Skip to main content
SpringerLink
Log in

A Comparative Analysis of Artificial Neural Network Algorithms to Enhance the Power Quality of Photovoltaic Distributed Generation System Based on Metrological Parameters

  • Original Paper
  • Published:
MAPAN Aims and scope Submit manuscript

Abstract

This paper examines the solar irradiance estimation as well as power quality enhancement of photovoltaic distributed generation system as seen from a metrological perspective. The enhancement of power quality is fundamental considerations. In this paper, the artificial neural network has been trained on historical data for solar forecasting and to anticipate reference current for the controller to integrate the power quality enhancement features. Six algorithms have been used for training. Metrological parameters were used to determine the optimum training of neural network. Training results of some of the algorithms were found approximately same with minor difference in terms mean square error and number of epochs. Scaled conjugate gradient algorithm shows least mean square error 0.01611 among six distinct algorithm. Results of the ANN model show that the comparable models are obtainable from different training algorithms. Using the power quality enhancement measure, a neural network estimates the reference current required for uninterrupted power transfer across systems. With a R = 25 Ω, L = 30 mH load, the harmonic content of the grid current was decreased from 19.69 to 4.20% using power quality enhancement controller based on an artificial neural network. Statistical parameter has been used to plot the error for a visual comparison of system efficacy.

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

Similar content being viewed by others

References

  1. H. Li, Z. Ren, Y. Xu, L. Wenyuan and B. Hu, A multi-data driven hybrid learning method for weekly photovoltaic power scenario forecast. IEEE Trans. Sustain. Energy, 13(1) (2022) 91–100.

    Article  ADS  Google Scholar 

  2. J. Shi, W.J. Lee, Y. Liu, Y. Yang and P. Wang, Forecasting power output of photovoltaic systems based on weather classification and support vector machines. IEEE Trans. Ind. Appl., 48(3) (2012) 1064–1069.

    Article  Google Scholar 

  3. R. Ahmed, V. Sreeram, Y. Mishra and M.D. Arif, A review and evaluation of the state-of-the-art in PV solar power forecasting: techniques and optimization. Renew. Sustain. Energy Rev., 124 (2020) 109792.

    Article  Google Scholar 

  4. G.M. Yagli, D. Yang and D. Srinivasan, Reconciling solar forecasts: probabilistic forecasting with homoscedastic Gaussian errors on a geographical hierarchy. Sol. Energy, 210 (2020) 59–67.

    Article  ADS  Google Scholar 

  5. G.M. Yagli, D. Yang and D. Srinivasan, Automatic hourly solar forecasting using machine learning models. Renew. Sustain. Energy Rev., 105 (2019) 487–498.

    Article  Google Scholar 

  6. T.J.B.M. Postma and F. Liebl, How to improve scenario analysis as a strategic management tool? Technol. Forecast. Soc. Change, 72(2) (2005) 161–173.

    Article  Google Scholar 

  7. J. Liang and W. Tang, Sequence generative adversarial networks for wind power scenario generation. IEEE J. Sel. Areas Commun., 38(1) (2020) 110–118.

    Article  MathSciNet  Google Scholar 

  8. S.I. Vagropoulos, E.G. Kardakos, C.K. Simoglou, A.G. Bakirtzis and J.P.S. Catalão, ANN-based scenario generation methodology for stochastic variables of electric power systems. Electr. Power Syst. Res., 134 (2016) 9–18.

    Article  Google Scholar 

  9. S.K. Singh, A. Agarwal and T. Kanumuri, A generalized WECS system with single phase multilevel inverter having MPPT control with neural network. J. Eng. Res. (JER) Kuwait, 10 (2022) 1–7.

    Google Scholar 

  10. S. K. Singh, A. Agarwal, T. Kanumuri, and M. Roy, “Modelling and controlling of induction heating unit for induction cooking application, in Journal of Physics: Conference Series, 2020.

  11. J. Hu, Z. Li, J. Zhu and J.M. Guerrero, Voltage stabilization: a critical step toward high photovoltaic penetration. IEEE Ind. Electron. Mag., 13(2) (2019) 17–30.

    Article  Google Scholar 

  12. A. Agarwal and Jatin and V.K. Jadoun, Design and modelling for LCL filter for measurement of THD reduction in different modular multilevel converters. MAPAN, 36(3) (2021) 599–606.

    Article  Google Scholar 

  13. D. Akinyele, E. Olabode and A. Amole, Review of fuel cell technologies and applications for sustainable microgrid systems. Inventions, 5(3) (2020) 1–35.

    Article  Google Scholar 

  14. E.S. Hass, F.C. Correa and C.B. Nascimento, “A novel non-isolated high DC-DC voltage based on boost converter employing coupled inductor”, 14th Brazilian Power Electron. Conf. COBEP, 2017 (2017) 1–6.

    Google Scholar 

  15. M.A. Abdullah, A.H.M. Yatim, C.W. Tan and R. Saidur, A review of maximum power point tracking algorithms for wind energy systems. Renew. Sustain. Energy Rev., 16(5) (2012) 3220–3227.

