The reactive power output of new energy and the recovery rate of grid voltage will affect the voltage characteristics of other nodes in the system. Traditional parameter determination methods for a single station are difficult to ensure the minimum overall voltage deviation of important nodes in the system. To solve the issue, an optimization method for new energy low voltage ride through (LVRT) control parameters considering the reactive power and voltage interaction is proposed in this paper. Firstly, the impact of LVRT control parameters and reactive power interaction on the overall voltage support capability is analyzed. Then, an index for the coupling degree of reactive power and voltage between nodes is proposed, based on this index as well as the degree of voltage drop at the new energy grid connection point, the value range of the reactive power current proportion coefficients of LVRT in different stations are determined. Finally, within the value ranges, with the goal of minimizing the overall voltage deviation of important nodes in the system, a deep reinforcement learning algorithm is used to coordinate and optimize the above parameters. The results of case studies show that the proposed method can improve the overall voltage recovery characteristics of the system compared to traditional methods, and the pre determination of the control parameter value range can enhance the convergence speed of the algorithm reward function.
| 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 |