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Optimal utilization of renewable energy sources in MG connected system with integrated converters: an AGONN Approach

  • S. AmirtharajEmail author
  • L. Premalatha
  • D. Gopinath
Article
  • 9 Downloads

Abstract

This paper proposes an efficient converter for the usage of hybrid renewable energy sources and reduced switching loss in the Micro Grid associated frameworks. The work resulted in a DC–DC converter for module integration and maximum power point tracking with an efficient adaptive control scheme. The proposed control scheme is a combined execution of both the adaptive grasshopper optimization algorithm (AGOA) and artificial neural network (ANN) named as AGONN strategy. In the proposed strategy, the AGOA plays out the evaluation technique to set up the correct control signals for the system and develops the control signals database for the offline way subject to the power exchange between source side and the load side. Moreover, to train the ANN system for the online way, the achieved dataset is utilized and it drives the control method in less execution time. Also, the objective function is characterized by the system data subject to equality and inequality constraints. The constraints are the accessibility of the renewable energy sources, power demand and the state of charge of storage elements. Batteries are used as an energy source, to balance out and allow the renewable power system units to continue running at a steady and stable output power. By then, the proposed show is executed in MATLAB/Simulink working platform and the execution is surveyed with the current methods.

Keywords

Hybrid renewable energy sources (HRES) MG system DC–DC converter MPPT Adaptive grasshopper optimization algorithm (AGOA) Artificial neural network (ANN) 

List of symbols

\(C_{0} ,C_{1} ,C_{2}\)

Capacitor

\(L_{1} ,\,L_{2}\)

Inductor

\(V_{c}\)

Average voltage

\(V_{0}\)

Average output load voltage

\(V_{in}\)

Input supply voltage

\(S_{w}\)

Switch

\(D_{1} ,\,D_{2}\)

Diodes

\(I_{L1} ,\,I_{L2}\)

Inductor current

\(I_{in}\)

Average DC input current

\(V_{TG}\)

Voltage transfer gain

\(D\)

Duty ratio

\(R\)

Load resistance

\(I_{D}^{{}}\)

Direct current

\(I_{Q}^{{}}\)

Quadrature current

\(I_{D}^{*}\)

Reference direct current vectors

\(I_{Q}^{*}\)

Reference quadrature current vectors

\(p_{ref}\)

Reference active power values

\(\beta_{ref}\)

Reference reactive power values

\(p_{inv}\)

Active power of inverter

\(\beta_{inv}\)

Reactive power of inverter

\(K_{p} ,\,K_{i}\)

Proportional and integral gain parameters

\(V_{D}^{*}\) and \(V_{Q}^{*}\)

Reference voltage vectors

\(F\)

Output voltage of filters

\(F_{l}\)

Filter value

\(T_{i}\)

Constant time

\(\omega\)

Angular frequency

\(J\)

Objective function value

e

Error signal

\(T_{A}\)

Target solution

\(\gamma_{Gc}\)

Number of gene crossover

\(L_{c}\)

Length of the individuals

\(\lambda_{pt}\)

Mutation point

\(I_{act}\)

Actual quadrature current

\(I_{ref}\)

Reference quadrature current

Notes

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical EngineeringAnna UniversityChennaiIndia
  2. 2.Department of Electrical EngineeringVIT UniversityChennaiIndia
  3. 3.Department of Mechanical EngineeringGKM College of Engineering and TechnologyChennaiIndia

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