Aiming at the problem of power coupling and complicated decoupling in the d-q coordinate system of a three-phase grid-connected inverter, a current closed-loop control strategy based on an improved QPIR (quasi-proportional integral resonant) controller in the α-β two-phase static coordinate system is proposed. Firstly, the mathematical model of an LCL three-phase grid-connected inverter is established, and its instantaneous power calculation equation is deduced. Secondly, the frequency method is applied to compare and analyze the proportional resonant, quasi-proportional resonant, and improved current controller, and the appropriate improved controller parameters are obtained according to the traditional proportional integral controller parameter design method and the weight coefficient. Finally, the improved controller is compared with the traditional controller in the simulation model of the LCL three-phase grid-connected inverter based on active damping. The results show that the proposed improved current control strategy has good dynamic response characteristics, can realize the non-static error control of grid-connected current, and realizes the decoupling control of active power and reactive power when the load jumps. At the same time, the results also prove the superiority of the proposed control strategy and verify its effectiveness.

To solve the problem of incompatibility and mutual repulsion between a distribution network (DN) and distributed generation (DG), this paper first analyzes the conflicts between current feeder automation (FA) and doubly-fed induction generation (DFIG). Then, a DFIG coordination control strategy compatible with FA is proposed to cause the DFIG operating continuously during fault isolation and power supply recovery. The strategy consists of three aspects: (1) a DFIG islanding active synchronization control technology to support islanding and distant presynchronization control; (2) DFIG senses the status of circuit breakers/tie-switch through generic object oriented substation event (GOOSE) messages to achieve rapid switch of control modes and distant synchronization; (3) tie-switch senses the status of DFIG through GOOSE messages for closing. Finally, the effectiveness of the strategy is validated by multiple sound and detailed time-domain simulation cases. This study indicates future possible development trends and provides a simple and effective strategy for researches in the field of smart grid DN–DG compatibility.

In recent years, the power system transient stability assessment (TSA) based on a data-driven method has been widely studied. However, the topology and modes of operation of power systems may change frequently due to the complex time-varying characteristics of power systems. which makes it difficult for prediction models trained on stationary distributed data to meet the requirements of online applications. When a new working situation scenario causes the prediction model accuracy not to meet the requirements, the model needs to be updated in real-time. With limited storage space, model capacity, and infinite new scenarios to be updated for learning, the model updates must be sustainable and scalable. Therefore, to address this problem, this paper introduces the continual learning Sliced Cramér Preservation (SCP) algorithm to perform update operations on the model. A deep residual shrinkage network (DRSN) is selected as a classifier to construct the TSA model of SCP-DRSN at the same time. With the SCP, the model can be extended and updated just by using the new scenarios data. The updated prediction model not only complements the prediction capability for new scenarios but also retains the prediction ability under old scenarios, which can avoid frequent updates of the model. The test results on a modified New England 10-machine 39-bus system and an IEEE 118-bus system show that the proposed method in this paper can effectively update and extend the prediction model under the condition of using only new scenarios data. The coverage of the updated model for new scenarios is improving.