FUZZY BASED EXPERT SYSTEM FOR RENEWABLE ENERGY MANAGEMENT

The aim of this work is determine the most appropriated period for connecting a particular generation source fuelled by biogas on a distribution network. The main electrical characteristics of the network are evaluated. The proposed simulations provided data for analyzing the quantitative parameters – voltage levels, power losses and load current. A group of decision makers was selected for establishing scores applied to the qualitative parameter evaluation – availability of ancillary services support – according to each period in analysis. The fuzzy-based expert system is then applied for selecting and ranking the most appropriated period for connecting the distributed generation source. The definition of the ranking is the outcome according to the final priorities – quantitative and qualitative analysis. .

[1]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[2]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[3]  Marina Yusoff,et al.  Intelligent Water Dispersal Controller: Comparison between Mamdani and Sugeno Approaches , 2007 .

[4]  José Miguel Mantas,et al.  Extraction of similarity based fuzzy rules from artificial neural networks , 2006, Int. J. Approx. Reason..

[5]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[6]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control - design and stability analysis , 1994 .

[7]  J. B. Kiszka,et al.  The influence of some fuzzy implication operators on the accuracy of a fuzzy model-part II , 1985 .

[8]  J. Buckley,et al.  Fuzzy expert systems and fuzzy reasoning , 2004 .

[9]  Alexander E. Gegov,et al.  Advanced Inference in Fuzzy Systems by Rule Base Compression , 2007, EUSFLAT Conf..

[10]  Jesús Alcalá-Fdez,et al.  Genetic learning of accurate and compact fuzzy rule based systems based on the 2-tuples linguistic representation , 2007, Int. J. Approx. Reason..

[11]  Luciane Neves Canha,et al.  A Novel Fuzzy-Based Methodology for Biogas Fuelled Hybrid Energy Systems Decision Making , 2011, Soft Computing in Green and Renewable Energy Systems.

[12]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[13]  N. Georganas,et al.  A comparison of Mamdani and Sugeno fuzzy inference systems for evaluating the quality of experience of Hapto-Audio-Visual applications , 2008, 2008 IEEE International Workshop on Haptic Audio visual Environments and Games.

[14]  Jernej Virant Design Considerations of Time in Fuzzy Systems , 1999 .

[15]  Dr. Hans Hellendoorn,et al.  An Introduction to Fuzzy Control , 1996, Springer Berlin Heidelberg.