Abstract：With the continuous integration of large-scale of renewable energy, a high share of renewable energy is the main feature of the modern power systems. Since the converter controller of renewable generation has nonlinear modules such as current limiting and switching actions, the dynamic characteristics of the induction motor in the near area will be influenced by them. Therefore when there are a large number of renewable generation in the near area of the induction motor, the dynamic interaction between the renewable energy and the induction motor needs to be considered. However, there are few research on the impact of renewable generation on induction motor equivalent modeling presently. To address this issue, this paper presents a new dynamic clustering method of induction motors in power systems with a high share of renewable energy. Firstly, the interaction between induction motor and power grid is analyzed, which shows that the power system interacts with the induction motor through its terminal voltage. Therefore, considering terminal voltage disturbed trajectories can improve the accuracy of equivalent model. The group-average method is used to quantify the similarity of terminal voltage fluctuation, and the similarity distance is applied to group the induction motors. Based on this, the clustering method that both considers the small disturbance dynamic similarity of induction motor and the dynamic similarity of the terminal voltage is proposed. The ultimate clustering result is obtained by dividing the intersection of the clustering results obtained by the two methods into a new group and the rest into a new group. For the power systems with a high share of new energy, it is found that when there are renewable energy near the induction motors in the same cluster, and some of these renewable energy enters into the low voltage ride through (LVRT) during the fault, the dynamic characteristics of the induction motors in the same group will have significant differences. Therefore, a dynamic clustering method considering the voltage drop depth of induction motor is proposed. Therefore, the paper proposes the clustering method for induction motors, which combining of three factors of ① the small disturbance dynamic similarity of induction motor, ② the dynamic similarity of terminal voltage and ③ the clustering results of terminal voltage drop depth. Based on the clustering results obtained by these three methods, the intersection is divided into a new group, and the rest are divided into a new group respectively to obtain the final clustering results. Based on the obtained clustering results of the induction motors, the power network is simplified according to the synchronous generator coherency-based equivalent method. The parameters of induction motor are aggregated by weighted average of each individual motor. Finally, the dynamic equivalence process of induction motors is elaborated by integrating the steps of motor clustering, parameter aggregation and power network simplification. The following conclusions can be drawn from the 10-machine 39 node New England simulation system and the real power grid of Central Tibet: (1) Compared with classical clustering method based on the small disturbance dynamic similarity of induction motor, further considers the dynamic similarity of the terminal voltage can improve the dynamical equivalent accuracy of induction motors. (2)For the power system with high proportion of photovoltaic power, it is found that when there is photovoltaic power near the induction motor in the same group, and some of these photovoltaic power enters the LVRT state, the effects of the renewable energy on the dynamics of induction motors should be included. Therefore, the clustering method by further considers the terminal voltage drop depth can improve the equivalent accuracy of induction motors. (3) The feasibility and adaptability of the modeling method proposed in this paper are illustrated by analyzing the errors of the equivalent models under different disturbances.
潘学萍, 王卫康, 陈海东, 孙晓荣, 郭金鹏. 计及低电压穿越影响的感应电动机动态分群[J]. 电工技术学报, 2024, 39(7): 2001-2016.
Pan Xueping, Wang Weikang, Chen Haidong, Sun Xiaorong, Guo Jinpeng. Study of Dynamic Clustering Method for Induction Motors Considering Low Voltage Ride Through Effects. Transactions of China Electrotechnical Society, 2024, 39(7): 2001-2016.
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