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Soft Computing

, Volume 23, Issue 23, pp 12255–12276 | Cite as

A GOA–RNN controller for a stand-alone photovoltaic/wind energy hybrid-fed pumping system

  • A. Ann RufusEmail author
  • L. Kalaivani
Foundations
  • 25 Downloads

Abstract

This paper presents a control scheme for a stand-alone photovoltaic/wind energy hybrid pumping system. The proposed control scheme is the joined execution of grasshopper optimization algorithm and recurrent neural network (GOA–RNN). The objective of the proposed control technique is to satisfy the load power demand and to maintain the power regulation (or) maximum energy conversion of the wind and solar subsystems. In the proposed system, the GOA is utilized to optimizing the combination of the resource parameters based on the solar irradiation and wind power uncertainty. Based on the optimal datasets, the RNN gives the best control signals, i.e., duty cycle. The RNN learning process is enhanced by using the GOA algorithm in perspective of the minimum error objective function. To validate the effectiveness of the proposed approach, the solar irradiation, wind uncertainty and load faults is studied. The proposed method is actualized in MATLAB/Simulink stage and evaluated their performance. To validate the advantage of the proposed approach, three test cases are studied and compared with different existing techniques. In the proposed approach the maximum generated power of PV, Wind and Load power under solar irradiance change condition is 800 W, 350 W and 1100 W. Under wind uncertainty is 810 W, 350 W and 1400 W. Under load fault condition is 900 W, 350 W and 1400 W. The comparison reveals that the proposed technique has the capability for maximizing the energy conversion of wind and PV generation system with less THD. Overall the results demonstrate that technically the stand-alone photovoltaic/wind energy hybrid pumping system is an ideal solution to achieve 97% energy autonomy in remote communities.

Keywords

Stand-alone system Photovoltaic Wind energy Duty cycle Resource parameters Hybrid pumping system 

List of symbols

\( d_{\text{pv}}^{\text{act}} \)

Actual duty cycle of PV system

\( d_{\text{wt}}^{\text{act}} \)

Actual duty cycle of wind system

\( P_{\text{wt}}^{\text{act}} \)

Actual power generation of PV

\( P_{\text{Act}} \)

Actual power of the system

\( d_{\text{pv}} \)

Control signal (duty cycle of the dc–dc converter)

\( I_{\text{pv}} \)

Current generated by the PV array

\( I_{\text{dc}} \)

Current injected on the dc bus

\( V_{\text{c}} \)

Cut-in wind speed

\( V_{\text{F}} \)

Cut-off wind speed

l

Electrical parameter of the converter

\( d_{\text{PV}}^{\hbox{max} } \)

Maximum control signals generation of PV

\( d_{\text{wt}}^{\hbox{max} } \)

Maximum control signals generation of wind

\( P_{\hbox{max} } \)

Maximum power delivered by the resources

\( P_{\text{pv}}^{\hbox{max} } \)

Maximum power generation for PV

\( P_{\text{wt}}^{\hbox{max} } \)

Maximum power generation for wind

\( n_{\text{P}} \)

Number of series strings in parallel

\( P_{\text{pv}} \)

Output power of PV

\( P_{\text{wt}} \)

Output power of wind

\( \chi \)

Pump efficiency

\( P_{\text{r}} \)

Rated electrical power

\( d_{mn} \)

Random solutions of control signals

\( V_{\text{R}} \)

Rated wind speed

h

Speed of pump for a given flow rate

\( P_{\text{target}} \)

Target output value

\( P_{\text{pump}} \)

Total power generation of load

\( V_{\text{pv}} \)

Voltage level on the PV panel array terminals

\( V_{\text{b}} \)

Voltage on the battery bank terminals

\( Q_{\text{flow}} \)

Water flow rate

Abbreviations

AI

Artificial intelligence

EDNS

Expected demand not supplied

GOA

Grasshopper optimization algorithm

HRES

Hybrid renewable energy system

PMSG

Permanent magnet synchronous generator

PV

Photovoltaic

PWM

Pulse width modulation

RNN

Recurrent neural network

VSI

Voltage source inverter

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical and Electronics EngineeringSCAD College of Engineering and TechnologyCheranmahadevi, TirunelveliIndia
  2. 2.Department of Electrical and Electronics EngineeringNational Engineering CollegeThoothukudi DistrictIndia

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