Application of Neural Networks in Power Quality Academic Projects in Electrical

2015 International Conference on Soft Computing Techniques and Implementations- (ICSCTI)

 ABSTRACT: Use of power electronic converters with nonlinear loads produces harmonic currents and reactive power. A shunt active power filter provides an elegant solution to reactive power compensation as well as harmonic mitigation leading to improvement in power quality. However, the shunt active power filter with PI type of controller is suitable only for a given load. If the load is varying, the proportional and integral gains are required to be fine tuned for each load setting. The present study deals with neural network based controller for shunt active power filter. The performance of neural network controller evaluated and compared with PI controller.


  1. Active Power Filter
  2. Neural Networks
  3. Back Propagation Algorithm
  4. Soft Computing.



Schematic Diagram of Shunt Active Power Filter

Fig1. Schematic Diagram of Shunt Active Power Filter




Fig 2. (a) Waveform of Load Current, Compensating Current, Source Current and Source Voltage for 1kVA with 􀄮=60º and (b) Waveform of Source Voltage and in the phase Source Current of Fig. (a)



The active power filter controller with neural network based controller has been seen to eminently minimize harmonics in the source current when the load demands non sinusoidal current, irrespective of whether the load is fixed or varying. Simultaneously, the power factor at source also becomes the unity, if the load demands reactive power. Thus, neural network based controller is far superior to PI type of controller which requires fine tuning of Kp and Ki every time the load changes. In the present work, the performance of a range of values of the load is considered to robustly test the controller. It has been demonstrated that neural network based controller, therefore, significantly improves the performance of a shunt active power filter.



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