Table of Contents
Advances in Electrical Engineering
Volume 2016, Article ID 9760538, 13 pages
http://dx.doi.org/10.1155/2016/9760538
Research Article

Fuzzy Logic Controller Based Distributed Generation Integration Strategy for Stochastic Performance Improvement

1Mewar University, Chittorgarh, Rajasthan, India
2Malwa Institute of Technology, Indore, India

Received 15 July 2016; Accepted 12 October 2016

Academic Editor: Mamun B. Ibne Reaz

Copyright © 2016 Jagdish Prasad Sharma and H. Ravishankar Kamath. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In the restructured environment, distributed generation (DG) is considered as a very promising option due to a high initial capital cost of conventional plants, environmental concerns, and power shortage. Apart from the above, distributed generation (DG) has also abilities to improve performance of feeder. Most of the distribution feeders have radial structure, which compel to observe the impact of distributed generations on feeder performance, having different characteristics and composition of time varying static ZIP load models. Two fuzzy-based expert system is proposed for selecting and ranking the most appropriated periods to an integration of distributed generations with a feeder. Madami type fuzzy logic controller was developed for sizing of distributed generation, whereas Sugeno type fuzzy logic controller was developed for the DG location. Input parameters for Madami fuzzy logic controller are substation reserve capacity, feeder power loss to load ratio, voltage unbalance, and apparent power imbalances. DG output, survivability index, and node distance from substation are chosen as input to Sugeno type fuzzy logic controller. The stochastic performance of proposed fuzzy expert systems was evaluated on a modified IEEE 37 node test feeder with 15 minutes characteristics time interval varying static ZIP load models.