Abstract
Most of the photo voltaic (PV) arrays often work in harsh outdoor environment, and undergo various faults, such as local material aging, shading, open circuit, short circuit and so on. The generation of these faults will reduce the power generation efficiency, and when a fault occurs in a PV model, the PV model and the systems connected to it are also damaged. In this paper, an on-line distributed monitoring system based on XBee wireless sensors network is designed to monitor the output current, voltage and irradiation of each PV module, and the temperature and the irradiation of the environment. A simulation PV module model is established, based on which some common faults are simulated and fault training samples are obtained. Finally, a memetic algorithm optimized Back Propagation ANN fault diagnosis model is built and trained by the fault samples data. Experiment result shows that the system can detect the common faults of PV array with high accuracy.
Highlights
Due to the increasing depletion of fossil energy resources and the increasing environmental pollution, many countries in the world are actively seeking alternative renewable clean energy [1]
Most of the photo voltaic (PV) arrays often work in harsh outdoor environment, and undergo various faults, such as local material aging, shading, open circuit, short circuit and so on. The generation of these faults will reduce the power generation efficiency, and when a fault occurs in a PV model, the PV model and the systems connected to it are damaged
Experiment result shows that the system can detect the common faults of PV array with high accuracy
Summary
Due to the increasing depletion of fossil energy resources and the increasing environmental pollution, many countries in the world are actively seeking alternative renewable clean energy [1]. In order to distinguish three states of the components, including normal, short and abnormal aging, it use the open circuit voltage of photovoltaic module, short-circuit current, maximum power point voltage current as the inputs of the trained neural network. In these methods, the number of feature variables used in fault diagnosis is limited, and the characteristic variables are difficult to measure in practice. With the continuous progress of low-cost wireless sensor technology, you can install a wireless sensor on each PV module In this subject, we use algorithm based on distributed on-line monitoring of photo voltaic array of XBee wireless sensor network and Artificial neural network algorithm based on memetic algorithm optimization to study fault diagnosis. The formatter will need to create these components, incorporating the applicable criteria that follow
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