Power quality improvement in distributed generation system using intelligent control methods

Hybrid electric power generation and its integration with the grid to supply consumer demand is main focus of this paper. The use of nonlinear load and advanced power electronic equipment-based devices at the consumer end introduces power quality issues in the power system network in terms of voltage and current. This paper explains the design of a control algorithm for a unified power quality conditioner used for mitigating both voltage and current-based power quality issues. The dynamic voltage restorer of unified power quality controller (UPQC) is designed with a unit vector control algorithm and the distribution static synchronous compensator (DSTATCOM) is designed with fuzzy logic and adaptive network-based fuzzy inference system based instantaneous reactive power theory control algorithm. The simulation model built using the MATLAB platform includes a three-phase voltage source along with hybrid electric power generation connected to linear and non-linear loads operating under different conditions. The result is analyzed in terms of voltage and current total harmonic distortion compared with IEEE 512 power quality standards and power factor improvement. The paper shows the adaptive neuro-fuzzy inference systems (ANFIS)-based control algorithm gives better results in terms of total harmonic distortion (THD) compared to the fuzzy-based control algorithm. The power factor is improved using ANFIS-based controller proving the efficiency of the controller