Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Proportional–integral–derivative optimization algorithm for double-fed induction generator with the maximum wind power tracking technique

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

This paper proposes a novel control-centric optimization algorithm, i.e., proportional–integral–derivative optimization algorithm. The proposed optimization algorithm is inspired by the conventional proportional–integral–derivative controller. The proposed optimization algorithm consists of two types of controllers, i.e., explorative controllers with variable parameters and exploitative controllers with fixed parameters. In the exploration process of the approach, multiple explorative controllers with variable parameters move toward the global optimal solution domain. In the exploitation process of the approach, multiple exploitative controllers with fixed parameters moving toward to local optimal solution. The case studies results obtained by the proposed proportional–integral–derivative optimization algorithm under a total of eight basic mathematical optimization problems and the parameters optimization problem of the double-fed induction generator with the maximum wind power tracking technique show that the proportional–integral–derivative optimization algorithm can explore and exploit the global optimal solution effectively.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Abu Arqub O, AL-Smadi M, Momani S, Hayat T (2016) Numerical solutions of fuzzy differential equations using reproducing kernel hilbert space method. Soft Comput 20(8):3283–3302

    Article  Google Scholar 

  • Afrasiabi S, Saffarian A, Mashhour E (2019) Dynamic state estimation of power systems using intelligent particle filtering based on ant colony optimisation for continuous domains. IET Gener Trans Distribut 13:2627–2636 (9)

    Article  Google Scholar 

  • Ahmadlou M, Karimi M, Alizadeh S, Shirzadi A, Parvinnejhad D, Shahabi H, Panahi M (2019) Flood susceptibility assessment using integration of adaptive network-based fuzzy inference system (anfis) and biogeography-based optimization (bbo) and bat algorithms (ba). Geocarto Int 34(11):1252–1272

    Article  Google Scholar 

  • Al-Betar MA, Awadallah MA, Khader AT, Bolaji AL, Almomani A (2018) Economic load dispatch problems with valve-point loading using natural updated harmony search. Neural Comput Appl 29(10):767–781

    Article  Google Scholar 

  • Ali MZ, Awad NH, Reynolds RG, Suganthan PN (2018) A balanced fuzzy cultural algorithm with a modified levy flight search for real parameter optimization. Inf Sci 447:12–35

    Article  Google Scholar 

  • Alvarez A, Munari P, Morabito R (2018) Iterated local search and simulated annealing algorithms for the inventory routing problem. Int Trans Oper Res 25(6):1785–1809

    Article  MathSciNet  Google Scholar 

  • Arqub OA, Abo-Hammour Z (2014) Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm. Inform Sci 279:396–415

    Article  MathSciNet  Google Scholar 

  • Arqub OA, Al-Smadi M (2020) Fuzzy conformable fractional differential equations: novel extended approach and new numerical solutions. Soft Comput 24(16):12501–12522

    Article  Google Scholar 

  • Arqub OA, Al-Smadi M, Momani S, Hayat T (2017) Application of reproducing kernel algorithm for solving second-order, two-point fuzzy boundary value problems. Soft Comput 21(23):7191–7206

    Article  Google Scholar 

  • Chevalier M, Gómez-Schiavon M, Ng AH, El-Samad H (2018) Design and analysis of a proportional-integral-derivative controller with biological molecules. Cell Syst 19:307–332

    Google Scholar 

  • Duan P-Y, Li J-Q, Wang Y, Sang H-Y, Jia B-X (2018) Solving chiller loading optimization problems using an improved teaching-learning-based optimization algorithm. Opt Control Appl Methods 39(1):65–77

    Article  Google Scholar 

  • El-Hay EA, El-Hameed MA, El-Fergany AA (2018) Improved performance of pem fuel cells stack feeding switched reluctance motor using multi-objective dragonfly optimizer. Neural Comput Appl 19:307–332

    Google Scholar 

  • Faris H, Hassonah MA, Al-Zoubi AM, Mirjalili S, Aljarah I (2018) A multi-verse optimizer approach for feature selection and optimizing svm parameters based on a robust system architecture. Neural Comput Appl 30(8):2355–2369

    Article  Google Scholar 

  • Faris H, Mirjalili S, Aljarah I (2019) Automatic selection of hidden neurons and weights in neural networks using grey wolf optimizer based on a hybrid encoding scheme. Int J Mach Learn Cybern 10(10):2901–2920

    Article  Google Scholar 

  • Hahm J, Kim E, Lee H, Yoon C (2016) A modified perturb and observe sliding mode maximum power point tracking method for photovoltaic system under partially shaded conditions. Int J Fuzzy Logic Intell Syst 16(4):281–292

    Article  Google Scholar 

  • Jain I, Jain VK, Jain R (2018) Correlation feature selection based improved-binary particle swarm optimization for gene selection and cancer classification. Appl Soft Comput 62:203–215

