Simulation and harmonic analysis of hybrid distributed energy generation based microgrid system using intelligent technique

Abstract

Wind and solar photovoltaic (PV) based hybrid renewable energy generation are environmentally friendly and reasonable. This study describes a hybrid distributed energy generations (DEGs)-based microgrid framework where DC-DC converter, three-stage inverter with fuzzy logics control (FLC), and LC filter channel are coupled with the PV and wind energy. In India, wind and PV energies are affordable for hybrid power frameworks since they are ecological well-disposed and broadly accessible. Generally, these sources produce a fluctuating yield voltage that prompts harm to the working grid on a steady inventory. The proposed model of the hybrid-based framework is executed utilizing MATLAB/Simulink. A boost converter being associated with PV cluster is associated with the basic DC bus also a battery is used by a two-way DC to DC converter, and connected into the utility framework by a typical DC to AC inverter. Wind cluster and PV is linked with maximum power point tracking (MPPT) to produce the higher capacity to the grid, and the charging and discharging of the battery energy can be done to adjust the energy between DEGs generation and utility network. In this paper, various cases of harmonic analysis are executed on MATLAB-simulation and feasibility of proposed grid models and FLC-based intelligent control.

Effects of integration of distributed generation on reliability in distribution system

“…Then, change the DG size with high values at each of these chosen buses to determine power loss. The best position is on the bus that has the lowest loss for each DG size, and the ideal DG size corresponds to that generation [24], [25]. The Computational procedure for the Proposed Methodology is shown in Figure 3.…”

Estimation of powerquality in distribution system using fuzzy logic theory

“…However, in practice there are other problems; for example, any parameter of a criterion function that is not itself a membership function can be fuzzy. As in (2) shows that the fuzzy sets characterize alternative options in terms of various criteria. (…”

“…The second last step is to get fuzzy sets. As in ( 9) is to find the relative importance coefficients of criteria and put these in (2). After solving this corresponding equation and get the result as in (10).…”

“…The application of new soft computing theories has recently been used to solve several issues. For the distribution and transmission of electrical networks, there are some concerns related to power quality issues [1], [2]. The distribution and transmission phase of the electrical power system requires voltage management.…”