Method for realizing operating state analysis and fault diagnosis of photovoltaic array based on density-based clustering algorithm

Abstract

The invention relates to a method for realizing operating state analysis and fault diagnosis of a photovoltaic array based on a density-based clustering algorithm. The method comprises the following steps: firstly collecting a plurality of electrical parameters of a maximum power point of a photovoltaic power generation array during daily work so as to obtain an electrical parameter sample combination per day; normalizing the electrical parameter samples so as to obtain a test sample combination; calculating the normalized test sample combination so as to obtain a distance matrix; automatically clustering the test samples by adopting the density-based clustering algorithm so as to obtain a plurality of clusters; respectively calculating the minimum distance between each group of reference data and each cluster based on reference data obtained by a simulation model in advance so as to form a distance vector; and finally, comparing each element in the distance vector with a cutoff distance in the clustering algorithm, and identifying a work type to which each cluster belongs. According to the method disclosed by the invention, accurate fault diagnosis can be directly realized by clustering the daily operation data of the photovoltaic system.

Description

technical field [0001] The invention relates to the technical field of grid-connected photovoltaic power generation system working state analysis and photovoltaic array fault diagnosis, in particular to a method for realizing photovoltaic array working state analysis and fault diagnosis based on a density clustering algorithm. Background technique [0002] The increasing installed capacity of photovoltaic power generation system puts forward a demand for the analysis of the working state of the photovoltaic system and the fault diagnosis of the photovoltaic array. As the core component of the system, the photovoltaic array usually works in a complex outdoor environment and is easily affected by various environmental factors, resulting in various failures such as open circuit, short circuit, hard shadow, hot spot, etc. The occurrence of faults will reduce the power generation efficiency of the power station, and even cause a fire in severe cases. At present, the conventional...

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Problems solved by technology

But there are some shortcomings in these schemes: the infrared image detection method cannot distinguish the state where the temperature difference is not obvious, the accuracy and efficiency of fault detection depend on the level of the detection equipment (infrared thermal imager), the cost is large, and the real-time performance is poor; Based on the time domain reflection analysis method, the photovoltaic array in operation cannot be operated online, it is not real-time, and it has high requirements for the equipment, and the diagnostic accuracy is limited; the multi-sensor fault detection method has many sensors and the detection structure is large. Disadvantages such as difficulty in popularization in large-scale photovoltaic array applications

At present, there is no application of density-based clustering algorithm in the working state analysis and fault diagnosis of photovoltaic arrays in published literature and patents.

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