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Design and implementation of fault tolerant fractional order controllers for the output power of self-excited induction generator

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Abstract

In this paper, a static volt-ampere-reactive compensator (SVC) composed of fixed capacitor-thyristor controlled reactor (FC-TCR) is employed to regulate the terminal voltage of self-excited induction generator (SEIG) in the purpose of controlling the output power. In addition, the speed of the generator is adjusted by robust proportional-integral-derivative (PID) in order to regulate the frequency. Fractional order PID (FOPID) and Tilt-PID (T-PID) controllers are proposed to adjust the triggering angle of FC-TCR. The objectives of the paper are to design the optimal voltage controller with a robust PID frequency controller and to maintain the output power at a desired level in case of voltage sensor faults. In this regards, nonlinear autoregressive network with exogenous inputs (NARX) and small signal models of the SEIG are constructed where triggering angle and generator speed are inputs, whereas terminal voltage and frequency are outputs, respectively. Training of the NARX model is achieved with a high regression value (R2 = 0.99) where the accuracy of the small signal voltage model is 82%. After comparing the accuracy of the models, NARX-based fault detection architecture and fault tolerant controller are designed in order to avoid incorrect control of the output power. To establish the effectiveness of proposed fault tolerant controllers, simulation studies are conducted using particle swarm optimization (PSO). The designed controllers are performed experimentally on three-phase 5.5 kW, 400 V, 50 Hz, balanced loaded SEIG to confirm the effectiveness. The system is tested with proposed controllers under the healthy sensor, faulty sensor and dynamic reference conditions. Results give good agreement with simulation results and designed fault tolerant T-PID controller performs better tracking dynamics with small deviations whether the voltage sensor is faulty or not.

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References

  1. Dahiya P, Sharma V, Naresh R (2017) Hybridized gravitational search algorithm tuned sliding mode controller design for load frequency control system with doubly fed induction generator wind turbine. Optim Control Appl Methods 38(6):993–1003

    Article  Google Scholar 

  2. B. Rezaeealam, “Calculation of magnetizing and leakage inductances of induction machine using finite element method,” Electr. Eng., pp. 1–9, 2020.

  3. Murthy SS, Malik OP, Tandon AK (1982) Analysis of self-excited induction generators. IEE Proceedings C (Generation, Transmission and Distribution) 129(6):260–265

    Article  Google Scholar 

  4. Paliwal S, Sinha SK, Chauhan YK, “Performance optimization of self excited induction generator: a state of art”, in, (2017) Recent Developments in Control. Automation & Power Engineering (RDCAPE) 2017:416–420

    Google Scholar 

  5. Chaturvedi Y, Kumar S, Gupta V (2020) Capacitance Requirement for Rated Current and Rated Voltage Operation of SEIG Using Whale Optimization Algorithm. Procedia Comput Sci 167:2581–2589

    Article  Google Scholar 

  6. H. Calgan, J. M. Andrade, and M. Demirtas, “RSM-Based Optimization of Excitation Capacitance and Speed for a Self-Excited Induction Generator,” in Mathematical Modelling and Optimization of Engineering Problems, Springer, 2020, pp. 139–155.

  7. Murthy SS, Kalla UK, Bhuvaneswari G (2010) “A novel electronic controller implementation for voltage regulation of single phase self-excited induction generator”, in Industry Applications Society Annual Meeting (IAS). IEEE 2010:1–8

    Google Scholar 

  8. Chauhan PJ, Chatterjee JK, Bhere H, Perumal BV, Sarkar D (2014) Synchronized operation of DSP-based generalized impedance controller with variable-speed isolated SEIG for novel voltage and frequency control. IEEE Trans Ind Appl 51(2):1845–1854

    Article  Google Scholar 

  9. Chilipi RR, Singh B, Murthy SS (2014) Performance of a self-excited induction generator with DSTATCOM-DTC drive-based voltage and frequency controller. IEEE Trans energy Convers 29(3):545–557

    Article  Google Scholar 

  10. Singh B, Murthy SS, Gupta S (2006) A voltage and frequency controller for self-excited induction generators. Electr Power Components Syst 34(2):141–157

    Article  Google Scholar 

  11. Tandekar JK, Ojha A, Jain S (2019) SEIG-Based Renewable Generation for MVDC Ship Power System with Improved Power Quality. Electr Power Components Syst 47(1–2):27–42

    Article  Google Scholar 

  12. S. Pati, K. B. Mohanty, and S. K. Kar, “Performance improvement of a STATCOM using fuzzy controller for isolated generator,” World J. Eng., 2018.

