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Aimed at the problem that the accuracy of photovoltaic array fault diagnosis based on support vector machine (SVM) is not high and it is easily affected by the kernel function and penalty factor parameters, a photovoltaic array fault diagnosis method based on SVM optimized by the seagull optimization algorithm (SOA) is proposed. The SOA is introduced to optimize the parameters of the SVM model, and an SOA-SVM fault diagnosis model based on the optimal parameters is established. MATLAB software is used to build a photovoltaic array simulation model, and the characteristic parameters under different fault types are extracted and further inputted into the SOA-SVM model for fault diagnosis. Experimental results show that the fault diagnosis accuracy of the SVM model optimized by SOA is significantly improved. Compared with the ABC-SVM and PSO-SVM models, the SOA-SVM model converges faster in the optimization process and has a higher fault diagnosis accuracy.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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