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


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.


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


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


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



Artificial intelligence


Expected demand not supplied


Grasshopper optimization algorithm


Hybrid renewable energy system


Permanent magnet synchronous generator




Pulse width modulation


Recurrent neural network


Voltage source inverter


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.


  1. Ahmadi S, Abdi S (2016) Application of the Hybrid Big Bang-Big Crunch algorithm for optimal sizing of a stand-alone hybrid PV/wind/battery system. Sol Energy 134:366–374. CrossRefGoogle Scholar
  2. Aissou S, Rekioua D, Mezzai N et al (2015) Modeling and control of hybrid photovoltaic wind power system with battery storage. Energy Convers Manag 89:615–625. CrossRefGoogle Scholar
  3. Alnejaili T, Drid S, Mehdi D et al (2015) Dynamic control and advanced load management of a stand-alone hybrid renewable power system for remote housing. Energy Convers Manag 105:377–392. CrossRefGoogle Scholar
  4. Belmili H, Almi M, Bendib B, Bolouma S (2013) A computer program development for sizing stand-alone photovoltaic-wind hybrid systems. Energy Procedia 36:546–557. CrossRefGoogle Scholar
  5. Belmili H, Boulouma S, Boualem B, Fayçal A (2017) Optimized control and sizing of standalone PV-wind energy conversion system. Energy Procedia 107:76–84. CrossRefGoogle Scholar
  6. Bilal B, Sambou V, Kébé C, Ndiaye P, Ndongo M (2012) Methodology to size an optimal stand-alone PV/wind/diesel/battery system minimizing the levelized cost of energy and the CO2 emissions. Energy Procedia 14:1636–1647CrossRefGoogle Scholar
  7. Bilal B, Sambou V, Ndiaye P, Kébé C, Ndongo M (2013) Study of the influence of load profile variation on the optimal sizing of a standalone hybrid PV/wind/battery/diesel system. Energy Procedia 36:1265–1275CrossRefGoogle Scholar
  8. Dali M, Belhadj J, Roboam X (2010) Hybrid solar–wind system with battery storage operating in grid-connected and standalone mode: control and energy management—experimental investigation. Energy 35:2587–2595. CrossRefGoogle Scholar
  9. Dufo-López R, Fernández-Jiménez L, Ramírez-Rosado I et al (2017) Daily operation optimisation of hybrid stand-alone system by model predictive control considering ageing model. Energy Convers Manag 134:167–177. CrossRefGoogle Scholar
  10. Gupta R, Kumar R, Bansal A (2015) BBO-based small autonomous hybrid power system optimization incorporating wind speed and solar radiation forecasting. Renew Sustain Energy Rev 41:1366–1375. CrossRefGoogle Scholar
  11. Hassan W, Kamran F (2018) A hybrid PV/utility powered irrigation water pumping system for rural agricultural areas. Cogent Eng 5:1–15. CrossRefGoogle Scholar
  12. Ismail M, Moghavvemi M, Mahlia T et al (2015) Effective utilization of excess energy in standalone hybrid renewable energy systems for improving comfort ability and reducing cost of energy: a review and analysis. Renew Sustain Energy Rev 42:726–734. CrossRefGoogle Scholar
  13. Jayalakshmi N, Gaonkar D (2017) An integrated control and management approach of stand-alone hybrid wind/PV/battery power generation system with maximum power extraction capability*. Distrib Gener Altern Energy J 32:7–26. CrossRefGoogle Scholar
  14. Jurasz J, Beluco A, Canales F (2018) The impact of complementarity on power supply reliability of small scale hybrid energy systems. Energy 161:37–743. CrossRefGoogle Scholar
  15. Kamel R (2016) New inverter control for balancing standalone micro-grid phase voltages: a review on MG power quality improvement. Renew Sustain Energy Rev 63:520–532. CrossRefGoogle Scholar
