APRIORI BASED MACHINE LEARNING IN POWER DISTRIBUTION NETWORK

Jasreen Kaur

Abstract


Power Distribution Networks (PDNs) are responsible for delivery of electricity from transmission substations to individual belonging. PDNs are still an open area of research and demand efficient load balancing algorithm. Although initially loads are balanced among three phase network but drift into unbalance state meeting ever growing and fluctuating demands. This imbalance in loads at different phases of system affects tool utilization, voltage ranges system stability and security .The comprehensive review has shown that the use of machine learning in not considered in PDNs. The issue of scalability has been ignored. This paper proposed Apriori based machine learning algorithm to predict the loads and schedule it in the optimum way. The comparisons have clearly shown that the effectiveness of the proposed technique over the existing one.

Keywords


load balancing; machine learning; Power Distribution Networks; phase swapping; feeder reconfiguration.

Full Text:

PDF


DOI: https://doi.org/10.26483/ijarcs.v9i2.5679

Refbacks

  • There are currently no refbacks.




Copyright (c) 2018 International Journal of Advanced Research in Computer Science