Management switching angles real-time prediction by artificial neural network

Abstract

Artificial neural networks (ANNs) is an efficient way for different types of real-world prediction problems. In the past decade, it has given a tremendous surge in a global research activities. ANNs embody much certainty and provide a great deal of promise This paper has present artificial neural network (ANN) technique analysis and prediction for management switching angles real-time. The proposes to be used ANN for prediction and selected obtine angles for implement the timing diagram for mulitlvel inverter circuit. In order to control the fundamental component, ANNs are used to solve the analysis of non-linear equation of the output timing diagram in order to determine the switching angles. Substantially, the number of switching devices are reducing as possible basically for reducing a switching loss in the system, also have been used ANNs technique to optimize a switching angles behavior to reduce total harmonic distortion (THD) at voltage and current output waveform equal THDV 8.05% THDA 5.1%. For the proposed controllers, the performance and results by the ANNs were obtained and compared by using MATLAB software.

The performance of a multilevel multifunctional solar inverter under various control methods

“…The presented system is a multi-functional H bridge-based inverter that addresses many issues encountered in distributed generation systems, such as energy shortage and poor power quality due to non-linear loads. The main advantages of H-bridge cascaded inverters are their upgradeable power rating, low cost, and modular structure [10], [20]- [27]. Apart from tracking point of maximum power and supplying generated electrical power to the utility grid, the proposed transformer-less single stage PV inverter can improve power quality.…”