  16. Khatib T, Ibrahim I, Mohamed A (2016) A review on sizing methodologies of photovoltaic array and storage battery in a standalone photovoltaic system. Energy Convers Manag 120:430–448. CrossRefGoogle Scholar
  17. Ku C-C, Lee K (1995) Diagonal recurrent neural networks for dynamic systems control. IEEE Trans Neural Netw 6:144–156. CrossRefGoogle Scholar
  18. Ma T, Yang H, Lu L (2014a) A feasibility study of a stand-alone hybrid solar–wind–battery system for a remote island. Appl Energy 121:149–158. CrossRefGoogle Scholar
  19. Ma T, Yang H, Lu L, Peng J (2014b) Technical feasibility study on a standalone hybrid solar-wind system with pumped hydro storage for a remote island in Hong Kong. Renew Energy 69:7–15. CrossRefGoogle Scholar
  20. Ma T, Yang H, Lu L, Peng J (2015) Pumped storage-based standalone photovoltaic power generation system: modeling and techno-economic optimization. Appl Energy 137:649–659. CrossRefGoogle Scholar
  21. Maheri A (2014a) A critical evaluation of deterministic methods in size optimisation of reliable and cost effective standalone hybrid renewable energy systems. Reliab Eng Syst Saf 130:159–174. CrossRefGoogle Scholar
  22. Maheri A (2014b) Multi-objective design optimisation of standalone hybrid wind-PV-diesel systems under uncertainties. Renew Energy 66:650–661. CrossRefGoogle Scholar
  23. Maouedj R, Mammeri A, Draou M, Benyoucef B (2015) Techno-economic analysis of a standalone hybrid photovoltaic-wind system. Application in electrification of a house in Adrar region. Energy Procedia 74:1192–1204. CrossRefGoogle Scholar
  24. Moradi H, Esfahanian M, Abtahi A, Zilouchian A (2018) Optimization and energy management of a standalone hybrid microgrid in the presence of battery storage system. Energy 147:226–238. CrossRefGoogle Scholar
  25. Perera A, Attalage R, Perera K, Dassanayake V (2013) Designing standalone hybrid energy systems minimizing initial investment, life cycle cost and pollutant emission. Energy 54:220–230. CrossRefGoogle Scholar
  26. Rezkallah M, Hamadi A, Chandra A, Singh B (2018) Design and implementation of active power control with improved P&O method for wind-PV-battery-based standalone generation system. IEEE Trans Ind Electron 65:5590–5600. CrossRefGoogle Scholar
  27. Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47. CrossRefGoogle Scholar
  28. Sawle Y, Gupta S, Bohre A (2017) Optimal sizing of standalone PV/Wind/Biomass hybrid energy system using GA and PSO optimization technique. Energy Procedia 117:690–698. CrossRefGoogle Scholar
  29. Semaoui S, Arab A, Bacha S, Azoui B (2013) Optimal sizing of a stand-alone photovoltaic system with energy management in isolated areas. Energy Procedia 36:358–368. CrossRefGoogle Scholar
  30. Slusarewicz J, Cohan D (2018) Assessing solar and wind complementarity in Texas. Renew Wind Water Solar 5:7. CrossRefGoogle Scholar
  31. Syed I (2017) Near-optimal standalone hybrid PV/WE system sizing method. Sol Energy 157:727–734. CrossRefGoogle Scholar
  32. Wang R, Li G, Ming M et al (2017) An efficient multi-objective model and algorithm for sizing a stand-alone hybrid renewable energy system. Energy 141:2288–2299. CrossRefGoogle Scholar
  33. Zhang L, Yi Z, Amari S (2018) Theoretical study of oscillator neurons in recurrent neural networks. IEEE Trans Neural Netw Learn Syst. MathSciNetCrossRefGoogle Scholar
  34. Zhou W, Lou C, Li Z et al (2010) Current status of research on optimum sizing of stand-alone hybrid solar–wind power generation systems. Appl Energy 87:380–389. CrossRefGoogle Scholar

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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