  13. Shawon MH, Hanzelka Z, Dziadecki A (2015) “Voltage-current and harmonic characteristic analysis of different FC-TCR based SVC”, in. IEEE Eindhoven PowerTech 2015:1–6

    Google Scholar 

  14. Mosaad MI (2011) Control of self excited induction generator using ANN based SVC. Int J Comput Appl 23(5):975–8887

    Google Scholar 

  15. T. Ouchbel, S. Zouggar, M. Sedik, M. Oukili, M. Elhafyani, and A. Rabhi, “Control of the output voltage of asynchronous wind turbine with variable speed using a static VAR compensator (SVC),” in Sustainability in Energy and Buildings, Springer, 2012, pp. 17–30.

  16. Ahmed T, Noro O, Hiraki E, Nakaoka M (2004) Terminal voltage regulation characteristics by static var compensator for a three-phase self-excited induction generator. IEEE Trans Ind Appl 40(4):978–988

    Article  Google Scholar 

  17. H. Çalgan, E. Ilten, and M. Demirtas, “Thyristor controlled reactor‐based voltage and frequency regulation of a three‐phase self‐excited induction generator feeding unbalanced load,” Int. Trans. Electr. Energy Syst., p. e12387, 2020.

  18. H. Mahvash, S. A. Taher, and M. Shahidehpour, “Performance improvement of type 4 wind turbine synchronous generator using fractional‐order PI (FOPI) and PI controllers designed by the analytical approach,” Int. Trans. Electr. Energy Syst., p. e12403, 2020.

  19. Guha D, Roy PK, Banerjee S (2018) Maiden application of SSA-optimised CC-TID controller for load frequency control of power systems. IET Gener Transm Distrib 13(7):1110–1120

    Article  Google Scholar 

  20. Geng H, Xu D, Wu B, Huang W (2011) Direct voltage control for a stand-alone wind-driven self-excited induction generator with improved power quality. IEEE Trans Power Electron 26(8):2358–2368

    Article  Google Scholar 

  21. L. G. Scherer, C. B. Tischer, F. C. Posser, C. M. Franchi, and R. F. de Camargo, “Hybrid topology for voltage regulation applied in three-phase four-wire micro hydro power station,” in IECON 2013–39th Annual Conference of the IEEE Industrial Electronics Society, 2013, pp. 7169–7174.

  22. Tischer CB, Tibola JR, Scherer LG, de Camargo RF (2017) Proportional-resonant control applied on voltage regulation of standalone SEIG for micro-hydro power generation. IET Renew Power Gener 11(5):593–602

    Article  Google Scholar 

  23. S. Dewangan and S. Vadhera, “Performance Improvement of Wind Turbine Induction Generator Using Neural Network Controller,” in Advances in Renewable Energy and Sustainable Environment, Springer, pp. 165–172.

  24. Natsheh E, Samara S (2020) Tree Search Fuzzy NARX Neural Network Fault Detection Technique for PV Systems with IoT Support. Electronics 9(7):1087

    Article  Google Scholar 

  25. Al-Waeli AHA, Kazem HA, Yousif JH, Chaichan MT, Sopian K (2020) Mathematical and neural network modeling for predicting and analyzing of nanofluid-nano PCM photovoltaic thermal systems performance. Renew Energy 145:963–980

    Article  Google Scholar 

  26. Liu J, Li T, Zhang Z, Chen J (2020) NARX prediction-based parameters online tuning method of intelligent PID system. IEEE Access 8:130922–130936

    Article  Google Scholar 

  27. Dalei J, Mohanty KB (2016) Fault classification in SEIG system using Hilbert-Huang transform and least square support vector machine. Int J Electr Power Energy Syst 76:11–22

    Article  Google Scholar 

  28. Iyer KLV, Lu X, Mukherjee K, Kar NC (2012) “Fault detection in copper-rotor SEIG system using artificial neural network for distributed wind power generation”, in. XXth International Conference on Electrical Machines 2012:1700–1705

    Google Scholar 

  29. Derbal M, Toubakh H (2018) Early Fault Diagnosis in Exciting Capacitors of Self-Excited Induction Generator for Wind Energy Applications. International Conference on Communications and Electrical Engineering (ICCEE) 2018:1–5

    Google Scholar 

  30. A. Sboui, M. Salah, A. El Fahem, K. Bacha, A. Chaari, and M. Gharbi, “Speed Ripples Effects on the SEIG Performances for Isolated-Site Wind-Turbine Application,” in 2018 15th International Multi-Conference on Systems, Signals & Devices (SSD), 2018, pp. 88–93.

