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Aiming at the disadvantages that numerous insulated gate bipolar transistor (IGBT) switching loss are difficult to accurately measure online in the cascaded energy storage application area, switching loss prediction model is established based on the error back propagation neural network. Firstly, dynamic test system of switching loss is built with cascaded H bridge power module, the massive switching loss data is obtained with changing the direct current bus voltage, alternating current and coolant temperature of power module. 3 main factors including collector-emitter voltage, collector current and device junction temperature are taken as the input of IGBT switching loss prediction model. The particle swarm optimization is used to optimize the initial weight and threshold of prediction model, improving prediction accuracy and accelerating the convergence of learning laws. The optimized performance of this model is compared and analyzed with the prediction model that the initial weight and threshold are given randomly. The results show that the prediction accuracy of the model proposed in this paper is higher. The maximum percentage error for 50 sets of random validation data is 3.3%.
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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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