Abstract
There are a large number of photovoltaic (PV) arrays in large-scale PV power plants or regional distributed PV power plants, and the output of different arrays fluctuates with the external conditions. The deviation and evolution information of the array output are easily covered by the random fluctuations of the PV output, which makes the fault diagnosis of PV arrays difficult. In this paper, a fault diagnosis method based on the deviation characteristics of the PV array output is proposed. Based on the current of the PV array on the DC (direct current) side, the deviation characteristics of the PV array output under different arrays and time series are analyzed. Then, the deviation function is constructed to evaluate the output deviation of the PV array. Finally, the fault diagnosis of a PV array is realized by using the probabilistic neural network (PNN), and the effectiveness of the proposed method is verified. The main contributions of this paper are to propose the deviation function that can extract the fault characteristics of PV array and the fault diagnosis method just using the array current which can be easily applied in the PV plant.
Highlights
In recent years, the PV industry has developed rapidly as the cost of PV modules has been greatly reduced
Livera et al [10] summarize the disadvantages of infrared-based fault diagnosis methods and the advantages of PV electrical parameters-based methods, such methods require a large number of test equipment, which greatly increases the cost of diagnosis, so it is difficult to be applied in actual PV power plants
This paper focuses on the fault feature extraction of PV array output and combines the fault feature extraction method with probabilistic neural network (PNN) classification algorithm for fault diagnosis
Summary
The PV industry has developed rapidly as the cost of PV modules has been greatly reduced. Through the PV model, Fu et al [15] and Liu et al [16] introduced the indicator of the array current dispersion rate of a combiner box Such type of method can effectively determine the fault type through the deviation analysis, but due to the complex modelling process, the performance differences between modules, and the nonlinear distortion of the PV module output parameters caused by the aging of the PV power plant, the model accuracy is difficult to meet the fault diagnosis requirements. In reference [20] based on variation between measured and estimated power, a statistical approach was introduced to set thresholds that can be used for locating defects in the PV system This kind of fault diagnosis method needs to master the prior knowledge of the distribution characteristics of the analyzed objects, but the prior knowledge is difficult to be obtained in advance.
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