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.
Highlights
The introduction or rather re-emergence of Artificial Intelligence (AI) has garnered interest from almost every industry
A rich amount of research is available in the field of PV fault detection using artificial neural networks (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 RBF network was tested in partial shading and overcast conditions, with the focus on how effectively the network would be able to adapt to the diverse conditions
Summary
The introduction or rather re-emergence of AI has garnered interest from almost every industry. Researchers in the field of PV systems are looking at non-conventional methods for accurate monitoring, fault detection and isolation of components in PV installations. A distributed sensor network (DSN) would be required for the monitoring of a PV installation. The hardware cost associated with the monitoring of the above parameters through a DSN can deter enterprises from implementing fault detection in their PV systems. This barrier has been eliminated by the introduction of „smart meters‟ which can provide all of the key parameters in one platform. In the context of ANN, this advancement does not have any impact on the efficiency of the network. The reason for this is because the accuracy of an ANN network depends on the „quality‟ of data processing which takes places before exposing the network to the data set
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