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Abnormal stator core temperatures in generators can lead to serious issues such as aging of insulating materials and winding shorts, thereby affecting the overall performance and lifespan of the generator. This study presents a stator core temperature prediction model for turbo generators based on FFCM-MHDA-iTransformer. It leverages an improved Transformer architecture, namely the inverted Transformer (iTransformer) model, which adopts an inverted time-series encoding approach to address the limitations of the standard Transformer in handling multivariate variable correlations. The model employs fused Fourier convolution mixer (FFCM) to enhance and extract local features from time-series data. Furthermore, the model replaces conventional self-attention with multi-head differential attention (MHDA), effectively reducing attention noise and directing the model’s focus towards critical information. After training and validation, the proposed model demonstrates higher prediction accuracy compared to other mainstream prediction models. It facilitates timely detection of potential faults, preventing shutdowns for maintenance, and holds significant application value for ensuring stable operation of turbo generators. This approach effectively enhances the accuracy and practicality of temperature prediction technology.
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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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