Power Quality Improvement using Dual Multilevel Converter for Micro Grid-Connected PV Energy Systems using ANFIS

Abstract

This paper presents the implementation of dual voltage source inverter (DVSI) approach to improve the microgrid performance by enhancing the power quality. This paper also improves the power quality in photovoltaic (PV) generation interactive microgrids, respectively. The power generated from PV based distributive energy resources (DER) is perfectly applied to the microgrid through the two inverters, thus the nonlinear and unbalance load related problems are compensated. Thus, the power quality problems such as voltage sag, current drops, and power factor, active, and reactive powers are reduced by dual multilevel converter (DMLC). This converter also used for cooperative controlling to minimize the unbalances and voltage harmonics in microgrid by using adaptive neuro fuzzy interface system (ANFIS) based controller, which is applied in the shunt and series manner between the two VSIs. The total load current is mutually shared among the series and shunt VSI, if one VSI fails, then the other VSI continues its operation. Thus, the DMLC components failure rate is reduced, and the system lost energy is also reduced, which leads to improved reliability by maintain the reduction of down time price at environmental free conditions. The proposed control model is implemented in MATLAB/Simulink environment, and the obtained results shows that the superiority of total harmonic distortion (THD) reduction by the proposed ANFIS controller as compared to the conventional artificial neural network (ANN) and fuzzy logic controllers (FLC) by eliminating all the load current and grid voltage-based harmonics.

Fuzzy Control Systems for Power Quality Improvement—A Systematic Review Exploring Their Efficacy and Efficiency

“…A type-2 fuzzy controller has the same structure as a type-1 fuzzy controller, plus a type-reduction block positioned before the defuzzification component [55]. To increase the performance of the control process, authors use evolutionary algorithms such as genetic algorithms [33,34] and machine learning algorithms like artificial neuronal networks, which lead to ANFIS [30].…”

“…Later, more researchers presented FLCs in the literature dedicated to controlling PQ using filters and specialized devices [8][9][10]. Table 1 shows a historical sequence of 17 FLCs proposed for PQ improvement in the literature [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. A review of the works published in the 1990s reveals that FLC research was initially focused on reactive power compensation, power factor rise, and voltage and frequency regulation, i.e., power system stability [31].…”