    Article  Google Scholar 

  16. A. Khalatbarisoltani, M. Kandidayeni, L. Boulon and X. Hu, Power allocation strategy based on decentralized convex optimization in modular fuel cell systems for vehicular applications. IEEE Trans. Veh. Technol., 69(12) (2020) 14563–14574.

    Article  Google Scholar 

  17. S. Pukhrem, M. Basu and M.F. Conlon, Probabilistic risk assessment of power quality variations and events under temporal and spatial characteristic of increased PV integration in low-voltage distribution networks. IEEE Trans. Power Syst., 33(3) (2018) 3246–3254.

    Article  ADS  Google Scholar 

  18. B. Liu, L. Wang, D. Song, M. Su, J. Yang, D. He, Z. Chen and S. Son, Input current ripple and grid current harmonics restraint approach for single-phase inverter under battery input condition in residential photovoltaic/battery systems. IEEE Trans. Sustain. Energy, 9(4) (2018) 1957–1968.

    Article  ADS  Google Scholar 

  19. S.K. Singh, A. Agarwal and T. Kanumuri, Power quality improvement of a fuel cell-powered filterless distributed generation system using sinusoidal pulse width modulation. Indian Journal of Pure & Applied Physics (IJPAP), 60(9) (2022) 754–762.

    Google Scholar 

  20. F. Blaabjerg, R. Teodorescu, M. Liserre and A.V. Timbus, Overview of control and grid synchronization for distributed power generation systems. IEEE Trans. Ind. Electron., 53(5) (2006) 1398–1409.

    Article  Google Scholar 

  21. G.V.A. Overview, L. Harnefors, S. Member, X. Wang, A.G. Yepes and F. Blaabjerg, “Passivity-based stability assessment of”, IEEE. J. Emerg. Sel. Top. Power Electron., 4(1) (2016) 116–125.

    Article  Google Scholar 

  22. D.J. Hogan, F.J. Gonzalez-Espin, J.G. Hayes, G. Lightbody and R. Foley, An adaptive digital-control scheme for improved active power filtering under distorted grid conditions. IEEE Trans. Ind. Electron., 65(2) (2017) 988–999.

    Article  Google Scholar 

  23. T. Noguchi, H. Tomiki, S. Kondo and I. Takahashi, Direct power control of PWM converter without power-source voltage sensors. IEEE Trans. Ind. Appl., 34(3) (1998) 473–479.

    Article  Google Scholar 

  24. C. Ma, J. Dasenbrock, J.C. Töbermann and M. Braun, A novel indicator for evaluation of the impact of distributed generations on the energy losses of low voltage distribution grids. Appl. Energy, 242 (2019) 674–683.

    Article  Google Scholar 

  25. Q. Li, B. Su, Y. Pu, Y. Han, T. Wang, L. Yin and W. Chen, A state machine control based on equivalent consumption minimization for fuel cell/ supercapacitor hybrid tramway. IEEE Trans. Transp. Electrif., 5(2) (2019) 552–564.

    Article  Google Scholar 

  26. S. Jain, N.K. Jain and W.J. Vaughn, Challenges in meeting all of India’s electricity from solar: An energetic approach. Renew. Sustain. Energy Rev., 82 (2018) 1006–1013.

    Article  Google Scholar 

  27. R. Panigrahi, S.K. Mishra, S.C. Srivastava, A.K. Srivastava and N.N. Schulz, Grid integration of small-Scale photovoltaic systems in secondary distribution network - a review. IEEE Trans. Ind. Appl., 56 (2020) 3178–3195.

    Article  Google Scholar 

  28. W. Li, P.W.Y. Chiu and Z. Li, An accelerated finite-time convergent neural network for visual servoing of a flexible surgical endoscope with physical and RCM constraints. IEEE Trans. Neural Networks Learn. Syst., 31(12) (2020) 5272–5284.

    Article  MathSciNet  Google Scholar 

  29. S. Motahhir, A. El Ghzizal, S. Sebti and A. Derouich, Modeling of photovoltaic system with modified incremental conductance algorithm for fast changes of irradiance. Int. J. Photoenergy, 2018 (2018) 1–13.

    Article  Google Scholar 

  30. I.S. Mohamed, S. Rovetta, T.D. Do, T. Dragicevi’c and A.A.Z. Diab, A neural-network-based model predictive control of three-phase inverter with an output LCfilter. IEEE Access, 7 (2019) 124737–124749.

    Article  Google Scholar 

  31. J. Duan, Z. Yi, D. Shi, C. Lin, X. Lu and Z. Wang, Reinforcementlearning-based optimal control of hybrid energy storage systems in hybrid AC–DC microgrids. IEEE Trans. Ind. Informat., 15(9) (2019) 5355–5364.

    Article  Google Scholar 

  32. M.A. Khan, A. Haque, V.S.B. Kurukuru and M. Saad, “Advanced control strategy with voltage sag classification for single-phase gridconnected photovoltaic system”, IEEE Trans. Emerg. Sel. Top. Ind. Electron., 3(2) (2020) 258–269.

