@article{MAKHILLIJEPE20126225277,
    title = {Fuzzy Bang-Bang Control with ANN Based Voltage Flicker Mitigation Using DVR},
    journal = {International Journal of Electrical and Power Engineering},
    volume = {6},
    number = {2},
    pages = {88-93},
    year = {2012},
    issn = {1990-7958},
    doi = {ijepe.2012.88.93},
    url = {https://makhillpublications.co/view-article.php?issn=1990-7958&doi=ijepe.2012.88.93},
    author = {T. Ruban Deva,L. Padma and},
    keywords = {Lyapunov function analysis,DVR,fuzzy bang-bang control,Voltage flicker,artificial neural network,India},
    abstract = {The quality of electric power is of supreme importance to 
  electrical utilities and their customers. Modern equipments are more sensitive 
  to power system anomalies than in the past. Microprocessor based controls and 
  power electronics devices are sensitive to many types of disturbances. Voltage 
  flicker is caused by loads that exhibit continuous, rapid variations in load 
  current. The phenomenon of flickering has been known since the introduction 
  of power supply networks. However, it grew rapidly along with the increase in 
  the number of loads and the increase in the power consumed. Electric arc furnace 
  is the main generator of voltage flicker which affects the performance of other 
  sensitive loads connected with the system. Hence, mitigation of voltage flicker 
  becomes inevitable. FACTS devices have been gradually introduced for voltage 
  flicker compensation. Dynamic Voltage Restorer (DVR) has been widely used to 
  mitigate voltage flicker. The DVR with series active compensation capability 
  opposed to variations of the arc resistance and suppress voltage flicker at 
  the source. The control strategy adopted to mitigate flicker in an effective 
  and robust manner is the key issue. A scheme based on fuzzy bang-bang control 
  with ANN is proposed for flicker mitigation using DVR in this study. Two dimensional 
  fuzzy control rules are framed based on Lyapunov function analysis and selection 
  of control rule with best response to current state is done using Artificial 
  Neural Network (ANN). Using the proposed control algorithm, the DVR will contribute 
  to the mitigation of flicker without deteriorating the effect of the other compensating 
  devices. The control algorithm is simulated on a power system model with arc 
  furnace load. Numerical simulations show the effectiveness of the controller 
  in compensating voltage flicker.}
    }