Control Strategy Of Power Converter Research Articles

Reinforcement Learning-Based Event-Triggered FCS-MPC for Power Converters

This article aims to first focus on an improvement of finite control-set model predictive control strategy for power converters that is based on reinforcement learning event-triggered predictive control architecture with the help of adaptive dynamic programming technique and event-triggered mechanism subject to system uncertainties. Our development, endowed with the merits of reinforcement learning and event-triggered control as well as predictive control solution, is able to alleviate the issues of parametric uncertainties and high switching frequency inherent in the existing scheme, while retaining the merits of the finite control-set model predictive control. Finally, this proposal is experimentally evaluated, where robust performance tests confirm the interest and applicability of the proposed control methodology.

Grid-Forming Controller Based on Virtual Admittance for Power Converters Working in Weak Grids

The high penetration of distributed energy resources, based on power electronics, is giving rise to stability issues in the electrical network, as initially those plants were not required to provide either voltage or frequency support. Due to this, TSOs and DSOs have set new requirements for them to provide grid support functionalities. This paper presents a grid forming control strategy for power converters which is based on a virtual admittance loop. By means of this strategy it is possible to use a conventional current control based inverter, just adding this outer loop. By doing so, the converter is not only able to perform current control, but also frequency and voltage support functionalities. In this work, the proposed control is described and analyzed, checking as well its stability boundaries. After this analysis, the results obtained in a hardware-in-the-loop platform, where faulty scenarios, islanding or black start conditions, will be shown. Finally, the results obtained in an experimental true scale workbench will show its effectiveness for providing such services.

Mitigating Impact of High Power Ramp Rates in Utility Grid Integrated Wind–Solar System Using an RLMAT Adaptive Control Strategy

With the unremitting progress in renewable energy based systems into a sizeable contributor in global energy mix, the need to address challenges associated with their inherent power variability has also risen. The high power-ramp rates experienced during sudden wind gusts and cloud passing are known to impact the security of the power grid, especially in remotely-located wind-solar systems. These challenges are further intensified with sudden connection/disconnection of large local loads in the system, which demand power at a high ramp rate. This work deals with the use of a coordinated control strategy for power converters, to limit the grid power-ramp rate during the aforementioned high power-ramp events (HPREs). The deficit power during HPREs is absorbed by the energy storage system (ESS). The reference currents for ramp rate limiting control are computed using a robust least mean absolute third (RLMAT) adaptive filter. The RLMAT filter further assists in improving the grid currents power quality in presence of nonlinear, unbalanced and poor power factor local loads in the system. The effectiveness of the control strategy is evaluated through both simulation analysis and test results obtained from a laboratory-scale prototype.

Hybrid Control Strategy of MPC and DBC to Achieve a Fixed Frequency and Superior Robustness

In this paper, a hybrid control strategy for power converters, based on improved deadbeat control (DBC) and improved finite control set model predictive control (MPC), is proposed. The presented control strategy employs a switched method to achieve a fixed switching frequency while maintaining a fast transient time. Moreover, the proposal incorporates error correction to achieve superior robustness. A prototype of a single-phase voltage source rectifier is established to verify the performance of the proposal. The comparative results with conventional MPC are given and illustrate the merits of the proposed control method.

Decentralized Control Strategy for PV Based DC Microgrid with Hybrid Storage

Due to the rapid development of power electronics technology and modernization of DC based loads, DC microgrids (DCMG) are becoming most attractive solution in terms of their increased efficiency and high power quality. However they face certain challenges like intermittency of renewable energy sources, limitations in dynamic behaviour of storage systems and various stability issues during islanding. Thus it is mandatory to select the DCMG components properly with a robust control strategy for power converters in order to maintain power balance despite of variation in source and load power in order maintain the system power quality. In this work, a DC bus signalling based decentralized control strategy is proposed for a PV based DCMG, which eliminates the risk associated with DC bus voltage fluctuation. Fuel cell is as an auxiliary power source along with solar PV and storage devices, reduces the possibility of black out and helps to increase system reliability. The proposed control strategy has been validated through MATLAB/Simulink based simulation studies for variable weather condition and load demand on DCMG system.