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Islanding detection for PV and DFIG using decision tree and AdaBoost algorithm

Madani, S. S ; Sharif University of Technology | 2012

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  1. Type of Document: Article
  2. DOI: 10.1109/ISGTEurope.2012.6465818
  3. Publisher: 2012
  4. Abstract:
  5. Under smart grid environment, islanding detection plays an important role in reliable operation of distributed generation (DG) units. In this paper an intelligent-based islanding detection algorithm for PV and DFIG units is proposed. Decision tree algorithm is used to classify islanding detection instances. This algorithm is rapid, simple, intelligible and easy to interpret. The error rate of this method is reduced by Adaptive Boosting (AdaBoost) technique. The proposed method is tested on a distribution system including PV, DFIG and synchronous generator. Probable events in the system are simulated under diverse operating states to generate classification data set. First and second order derivatives of locally measured electrical parameters are used for construction of 16-dimensional instances. The results indicate that Adaboost technique yields improved islanding detection accuracy. This algorithm is capable of detecting islanding phenomenon under operating states with negligible power mismatch
  6. Keywords:
  7. Adaptive Boosting (AdaBoost) ; Decision tree ; Distributed generation (DG) ; Non-detection zone (NDZ) ; AdaBoost algorithm ; Data set ; Decision-tree algorithm ; Distributed generation units ; Distribution systems ; Electrical parameter ; Error rate ; Islanding detection ; Islanding phenomenon ; Non detection zones ; Operating state ; Power mismatch ; Reliable operation ; Second order derivatives ; Smart grid ; Algorithms ; Artificial intelligence ; Classification (of information) ; Data mining ; Decision trees ; Distributed power generation ; Electric fault currents ; Electric power transmission networks ; Smart power grids ; Synchronous generators ; Adaptive boosting
  8. Source: IEEE PES Innovative Smart Grid Technologies Conference Europe ; 2012 ; 9781467325974 (ISBN)
  9. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6465818