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

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Software deployment Computer science Artificial neural network Photovoltaic system Fault detection and isolation Field (mathematics) Data mining Fault (geology) Artificial intelligence Set (abstract data type) Radial basis function Machine learning Engineering Mathematics Actuator Pure mathematics Programming language Operating system Electrical engineering Seismology Geology

Muhammad M. Hussain , Mahmoud Dhimish , Violeta Holmes , Peter Mather ·

YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.3934/electreng.2020.1.001 · OA: W4230774624

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.