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Adaptive Neural Network-Based LMS for DSTATCOM

  • Vivekananda GanjiEmail author
  • D. Suresh
  • K. Chandrasekhar Koritala
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 65)

Abstract

This paper presents distributed static compensator (DSTATCOM) to eliminate harmonics and reactive power of the nonlinear load. The DSTATCOM is realized using voltage source inverter with DC bus capacitor. The DSTATCOM acts as a harmonics current source and inject reactive compensation current. The control schemes for determining the reference compensating currents of the three-phase DSTATCOM based on least mean square (LMS) algorithms are presented. The performance of LMS algorithm with PI and artificial neural network controller (ANN) is studied. The algorithm used for training ANN controller is Levenberg–Marquardt backpropagation (LMBP). The training data for ANN controller is generated offline. The firing pulses for DSTATCOM are obtained with hysteresis current controller. An extensive simulation study is carried out to test the performance of ANN controller and compared with PI controller.

Keywords

PI controller LMS Active power filter Harmonic 

References

  1. 1.
    Singh B, Verma V (2008) Selective compensation of power-quality problems through active power filter by current decomposition, power delivery. IEEE Trans Power Deliv 23(2):792–799CrossRefGoogle Scholar
  2. 2.
    Gayadhar P, Ray PK, Puhan PS, Dash SK (2013) Novel schemes used for estimation of power system harmonics and their elimination in a three-phase distribution system. Int J Electr Power Energy Syst 53:842–856CrossRefGoogle Scholar
  3. 3.
    Saad S, Zellouma L (2009) Fuzzy logic controller for three-level shunt active filter compensating harmonics and reactive power. Electric Power Syst Res 79(10):1337–1341CrossRefGoogle Scholar
  4. 4.
    Premalatha S, Dash SS, Babu PC (2013) Power quality improvement features for a distributed generation system using shunt active power filter. In Procedia Eng, ICONDM 2013 64:265–274CrossRefGoogle Scholar
  5. 5.
    Singh GK, Singh AK, Mitra RA (2007) Simple fuzzy logic based robust active power filter for harmonics minimization under random load variation. Electric Power Syst Res 77(8):101–111CrossRefGoogle Scholar
  6. 6.
    Panda AK, Mikkili S (2013) FLC based shunt active filter (p–q and Id–Iq) control strategies for mitigation of harmonics with different fuzzy MFs using MATLAB and real-time digital simulator. Int J Electr Power Energy Syst 47:313–336CrossRefGoogle Scholar
  7. 7.
    Singh B, Solanki J (2006) A comparative study of control algorithms for DSTATCOM for load compensation. In Proc IEEE ICIT, 15–17 Dec 2006, pp 1492–1497Google Scholar
  8. 8.
    Singh B, Verma V, Solanki J (2007) Neural network-based selective compensation current quality problems in distribution system. IEEE Trans Ind Electron 54(1):53–60CrossRefGoogle Scholar
  9. 9.
    Widrow B, McCool JM, Ball M (1975) The complex LMS algorithm. Proc IEEE 63(4):719–720CrossRefGoogle Scholar
  10. 10.
    Suresh D, Singh SP (2016) Design of single input fuzzy logic controller for shunt active power filter. IETE J Res 61(5):500–509CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Vivekananda Ganji
    • 1
    Email author
  • D. Suresh
    • 2
  • K. Chandrasekhar Koritala
    • 3
  1. 1.Department of Electrical and Electronics EngineeringAcharya Nagarjuna UniversityGunturIndia
  2. 2.Department of Electrical and Electronics EngineeringVignan Institute of Technology and ScienceHyderabadIndia
  3. 3.Department of Electrical and Electronics EngineeringRVR&JCGunturIndia

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