Mounir Bouzguenda, Tarek Selmi, Adel Gastli and Ahmed Masmoudi
The purpose of this paper is to study the problem of the leakage currents in transformerless inverter topologies. It proposes a novel topology and how important the adopted…
Abstract
Purpose
The purpose of this paper is to study the problem of the leakage currents in transformerless inverter topologies. It proposes a novel topology and how important the adopted control strategy on the power quality produced by the inverter.
Design/methodology/approach
The paper presents an investigation of a novel transformerless inverter topology. It adopted a control strategy in which the DC source is disconnected from the inverter when the zero vectors of the control are applied. By using such control strategy, the electrical efficiency of the whole system was improved and the leakage current was significantly reduced.
Findings
The paper provides a solution to minimize the leakage current in transformerless inverter topologies. Besides, the problem of zero-crossing distortions was totally eliminated.
Research limitations/implications
Because of the high conversion ratio of the boost converter, the efficiency of the whole system needs to be enhanced.
Practical implications
The paper includes the experimental results of the proposed topology which are in good match with the simulation results.
Originality/value
This paper identifies a need to study the leakage current phenomena in transformerless inverter topologies.
Details
Keywords
Abdullah Al‐Badi, Adel Gastli and Joseph A. Jervase
The parameters of axial‐field machines are very small compared with the parameters of conventional machines. Different measuring methods are normally used in order to obtain good…
Abstract
Purpose
The parameters of axial‐field machines are very small compared with the parameters of conventional machines. Different measuring methods are normally used in order to obtain good estimates of the machine parameters. These methods are difficult to perform, costly and time consuming. This paper proposes the use of genetic algorithms to predict the self and mutual inductances of a specific type of axial‐field machine, the Torus motor.
Design/methodology/approach
The parameter extraction is reformulated as a search and optimization problem in which the only requirement is a set of values of current versus time and an approximate estimate of the parameters.
Findings
The predicted machine self and mutual inductances are verified by comparing with several measuring methods and excellent agreement is obtained.
Originality/value
Demonstrates that genetic algorithms can predict the self and mutual inductances of the Torus machine automatically with high accuracy.