    Article  Google Scholar 

  33. A. Rosato, M. Panella, R. Araneo, and A. Andreotti, A neural network based prediction system of distributed generation for the management of microgrids,” IEEE Trans. Ind. Appl., 55(6) (2019) 7092–7102.

    Article  Google Scholar 

  34. N. Chettibi, A. Mellit, G. Sulligoi and A. Massi Pavan, Adaptive neural network-based control of a hybrid AC/DC microgrid. IEEE Trans. Smart Grid, 9(3) (2018) 1667–1679.

    Google Scholar 

  35. A.N. Akpolat, M.R. Habibi, H.R. Baghaee, E. Dursun, A.E. Kuzucuoğlu, Y. Yang, T. Dragičević and F. Blaabjerg, Dynamic stabilization of dc microgrids using ann-based model predictive control. IEEE Transactions on Energy Conversion, 37(2) (2021) 999–1010.

    Article  ADS  Google Scholar 

  36. Y. Zeng, A.I. Maswood, J. Pou, X. Zhang, Z. Li, C. Sun, S. Mukherjee, A.K. Gupta and J. Dong, Active disturbance rejection control using artificial neural network for dual-active-bridge-based energy storage system. IEEE Journal of Emerging and Selected Topics in Power Electronics, 11 (2023) 301–311.

    Article  Google Scholar 

  37. M. Novak, H. Xie, T. Dragicevic, F. Wang, J. Rodriguez and F. Blaabjerg, Optimal cost function parameter design in predictive torque control (PTC) using artificial neural networks (ANN). IEEE Transactions on Industrial Electronics, 68(8) (2020) 7309–7319.

    Article  Google Scholar 

  38. S. K. Singh and A. Agarwal, “Implementation of artificial neural network-based control for power quality enhancement of proton exchange membrane fuel cell powered distributed generation system,” Physica Scripta, 98(5) (2023) 055005.

    Article  ADS  Google Scholar 

  39. B. Adineh, M.R. Habibi, A.N. Akpolat and F. Blaabjerg, “Sensorless voltage estimation for total harmonic distortion calculation using artificial neural networks in microgrids”, IEEE Trans. Circuits Systs. II Exp. Briefs, 68(7) (2021) 2583–2587.

    Google Scholar 

  40. G. Reikard, Predicting solar radiation at high resolutions: a comparison of time series forecasts. Sol. Energy., 83 (2009) 342–349.

    Article  ADS  Google Scholar 

  41. A.M. Nobre, C.A. Severiano, S. Karthik, M. Marek Kubis, L. Zhao, F.R. Martins, E.B. Pereira, R. Rüther and T. Reindl, “PV power conversion and short-term forecasting in a tropical densely-built environment in Singapore.” Renew Energy, 94 (2016) 496–509.

    Article  Google Scholar 

  42. R.E. Bird, ‘A simple, solar spectral model for direct-normal and diffusehorizontal irradiance.’ Sol. Energy, 32(4) (1984) 461–471.

    Article  ADS  Google Scholar 

  43. A. Dolara, F. Grimaccia, S. Leva, M. Mussetta and E. Ogliari, ‘A physical hybrid artificial neural network for short term forecasting of PV plantpower output.’ Energies, 8(2) (2015) 1138–1153.

    Article  MATH  Google Scholar 

  44. S. Leva, M. Mussetta and E. Ogliari, ‘PV module fault diagnosis based on microconverters and day-ahead forecast.’ IEEE Trans. Ind. Electron., 66(5) (2019) 3928–3937.

    Article  Google Scholar 

  45. A. Nespoli, M. Mussetta, E. Ogliari, S. Leva, L. Fernández-Ramírez and P. García-Triviño, ‘Robust 24 hours ahead forecast in a microgrid: A real case study.’ Electronics, 8(12) (2019) 1–13.

    Article  Google Scholar 

  46. I.V. Tetko, D.J. Livingstone and A.I. Luik, ‘Neural network studies. 1. Comparison of overfitting and overtraining.’ J. Chem. Inf. Comput. Sci., 35(5) (1995) 826–833.

    Article  Google Scholar 

  47. SolarTech Lab: Forecasting. Accessed: Sep. 1, 2021. http://www.solartech.polimi.it/activities/forecasting/

  48. A. Agarwal, S.K. Singh, T. Kanumuri and H. Kumar, Power quality improvement of photovoltaic distributed generation system using artificial neural network for environmental preservation. Journal of Engineering Research, 10 (2022) 1–15.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, National Institute of Technology, Delhi, 110036, India

    Shubham Kumar Singh & Anshul Agarwal

Authors
  1. Shubham Kumar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anshul Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anshul Agarwal.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, S.K., Agarwal, A. A Comparative Analysis of Artificial Neural Network Algorithms to Enhance the Power Quality of Photovoltaic Distributed Generation System Based on Metrological Parameters. MAPAN 38, 607–618 (2023). https://doi.org/10.1007/s12647-023-00649-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12647-023-00649-7

Keywords

Search

Navigation