An Artificial Intelligence-Based Fault Detection Method for Solar Panels
A solar panel and fault detection technology, which is applied in the monitoring of photovoltaic systems, electrical components, photovoltaic power generation, etc., can solve problems such as high cost, low efficiency, and inability to accurately determine the type of fault, so as to achieve targeted and improve detection efficiency effect
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Embodiment 1
[0036] The artificial intelligence-based solar battery panel fault detection method provided by the present invention is aimed at a specific scenario: the set solar panel array, when generating power at maximum power, continuously performs Adjustment of altitude angle and azimuth angle; however, due to the environment and the use time of the power generation system, there may be a rotation fault during the rotation of the solar panels, so that there is mutual shading between the solar panels, and the maximum power generation cannot be obtained more effectively .
[0037] In order to realize the fault detection of the solar cell panel array, specifically, an artificial intelligence-based solar cell panel fault detection method provided in this embodiment, such as figure 1 As shown, the method includes the following steps:
[0038] Step 1, obtaining a solar panel array image, and preprocessing the solar panel array image to obtain grayscale image information;
[0039] Specific...
Embodiment 2
[0083] In this embodiment, on the basis of the fault of the solar cell panel judged in the first embodiment, the power generation of the solar cell panel is adjusted to minimize the impact of the rotation fault on the power generation and maximize the power generation.
[0084] Specifically, the solar panel array is adjusted according to the battery panel failure type and the affected related area obtained in the first embodiment, and the specific process is as follows:
[0085] 1) Obtain the data of the shadow area, shadow shape, initial adjustment angle and real-time solar panel array power of each solar panel, and form sequence data of all solar panel data, where the sequence data includes test set and training set;
[0086] Construct the regulation network model; use the training set to train the regulation network model, and obtain the trained regulation network model;
[0087] In this embodiment, the input to the network is performed after the sequence data is self-encod...
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