Skip to main content

Advertisement

SpringerLink
Log in

Hybrid k-means Grasshopper Optimization Algorithm based FOPID controller with feed forward DC–DC converter for solar-wind generating system

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

The main issue in renewable energy generating system is the variation in power generation due to the intermittent nature of the renewable sources. Due to this, voltage deviation and unexpected surges in the output voltage may affect the power system and cause unstable operation. Therefore, a power flow control of solar-wind Renewable Energy System (RES) is presented in this paper to reduce the power fluctuation and at the same time to maintain the State of Charge (SOC) of the battery within allowable limits. Initially, Maximum Power Point Tracking (MPPT) algorithm is used to operate the power system close to the peak power point. Then, the buck–boost converter with Feed Forward (FF) technique is employed to produce the response with lesser ripples. The Fractional Order Proportional Integral Derivative (FOPID) controller has the characteristics of having short rise time, reduced oscillations, or overshoot with strong robustness compared with the conventional PID controller. However, improper tuning of the controller parameters may degrade the performance of the system. Hence, we introduce a k-means Grasshopper Optimization Algorithm (k-GOA) to determine the best tuning parameters with faster convergence. In order to validate the supremacy of this technique, its performance is compared with the existing controllers like Jaya Optimization (JO) FOPID, Salp Swarm Optimization (SSO) FOPID, Chaotic Atom Search Optimization (ChASO) FOPID, Ant Lion Optimization (ALO) FOPID and Fibonacci Search Technique (FST) FOPID controllers. The outcomes show that despite sudden load changes and changes in the power generation, the power balance between the supply and demand is effectively managed by the proposed k-GOA FOPID controller. Moreover, the k-GOA has obtained faster convergence than existing optimization techniques and the converter have produced less ripples. Moreover, the proposed system has attained an efficiency of 98.5% whereas, the existing systems has obtained lower efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26

Similar content being viewed by others

Abbreviations

\( {\text{I}}_{{{\text{ph}}}}\) :

Photocurrent of PV cell (A)

\( {\text{I}}_{{0}}\) :

Saturation current of the equivalent diode of PV cell (A)

\({\text{I}}\) :

Output current of PV module (A)

\({\text{V}}\) :

Output voltage of PV module (V)

\( {\text{R}}_{{\text{s}}}\) :

Series resistance of the PV cell (Ω)

\( {\text{N}}_{{\text{s}}}\) :

Number of PV cell in series

\({\text{n}}\) :

Ideality factor of PV cell

K:

Boltzmann constant (1.38 × 10–23 J/K)

T:

Module temperature (K)

Q:

Electron charge (1.6 × 10–19 C)

\({\text{V}}\left( {{\text{q}} + 1} \right)\) :

Next output voltage of PV module (V)

\(\Delta {\text{V}}\) :

Step size of P&O algorithm (V)

\(\Delta {\text{P}}\) :

Oscillating power (W)

\(\Delta {\text{V}}\left( {\text{q}} \right)\) :

Current step size of P&O algorithm (V)

\({\text{P}}\left( {\text{q}} \right)\) :

Current output power of PV module (W)

\({\text{P}}\left( {{\text{q}} - 1} \right)\) :

Previous output power of PV module (W)

\({\text{V}}\left( {\text{q}} \right)\) :

Current output voltage of PV module (V)

\({\text{V}}\left( {{\text{q}} - 1} \right)\) :

Previous output voltage of PV module (V)

\({\text{I}}\left( {\text{q}} \right)\) :

Current ouput current of PV module (A)

\({\text{I}}\left( {{\text{q}} - 1} \right)\) :

Previous output current of PV module (A)

\( {\text{I}}_{{{\text{ph}},{\text{f}}}}\) :

Photocurrent at standard test conditions (A)

\( {\text{I}}_{{0,{\text{f}}}}\) :

Saturation current of the equivalent diode at standard test conditions (A)

\( {\text{I}}_{{{\text{sc}},{\text{f}}}}\) :

Reference short circuit current (A)

\( {\text{R}}_{{{\text{s}},{\text{f}}}}\) :

Reference series resistance of the cell (Ω)

\( {\text{R}}_{{{\text{sh}},{\text{f}}}}\) :

Reference shunt resistance of the cell Ω)

