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Energy saving for separately excited DC motor via optimization method with deterministic recursive linear least square algorithm

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Abstract

This paper presents the energy saving for separately excited direct current (DC) motor. The optimization method is adopted to calculate the field current for loss minimization. The appropriate motor and power loss parameters are important for calculating the field current for energy saving. Therefore, the deterministic recursive linear least square (DRLS) algorithm is used for the online estimation of the parameters of separately excited DC motor. Moreover, the adaptive Tabu search is used for the identification of power loss parameters. The results of the energy saving obtained using the optimization method with DRLS algorithm are compared with those obtained using the conventional and optimization methods without parameter estimation. The comparison results indicate that the optimization method with DRLS algorithm can achieve the minimum power loss for separately excited DC motor. The maximum percentage of energy saving is 77.77% compared with the conventional method.

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Abbreviations

LS:

The least square algorithm

DRLS:

The deterministic recursive linear least square

ATS:

Adaptive tabu search

OM:

Optimization method

OM + DRLS:

Optimization method with the deterministic recursive linear least square

\(v_{a}\) :

Armature voltage (V)

\(i_{a}\) :

Armature current (A)

\(R_{a}\) :

Armature resistance (\(\Omega\))

\(L_{a}\) :

Armature inductance (H)

\(v_{f}\) :

Field voltage (V)

\(i_{f}^{{}}\) :

Field current (A)

\(R_{f}\) :

Field resistance (\(\Omega\))

\(L_{f}\) :

Field inductance (H)

\(\omega\) :

Motor speed (rad/s)

\(N\) :

Motor speed (rpm)

\(K_{v}\) :

Voltage constant (Vs/Arad)

\(K_{t}\) :

Torque constant (Nm/A2)

\(J\) :

The moment of inertia (kgm2)

\(B\) :

The viscous friction coefficient (Nms/rad)

\(T_{L}\) :

Torque load (Nm)

\(P_{a}\) :

Armature copper loss (W)

\(P_{f}\) :

Field copper loss (W)

Pm :

Friction and windage losses (W)

\(K_{m}\) :

Constant coefficient of the friction and windage losses

\(P_{i}\) :

Core losses (W)

\(K_{h}\) :

Coefficients of the hysteresis loss

\(K_{e}\) :

Coefficients of the eddy current loss

PBD :

Brush loss (W)

\(V_{e}\) :

Voltage drop across the brushes (V)

\(P_{s}\) :

Stray loss (W)

\(K_{st}\) :

Coefficients of the stray loss (W)

Ploss :

Power loss (W)

Pin :

Input power (W)

Pout :

Output power (W)

\(n\) :

The total data number

References

  1. Farahani G, Rahmani K (2019) Speed control of a separately excited DC motor using new proposed fuzzy neural algorithm based on FOPID controller. J Control Autom Electr Syst 30:728–740

    Article  Google Scholar 

  2. Kumar S, Giri V (2016) Genetic algorithms techniques based optimal PID tuning for speed control of DC motor. Am J Eng Technol Manag 1:59–64

    Google Scholar 

  3. Hameed W, Kadhim A (2016) Field weakening control of a separately excited DC motor using neural network optimized by social spider algorithm. Engineering 8:1–10

    Article  Google Scholar 

  4. Abd-Elraouf M, El-Shenawy A (2017) Performance improvement of automatic tension control in multi coordinate drive systems for industrial paper winder. In: Nineteenth International Middle East Power Systems Conference, pp 1163–1169.

  5. Himani RD (2016) Development of Wind Turbine emulator for standalone wind energy conversion system. IEEE 6th International Conference on Power Systems

  6. Al Shiboul Y, Dalabeeh A, Al Shehhi K (2019) Optimal state feedback controller for separately excited DC motor. Int J Comput Sci Mob Comput 8:187–199

    Google Scholar 

  7. Pant M, Thangaraj R, Singh V (2008) Efficiency optimization of electric motors: a comparative study of stochastic algorithms. World J Modell Simul 4:140–148

    Google Scholar 

  8. Milan S, Vivek A, Paluri N (2009) A new energy optimal control scheme for a separately excited DC motor based incremental motion drive. Int J Autom Comput, 267–276

  9. Sergaki E, Stavrakakis G, Pouliezos A (2002) Optimal robot speed trajectory by minimization of the actuator motor electromechanical losses. J Intell Robot Syst, 187–207

  10. Issa R (2013) Separately excited DC motor optimal efficiency controller. J Eng Innov Technol 3:533–539

    Google Scholar 

  11. Zaki A, El-sattar A, Wahsh S, Mashaly H, Amer S (1998) Efficiency-optimized speed control for a separately-excited DC motor using adaptive Neuro controller. In: International Conference on Power Electronic Drives and Energy Systems for Industrial Growth, 421–426

  12. Angelo C, Bossio G, Garcia G (1999) Loss minimization in DC motor drives. In: IEEE International Electric Machines and Drives Conference. IEMDC'99. Proceedings, 701–703

  13. Hani S, Hassan K, Hazari R, Mannan A (2015) Speed control of separately excited DC motor with efficiency maximization propensity. The AIUB J Sci Eng 14:61–70

    Google Scholar 

  14. Udomsuk S, Areerak K-L, Areerak K-N (2015) The energy saving for separately excited DC motor drive via model based method. J Electr Eng Technol, 470–479

  15. Young P (2011) Recursive estimation and time-series analysis. Springer, New York

    Book  Google Scholar 

  16. Puangdownreong D, Areerak K-N, Srikaew A, Sujitjorn S, Totaron P (2002) System identification via adaptive Tabu search. In: IEEE International Conference on Industrial Technology, 915–920

  17. Ryff P (1994) Electric machinery. Prentice-Hall International, Singapore

    Google Scholar 

  18. Chapman J (1999) Electric machinery fundamentals. Mc Graw Hill, Boston

    Google Scholar 

  19. Glover F, Laguna M (1997) Tabu search. Kluwer Academic

    Book  MATH  Google Scholar 

  20. Kluabwang J, Puangdownreong D, Sujitjorn S (2012) Multipath adaptive tabu search for a vehicle control problem. J Appl Math 2012:1–20

    Article  MathSciNet  MATH  Google Scholar 

  21. Jean S, Tignol P (2001) Galois theory of algebraic equations. World Scientific, Belgium

    MATH  Google Scholar 

  22. Johan K, Wittenmark B (1995) Adaptive control. Addison-Wesley, Boston

    MATH  Google Scholar 

Download references

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Authors and Affiliations

  1. School of Mechatronics Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand

    Jeerawan Homjan

  2. School of Electrical Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand

    Kongpol Areerak & Kongpan Areerak

  3. School of Mathematics, Institute of Science, Suranaree University of Technology, Nakhon Ratchasima, Thailand

    Tidarut Areerak

  4. Suranaree University of Technology, Nakhon Ratchasima, Thailand

    Sasiya Udomsuk

  5. She Is Now With the National Electronics and Computer Technology Center, Pathum Thani, Thailand

    Sasiya Udomsuk

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  1. Jeerawan Homjan
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  2. Kongpol Areerak
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  4. Sasiya Udomsuk
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  5. Kongpan Areerak
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Correspondence to Kongpol Areerak.

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This work was supported by Suranaree University of Technology (SUT).

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Homjan, J., Areerak, K., Areerak, T. et al. Energy saving for separately excited DC motor via optimization method with deterministic recursive linear least square algorithm. Electr Eng 104, 3955–3967 (2022). https://doi.org/10.1007/s00202-022-01593-6

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  • DOI: https://doi.org/10.1007/s00202-022-01593-6

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