  31. Ahmed T, Nishida K, Soushin K, Nakaoka M (2005) Static VAR compensator-based voltage control implementation of single-phase self-excited induction generator. IEE Proce-Generat Transm Distrib 152(2):145–156

    Article  Google Scholar 

  32. T. Ahmed, K. Ogura, K. Soshin, E. Hiraki, and M. Nakaoka, Small-scale wind turbine coupled single-phase self-excited induction generator with SVC for isolated renewable energy utilization,” in Power Electronics and Drive Systems, 2003. PEDS 2003. The Fifth International Conference on, 2003, 1, 781–786.

  33. Singh B, Murthy SS, Gupta S (2004) Analysis and design of STATCOM-based voltage regulator for self-excited induction generators. IEEE Trans Energy Convers 19(4):783–790

    Article  Google Scholar 

  34. Çimen ME, Kaçar S, Guleryüz E, Gürevin B, Akgül A (2018) Modelling of chaotic motion video with artificial neural networks. J Inst Sci Technol 20(3):23–35

    Google Scholar 

  35. Di Piazza A, Di Piazza MC, Vitale G (2016) Solar and wind forecasting by NARX neural networks. Renew Energy Environ Sustain 1:39

    Article  Google Scholar 

  36. Sahin E (2020) Design of an optimized fractional high order differential feedback controller for load frequency control of a multi-area multi-source power system with nonlinearity. IEEE Access 8:12327–12342

    Article  Google Scholar 

  37. Özdemir N, İskender BB (2010) Fractional order control of fractional diffusion systems subject to input hysteresis. J. Comput. nonlinear Dyn. 5:2

    Google Scholar 

  38. Ilten E, Demirtas M (2019) Fractional order super-twisting sliding mode observer for sensorless control of induction motor. COMPEL-The Int J Comput Math Electr Electron Eng 38(2):878–892

    Article  Google Scholar 

  39. Ilten E, Demirtas M (2016) Off-line tuning of fractional order PIλ controller by using response surface method for induction motor speed control. J Control Eng Appl Informatics 18(2):20–27

    Google Scholar 

  40. Demirtas M, Ilten E, Calgan H (2019) Pareto-based multi-objective optimization for fractional order PIλ speed control of induction motor by using elman neural network. Arab J Sci Eng 44(3):2165–2175

    Article  Google Scholar 

  41. M. S. Ayas, E. Sahin, and İ. H. Altaş, Performance of PSO based classical and fractional PID controllers for an unmanned surface vehicle,” in 2018 26th Signal Processing and Communications Applications Conference (SIU), 2018, 1–4.

  42. Simani S, Fantuzzi C (2000) Fault diagnosis in power plant using neural networks. Inf Sci (Ny) 127(3–4):125–136

    Article  Google Scholar 

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Acknowledgement

Corresponding author was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) 2211-E program. The authors also would like to thank Prof. M. Sedraoui for the useful discussion.

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Correspondence to Haris Calgan.

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Appendix

Appendix

SEIG parameters

Power (Pn)

5.5 kW

Rated voltage (Vn)

400 V

Rated current (In)

11.2 A

Rated speed (Nn)

1465 r/min

Frequency (fn)

50 Hz

cosineφ

0.79

Stator resistance (R1)

0.90 \(\Omega \)

Rotor resistance (R2)

0.61 \(\Omega \)

Stator reactance (X1)

1.50 \(\Omega \)

Rotor reactance (X2)

4.20 \(\Omega \)

Rotor Inertia (J)

0.038 kg·m2

Friction Factor (B)

0.00051 N·m·s

Pole Pairs

2

IM parameters

Power (Pn)

7.5 kW

Rated voltage (Vn)

400 V

Rated current (In)

13.3 A

Rated speed (Nn)

2930 r/min

Frequency (fn)

50 Hz

cosineφ

0.90

Pole Pairs

1

PSO Options

Function tolerance

1e-6

Inertia range

[0.1,1.1]

Initial swarm span

2000

Max iteration

1000

Max stall iterations

20

Max stall time

Inf

Max time

Inf

Min neighbors fraction

0.25

Objective limit

-Inf

Self-adjustment weight

1.49

Social adjustment weight

1.49

Swarm size

[100, 10*number of variables]

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Calgan, H., Demirtas, M. Design and implementation of fault tolerant fractional order controllers for the output power of self-excited induction generator. Electr Eng 103, 2373–2389 (2021). https://doi.org/10.1007/s00202-021-01242-4

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