\( {\text{n}}_{{\text{f}}}\) :

Ideality factor at standard test conditions

\( {\text{V}}_{{{\text{oc}},{\text{f}}}}\) :

Open circuit voltage at standard test conditions (V)

\( {\text{N}}_{{\text{f}}}\) :

Ideality factor at standard test conditions

\( {\text{V}}_{{\max {\text{f}}}}\), \( {\text{I}}_{{\max {\text{f}}}}\) :

Maximum reference voltage and current

References

  • Abdulkhader HK, Jacob J, Mathew AT (2019) Robust type-2 fuzzy fractional order PID controller for dynamic stability enhancement of power system having RES based microgrid penetration. Int J Electr Power Energy Syst 110:357–371

    Article  Google Scholar 

  • Azar AT, Serrano FE (2018) Fractional order sliding mode PID controller/observer for continuous nonlinear switched systems with PSO parameter tuning. In: International conference on advanced machine learning technologies and applications, pp 13–22

  • Cansiz A, Faydaci C, Qureshi MT, Usta O, McGuiness DT (2018) Integration of a SMES–battery-based hybrid energy storage system into microgrids. J Supercond Novel Magn 31(5):1449–1457

    Article  Google Scholar 

  • Gielen D, Boshell F, Saygin D, Bazilian MD, Wagner N, Gorini R (2019) The role of renewable energy in the global energy transformation. Energy Strategy Rev 1(24):38–50

    Article  Google Scholar 

  • Gonzalez A, Riba JR, Esteban B, Rius A (2018) Environmental and cost optimal design of a biomass–Wind–PV electricity generation system. Renew Energy 1(126):420–430

    Article  Google Scholar 

  • Hekimoğlu B (2019) Optimal tuning of fractional order PID controller for DC motor speed control via chaotic atom search optimization algorithm. IEEE Access 18(7):38100–38114

    Article  Google Scholar 

  • Horla D, Sadalla T (2020) Optimal tuning of fractional-order controllers based on Fibonacci-search method. ISA Trans 104:287–298

    Article  Google Scholar 

  • Huang C, Li F, Jin Z (2015) Maximum power point tracking strategy for large-scale wind generation systems considering wind turbine dynamics. IEEE Trans Ind Electron 62(4):2530–2539

    Article  Google Scholar 

  • Hwas A, Katebi R (2012) Wind turbine control using PI pitch angle controller. IFAC Proc 45(3):241–246

    Google Scholar 

  • Jumani TA, Mustafa MW, Hussain Z, Rasid MM, Saeed MS, Memon MM, Khan I, Nisar KS (2020) Jaya optimization algorithm for transient response and stability enhancement of a fractional-order PID based automatic voltage regulator system. Alex Eng J. 59(4):2429–2440

    Article  Google Scholar 

  • Kaouane M, Boukhelifa A, Cheriti A (2016) Regulated output voltage double switch buck–boost converter for photovoltaic energy application. Int J Hydrogen Energy 41(45):20847–20857

    Article  Google Scholar 

  • Khan IA, Alghamdi AS, Jumani TA, Alamgir A, Awan AB, Khidrani A (2019) Salp swarm optimization algorithm-based fractional order PID controller for dynamic response and stability enhancement of an automatic voltage regulator system. Electronics 8(12):1472

    Article  Google Scholar 

  • Li S, Jiang Y, Fang B, Wang C (2020) Characteristics analysis of inertia damping of grid-connected system of direct-drive wind power generation. IEEE Access 16(8):189802–189810

    Article  Google Scholar 

  • Lü X, Qu Y, Wang Y, Qin C, Liu G (2018) A comprehensive review on hybrid power system for PEMFC-HEV: issues and strategies. Energy Convers Manag 1(171):1273–1291

    Article  Google Scholar 

  • Mazouz F, Belkacem S, Colak I, Drid S, Harbouche Y (2020) Adaptive direct power control for double fed induction generator used in wind turbine. Int J Electr Power Energy Syst 1(114):105395

    Article  Google Scholar 

  • Mirjalili SZ, Mirjalili S, Saremi S, Faris H, Aljarah I (2018) Grasshopper optimization algorithm for multi-objective optimization problems. Appl Intell 48(4):805–820

