Abstract
This paper presents an efficient neural network-based method for fault diagnosis in photovoltaic arrays. The proposed method was elaborated on three main steps: the data-feeding step, the fault-modeling step, and the decision step. The first step consists of feeding the real meteorological and electrical data to the neural networks, namely solar irradiance, panel temperature, photovoltaic-current, and photovoltaic-voltage. The second step consists of modeling a healthy mode of operation and five additional faulty operational modes; the modeling process is carried out using two networks of artificial neural networks. From this step, six classes are obtained, where each class corresponds to a predefined model, namely, the faultless scenario and five faulty scenarios. The third step involves the diagnosis decision about the system’s state. Based on the results from the above step, two probabilistic neural networks will classify each generated data according to the six classes. The obtained results show that the developed method can effectively detect different types of faults and classify them. Besides, this method still achieves high performances even in the presence of noises. It provides a diagnosis even in the presence of data injected at reduced real-time, which proves its robustness.
Highlights
In the last few years, there has been a growing interest in developing alternative energies, which are inexhaustible and environment friendly compared to energies derived from fossil deposits
Healthy artificial neural network (ANN) are summarized in Figure 20 below, which shows good training performance
The appropriate neural structure is characterized by the transfer function of a hyperbolic tangent in the first hidden layer and a linear transfer function in the second hidden layer
Summary
In the last few years, there has been a growing interest in developing alternative energies, which are inexhaustible and environment friendly compared to energies derived from fossil deposits (oil, petroleum, and natural gas). Alternate energy encompasses all those renewable resources that do not involve fossil fuels, such as solar, wind, geothermal, hydroelectric, and biomass. Solar energy, both thermal and photovoltaic, shows the greatest growth rate globally. The PV production price dropped significantly during the same period allowing this type of energy to compete freely with alternative sources. With this increased capacity, the fault diagnostic and maintenance of solar PV plants become critical to maintaining the competitiveness of this energy sector [1,2]. The failures detected in a PV system are classified into three categories according to the source of the default (Figure 1): internal, external, and ageing effects [1,3,4]
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