    Article  Google Scholar 

  • Jena C, Mishra SS, Panda B (2018) Group search optimization technique for multi-area economic dispatch. In: Satapathy SC, Tavares JMR, Bhateja V, Mohanty JR (eds) Inf Decis Sci. Springer, Singapore, pp 217–225

    Google Scholar 

  • Kadri RL, Boctor FF (2018) An efficient genetic algorithm to solve the resource-constrained project scheduling problem with transfer times: the single mode case. Euro J Oper Res 265(2):454–462

    Article  MathSciNet  Google Scholar 

  • Kan G, He X, Ding L, Li J, Hong Y, Zuo D, Ren M, Lei T, Liang K (2018) Fast hydrological model calibration based on the heterogeneous parallel computing accelerated shuffled complex evolution method. Eng Optimiz 50(1):106–119

    Article  MathSciNet  Google Scholar 

  • Kanimozhi G, Kumar H (2018) Modeling of solar cell under different conditions by ant lion optimizer with lambertw function. Appl Soft Comput 71:141–151

    Article  Google Scholar 

  • Karaboga D, Aslan S (2019) Discovery of conserved regions in dna sequences by artificial bee colony (abc) algorithm based methods. Nat Comput 18(2):333–350

    Article  MathSciNet  Google Scholar 

  • Khalilpourazari S, Khalilpourazary S (2019) An efficient hybrid algorithm based on water cycle and moth-flame optimization algorithms for solving numerical and constrained engineering optimization problems. Soft Comput 23(5):1699–1722

    Article  Google Scholar 

  • Li S, Fang H, Liu X (2018) Parameter optimization of support vector regression based on sine cosine algorithm. Exp Syst Appl 91:63–77

    Article  Google Scholar 

  • Liu C, Niu P, Li G, Ma Y, Zhang W, Chen K (2018) Enhanced shuffled frog-leaping algorithm for solving numerical function optimization problems. J Intell Manuf 29(5):1133–1153

    Article  Google Scholar 

  • Mafarja M, Mirjalili S (2018) Whale optimization approaches for wrapper feature selection. Appl Soft Comput 62:441–453

    Article  Google Scholar 

  • Mikaeil R, Haghshenas SS, Haghshenas SS, Ataei M (2018) Performance prediction of circular saw machine using imperialist competitive algorithm and fuzzy clustering technique. Neural Comput Appl 29(6):283–292

    Article  Google Scholar 

  • Rabah M, Rohan A, Han Y-J, Kim S-H (2018) Design of fuzzy-PID controller for quadcopter trajectory-tracking. Int J Fuzzy Logic Intell Syst 18(3):204–213

    Article  Google Scholar 

  • Shankar K, Baviskar AS (2018) Improved hybrid strength pareto evolutionary algorithms for multi-objective optimization. Int J Intell Comput Cybern 11(1):20–46

    Article  Google Scholar 

  • Tey KS, Mekhilef S, Seyedmahmoudian M, Horan B, Oo AT, Stojcevski A (2018) Improved differential evolution-based MPPT algorithm using sepic for pv systems under partial shading conditions and load variation. IEEE Trans Ind Inform 14(10):4322–4333

    Article  Google Scholar 

  • Wang H, Wang W, Cui Z, Zhou X, Zhao J, Li Y (2018) A new dynamic firefly algorithm for demand estimation of water resources. Inf Sci 438:95–106

    Article  MathSciNet  Google Scholar 

  • Wu Z, Yu D, Kang X (2018) Application of improved chicken swarm optimization for MPPT in photovoltaic system. Optimal Control Appl Methods 39(2):1029–1042

    Article  MathSciNet  Google Scholar 

  • Yang B, Zhang X, Yu T, Shu H, Fang Z (2017) Grouped grey wolf optimizer for maximum power point tracking of doubly-fed induction generator based wind turbine. Energy Convers Manage 133:427–443

    Article  Google Scholar 

  • Zhang X, Lou Y, Yuen SY, Wu Z, He Y, Zhang X (2019) Hybrid artificial bee colony with covariance matrix adaptation evolution strategy for economic load dispatch, in. IEEE Congress Evolut Comput (CEC) 2019:204–209

    Google Scholar 

  • Zheng Z xin, Li J qing (2018) Optimal chiller loading by improved invasive weed optimization algorithm for reducing energy consumption. Energy Build 161:80–88

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by Natural Science Foundation of Guangxi Province under Grant. AD19245001 and 2020GXNSFBA159025.

Author information

Authors and Affiliations

  1. College of Electrical Engineering, Guangxi University, Nanning, 530004, Guangxi, People’s Republic of China

    Linfei Yin & Qi Gao

Authors
  1. Linfei Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qi Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Linfei Yin.

Ethics declarations

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 or animals performed by any of the authors.

Additional information

Communicated by V. Loia.

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

Yin, L., Gao, Q. Proportional–integral–derivative optimization algorithm for double-fed induction generator with the maximum wind power tracking technique. Soft Comput 25, 3097–3111 (2021). https://doi.org/10.1007/s00500-020-05365-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-05365-x

Keywords

Search

Navigation