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Outline
In order to correctly identify the operating status of photovoltaic arrays and reduce the missed diagnosis rate of faults, an improved bidirectional long short-term memory network (BiLSTM) fault diagnosis method for photovoltaic arrays is proposed. Firstly, by combining Attention mechanisms to optimize and adjust the attention weight allocation of fault labels, the missed diagnosis rate of photovoltaic array fault diagnosis can be reduced; Secondly, the traditional Harris hawks optimization (HHO) algorithm is further optimized by integrating the best point set, segmented adaptive escape energy mechanism, and golden sine strategy, and the optimized Harris hawks optimization (IHHO) algorithm is used to optimize the hyperparameters of the BiLSTM model. Comparative experiments show that the IHHO-Attention-BiLSTM model achieves a fault diagnosis accuracy of 99.26% for photovoltaic arrays, with a missed diagnosis rate as low as 0.13%.
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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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