    Article  Google Scholar 

  • Oladipo S, Sun Y, Wang Z (2020) Optimization of PID and FOPID controllers with new generation metaheuristic algorithms for controlling AVR system: concise survey. In: 2020 12th international conference on computational intelligence and communication networks (CICN). IEEE, pp 280–286

  • Oshaba AS, Ali ES, Elazim SA (2017) PI controller design using ABC algorithm for MPPT of PV system supplying DC motor pump load. Neural Comput Appl 28(2):353–364

    Article  Google Scholar 

  • Ozdemir S, Altin N, Sefa I (2017) Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter. Int J Hydrogen Energy 42(28):17748–17759

    Article  Google Scholar 

  • Park WJ, Sung BC, Park JW (2010) The effect of SFCL on electric power grid with wind-turbine generation system. IEEE Trans Appl Supercond 20(3):1177–1181

    Article  Google Scholar 

  • Pazikadin AR, Rifai D, Ali K, Malik MZ, Abdalla AN, Faraj MA (2020) Solar irradiance measurement instrumentation and power solar generation forecasting based on artificial neural networks (ANN): a review of five years research trend. Sci Total Environ 1(715):136848

    Article  Google Scholar 

  • Peng L, Zheng S, Chai X, Li L (2018) A novel tangent error maximum power point tracking algorithm for photovoltaic system under fast multi-changing solar irradiances. Appl Energy 210:303–316

    Article  Google Scholar 

  • Pradhan R, Majhi SK, Pradhan JK, Pati BB (2020) Optimal fractional order PID controller design using Ant Lion Optimizer. Ain Shams Eng J 11(2):281–291

    Article  Google Scholar 

  • Sahu BK, Pati TK, Nayak JR, Panda S, Kar SK (2016) A novel hybrid LUS–TLBO optimized fuzzy-PID controller for load frequency control of multi-source power system. Int J Electr Power Energy Syst 74:58–69

    Article  Google Scholar 

  • Sheikhan M, Shahnazi R, Yousefi AN (2013) An optimal fuzzy PI controller to capture the maximum power for variable-speed wind turbines. Neural Comput Appl 23(5):1359–1368

    Article  Google Scholar 

  • Singh SS, Fernandez E (2018) Modeling, size optimization and sensitivity analysis of a remote hybrid renewable energy system. Energy 15(143):719–731

    Article  Google Scholar 

  • Szczepanski R, Tarczewski T, Grzesiak LM (2019) Adaptive state feedback speed controller for PMSM based on Artificial Bee Colony algorithm. Appl Soft Comput 1(83):105644

    Article  Google Scholar 

  • Udayakumar MD, Anushree G, Sathyaraj J, Manjunathan A (2020) The impact of advanced technological developments on solar PV value chain. Mater Today Proc. https://doi.org/10.1016/j.matpr.2020.09.588

    Article  Google Scholar 

  • Ulusoy S, Nigdeli SM, Bekdaş G (2020) Novel metaheuristic-based tuning of PID controllers for seismic structures and verification of robustness. J Build Eng 33:101647

    Article  Google Scholar 

  • Youssef AR, Ali AI, Saeed MS, Mohamed EE (2019) Advanced multi-sector P&O maximum power point tracking technique for wind energy conversion system. Int J Electr Power Energy Syst 1(107):89–97

    Article  Google Scholar 

  • Zamani AA, Tavakoli S, Etedali S (2017) Fractional order PID control design for semi-active control of smart base-isolated structures: a multi-objective cuckoo search approach. ISA Trans 67:222–232

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India

    K. Aseem & S. Selva Kumar

Authors
  1. K. Aseem
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Selva Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All the authors have participated in writing the manuscript and have revised the final version. All authors read and approved the final manuscript.

Corresponding author

Correspondence to K. Aseem.

Ethics declarations

Conflict of interest

Authors declares that they have no conflict of interest.

Ethical approval

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

Informed consent

There is no informed consent for this study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aseem, K., Selva Kumar, S. Hybrid k-means Grasshopper Optimization Algorithm based FOPID controller with feed forward DC–DC converter for solar-wind generating system. J Ambient Intell Human Comput 13, 2439–2462 (2022). https://doi.org/10.1007/s12652-021-03173-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-021-03173-1

Keywords

Search

Navigation