Evaluation on Training Algorithms of Back Propagation Neural Network for a Solar Photovoltaic Based DSTATCOM System

  • Nor Hanisah BaharudinEmail author
  • Tunku Muhammad Nizar Tunku Mansur
  • Rosnazri Ali
  • Muhammad Irwanto Misrun
Part of the Power Systems book series (POWSYS)


This chapter discusses evaluation on the Back Propagation Neural Network (BPNN) control algorithm based on Fast Fourier Transform (FFT) control algorithm with different BPNN training algorithms for Distribution Static Compensator (DSTATCOM) with integrated solar photovoltaic system. Furthermore, the comparison is performed with different weight or bias training functions such as supervised and unsupervised. Each training algorithms have been utilized to investigate its performance in generating the target pattern for harmonic elimination in term of accuracy, learning epochs and training time. The performance of the BPNN training algorithms is determined by calculating the error between the target and output pattern using Mean Squared Error (MSE). The lower value of the MSE shows the higher accuracy of the output pattern according to the target pattern given. Number of iterations (epochs) and training time are evaluated to investigate the performance of different BPNN training algorithms on DSTATCOM for harmonic reduction under nonlinear load condition.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Nor Hanisah Baharudin
    • 1
    Email author
  • Tunku Muhammad Nizar Tunku Mansur
    • 1
  • Rosnazri Ali
    • 1
  • Muhammad Irwanto Misrun
    • 1
  1. 1.School of Electrical System EngineeringUniversiti Malaysia Perlis (UniMAP)ArauMalaysia

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