Three-Vector-Based Low Complexity Model Predictive Control for Soft Open Point

Soft open point (SOP) can improve the flexibility and reliability of power supplies; thus, they are widely used in distribution network systems. Traditional single-vector model predictive control (SV-MPC) can quickly and flexibly control the power and current at both ports of the SOP. However, SV-MPC can only select one voltage vector in a sampling time, producing large current ripples, and power fluctuations. In order to solve the above problems, this paper proposes a three-vector-based low complexity model predictive control (TV-MPC). In the proposed control method, two effective voltage vectors and one zero voltage vector are selected in a sampling time. For the two-port SOP, methods are given to judge the sectors on both sides and select the voltage vectors. Furthermore, the calculation method of the distribution time is proposed as well. Finally, the effectiveness of the proposed method is verified by steady-state and dynamic-state simulation results compared with the SV-MPC.

An Improved Model Predictive Control Method for Three-Port Soft Open Point

Soft open point (SOP) is a key power electronic device to improve the flexibility and stability of the distribution network and is becoming a research hotspot. The traditional double closed-loop control of SOP has a complex structure, difficult process of parameter design, and poor output power quality. To solve these problems, finite control set model predictive control (FCS-MPC) has been adopted. Since FCS-MPC involves a large amount of calculation and experiences delay in current tracking, an improved FCS-MPC with delay compensation is proposed for three-port SOP in this paper to replace the inner loop current control strategy. Improved model predictive control combines the two-step prediction method based on the voltage vector and vector angle compensation method. The vector method is used to construct a mathematical model and a prediction model of three-port SOP. Based on FCS-MPC, the two-step prediction method that takes voltage as the target is used to compensate for the current delay problem, and the amount of calculation is reduced by converting the control target. Meanwhile, the vector angle compensation method is used to compensate the future reference value. Finally, a simulation model is built in MATLAB/Simulink. The simulation results under steady state and dynamic conditions show that the proposed strategy can effectively improve the current delay and reduce the amount of calculation. Furthermore, it has better current tracking accuracy and faster dynamic response speed.

Fault-tolerant Distribution Network Enabled by Series Soft Open Point

Reliability enhancement is always considered as the top priority of the power distribution network. It is highly expected that the power network faults can be locally addressed without spreading into the entire grid. To fulfill the requirement, this paper develops a smart power-electronics device named series soft open point (SSOP). The proposed SSOP connects two AC terminals and can suppress the surge current caused by a short-circuit fault. In addition, it also performs the grid-forming function during an islanding fault. The principle of SSOP will be elaborated and the effectiveness of this proposal is validated by experimental results.

Fault-tolerant Distribution Network Enabled by Series Soft Open Point

Reliability enhancement is always considered as the top priority of the power distribution network. It is highly expected that the power network faults can be locally addressed without spreading into the entire grid. To fulfill the requirement, this paper develops a smart power-electronics device named series soft open point (SSOP). The proposed SSOP connects two AC terminals and can suppress the surge current caused by a short-circuit fault. In addition, it also performs the grid-forming function during an islanding fault. The principle of SSOP will be elaborated and the effectiveness of this proposal is validated by experimental results.