing of the Radial Basis Function (RBF) network achieving unprecedented detection accuracy of 98.1%. The proposed methodology goes beyond data normalisation and implements a ‘mapping of inputs’ approach to the data set before exposing it to the network for training. The accuracy of the proposed network is further endorsed through testing of the network in partial shading and overcast conditions.

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