Total views : 204

Artificial Neural Network based Harmonics Estimator for a Power Electronics Converter


  • Department of EEE, NIT Puducherry, Karaikal - 609609, Puducherry, India
  • Department of EEE, MVIT, Kalitheerthalkuppam - 605107, Puducherry, India
  • Department of EEE, SRM University, Chennai - 603203, Tamil Nadu, India


Objectives: This paper presents harmonics estimation using Artificial Neural Network (ANN) for a 2 pulse Un-controlled power electronics converter. Methods/Analysis: Feed-forward architecture is chosen to model ANN-based Harmonics Estimator. The Feedforward architecture trained with various Learning algorithms is investigated. The suitable ANN model is identified. The performance of ANN based harmonics estimator is compared with conventional Fourier series method. Findings: The feed-forward architecture trained with LM algorithm is identified to be suitable for harmonics estimation in 2-pulse uncontrolled rectifier. Novelty/Improvement: The suitability of feed-forward architecture with different learning algorithms is investigated which is novel in this paper.


Artificial Neural Networks, Estimator, Feed-Forward Neural Architectures, Harmonics, Learning Algorithms, Power Electronics Converters, 2-Pulse Controlled Rectifier.

Full Text:

 |  (PDF views: 203)


  • Venkatesh C, Kumar DS, Sarma DVSSS, Sydulu M. Modelling of nonlinear loads and estimation of harmonics in industrial distribution system. Fifteenth National Power Systems Conference (NPSC); IIT Bombay. 2008. p. 592–7.
  • Rrezvani F, Mozafari B, Faghihi F. Power quality analysis for photovoltaic system considering unbalanced voltage. Indian Journal of Science and Technology. 2015 Jul; 8(14). DOI: 10.17485/ijst/2015/v8i14/60194
  • Ravi M. Comparison of PV supported DVR and DSTATCOM with multiple feeders in standalone WECS by mitigating power quality problems. Indian Journal of Science and Technology. 2015 Jul; 8(15). DOI: 10.17485/ijst/2015/v8i15/54089.
  • Srinivasarao B, Sreenivasan G, Sharma S. Comparison of facts controller for power quality problems in power system. Indian Journal of Science and Technology. 2015 Nov; 8(31). DOI: 10.17485/ijst/2015/v8i1/76302.
  • Sahinya KS, Sneha R, Srilakshmi M. Neural network based harmonic estimation of non-linear loads in power system applications [BTech thesis]. SRM University; 2012.
  • Almaita E, Asumadu JA. On-line harmonic estimation in power system based on sequential training radial basis function neural network. IEEE International Conference on Industrial Technology (ICIT); 2011. p. 139–44.
  • Sekaran EC, Anbalagan P. Comparison of neural network and fast fourier transform based selective harmonic extraction and total harmonic reduction for power electronic converters. Asian Power Electronics Journal. 2008; 2(1):1–9.
  • Dehini R, Bassou A, Ferdi B. The harmonics detection method based on neural network applied to harmonics compensation. Asian Power Electronics Journal. 2010; 2(5):258–67.
  • Jain SK, Singh SN. Low-order dominant harmonic estimation using adaptive wavelet neural network. IEEE Transaction on Industrial Electronics. 61(4); 2014:428–35.
  • Valtierra-Rodriguez M, Osornio-Rios RA, Garcia-Perez A, de Jesus Romero-Troncoso R. FPGA-based neural network harmonic estimation for continuous monitoring of the power line in industrial applications. Elsevier Journal on Electric Power Systems Research. 2013; 98:51–7.
  • do Nascimento CF, de Oliveira AA, Goedtel A, da Silva IN. Neural network-based approach for identification of the harmonic content of a nonlinear load in a single-phase system. IEEE Latin America Transactions. 2010; 8(1):65–73.
  • Venkadesan A, Himavathi S, Muthuramalingam A. Performance comparison of neural architectures for on-line flux estimation in sensor-less vector controlled IM Drives. Springer Journal on Neural Computing and Applications. 2013; 22(7-8):1735–44.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.