Design and Performance Analysis of Hybrid controller for Self Tuning Filter Based Solar Integrated UPQC

Abstract

Integration of renewable energy sources into the electric distribution system and increase in the usage of power electronics devices leads to the power quality (PQ) issues. This work presents an artificial intelligence hybrid controller for the self tuning filter (STF) based unified power quality conditioner (UPQC) integrated with solar Photo-voltaic (SPV) system. The UPQC comprises a series and a shunt voltage source converter, connected with a dc link. The hybrid controller adapts both the Fuzzy Logic controller (FL-C) and artificial neural network (ANN). The neuro-fuzzy hybrid controller (NFHC) is adapted to maintain DC-Link voltage. The control of proposed UPQC works on p-q theory without using any low pass filters (LPF’s) or phased locked loop (PLL). The prime objectives of the proposed work are minimization of harmonics in current waveforms and power factor improvement, rapid action for DC-link voltage balancing, elimination of sag/swell, distortions in the source voltage, and suitable compensation for unbalanced networks. The performance analysis of the proposed controller was carried out with four different test cases for various combinations of loads and comparative analysis was done with those of existing methods like proportional integral controller (PI-C), sliding mode controller (SM-C), and FL-C. The proposed method shows a superior performance in minimizing THD which is much lower than other methods.