A Z-source Inverter Based Flexible DG System with P+resonance and Repetitive Controllers for Power Quality Improvement of a Weak Grid

In the present power systems, nonlinear loads connected at distribution level have increased immensely. This results in reduction of voltage quality for distribution level customers. Significantly, this is an outcome of the concerted action of small loads drawing nonlinear currents. However, the utility has an obligation to deliver a quality supply to consumers. Interestingly, with the increase in energy demand and penetration of renewable sources, generation units popularly known as distributed generators (DGs) are increasingly connected at the distribution level. Most of these sources are connected to the grid using power electronic converters. The power delivered from these sources depend on many factors like energy availability and load demand etc. In many occasions, the converters used in power conversion can be left with some excess capacity. This could be used to provide some ancillary functions of the power distribution like harmonics and unbalance mitigation etc. Moreover, some of these DG sources have large operating ranges demanding special converters with wide operating range. Being a single stage buck-boost inverter, recently proposed Z-source inverter is a good candidate for future DG systems. Considering these factors, this paper presents the controller design for a Z-source inverter based DG system to improve the power quality of distribution systems. To improve the reference tracking and to eliminate harmonics, a p+resonance and repetitive controller designed using a simple time delay is employed. When the system is running at full capacity, the proposed controller improves the quality of the injecting current. Subsequently, the duality of this internal model based control structure is exploited to improve the voltage quality at the connection point of the inverter when the system is not operated in the full power capacity. Proposed control method is tested with simulation results obtained using Matlab/Simulink and PLECS. Subsequently, it is experimentally validated using a laboratory prototype.