Expert knowledge based proportional resonant controller for three phase inverter under abnormal grid conditions

ABSTRACT

Integration of photovoltaic (PV) power to the grid is achieved using three-phase inverters with high-quality current waveforms. The new grid codes impose a limit on the total harmonic distortion (THD) value of the inverter’s current waveforms. Due to simple structure and ease of design, proportional integral (PI) controllers are the most widely adopted methods to control three-phase grid-connected inverters; however under abnormal grid conditions, PI controllers may suffer from instability problems. To address the aforementioned problem, this article proposes an expert knowledge-based proportional resonant (PR) control for the integration of PV power to the grid under abnormal grid conditions. The proposed control method adjusts the control parameters based on rules generated from expert knowledge. Initially, parameters of the proposed control methods are optimized using particle swarm optimization (PSO) method. The proposed control is tested with a 100 kW grid-tied three-phase inverter unit under normal and abnormal grid conditions and its performance are compared with PI and fixed gain PR control methods. Moreover, the proposed control is tested experimentally with a scaled down 300 W grid-tied inverter lab prototype module. From the simulation and experimental results, it is verified that the proposed adaptive PR control technique is capable of providing low THD in the injected current under abnormal grid conditions compared with fixed gained PR controllers.

KEYWORDS:

• Three-phase grid-connected inverter
• proportional resonant control
• adaptive control
• unbalanced grid

Acknowledgement

This work was supported by the Research Groups Program funded by Deanship of Scientific Research, Taif University, Ministry of Education, Saudi Arabia, under Grant 1-441-106.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the Research Groups Program funded by Deanship of Scientific Research, Taif University, Ministry of Education, Saudi Arabia, [1-441-106].