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A small-signal stability preventive control method based on convolutional neural network (CNN) sensitivity analysis is presented in the paper, to improve the developing speed of small- signal stability preventive control measures. For poor or negative damping low frequency oscillation modes (i.e., the damping ratios are smaller than a threshold), first, an optimization model with small- signal stability constraints is established; second, the sensitivities of the damping ratios with respect to control variables (the active power of adjustable generators) based on CNN model of damping ratio prediction are calculated and then the optimization model is transformed into a quadratic programming model by linearizing small-signal stability constraints through sensitivities; finally, the adjustment amounts of generator active power are obtained. Several iterations are needed to make the damping ratios meet specific requirements. Analysis results of WEPRI 36-node case show that the effective control measures can be obtained by the presented method, which is more precise than that of the support vector machine method. The computing speed of the presented method is faster than that of the traditional eigenvalue analysis method. The ideas presented in this paper can also be applied to transient stability preventive control.
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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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