Wind turbine power curve estimation based on cluster center fuzzy logic modeling

Wind energy applications and turbine installation at different scales have been increased for last decade. Technically wind turbine capacity has been improved at high levels. However, electricity could not be generated at all stages of wind speed and so there are some limits related to cut-in and cut-out data. One of the main problems in wind engineering is to estimate output data of wind turbines depends on wind speed and system values. Wind speed problematic values, that are less than cut-in and greater than cut-out, take the most important role for estimating wind power curve models. All wind turbines have different cut-in and cut-out limits and generating of electricity could be achieved in a certain interval that could be called as affective interval. Fuzzy logic that is a new and novel verbal logical approach has many applications in the field of engineering. Cluster center fuzzy logic modeling is also a new and the effective method in this scientific area. In this paper, the first power curve of a wind turbine is modeled by least square methodology. After that depending on the fuzzy logic approach a new application is realized. It is seen that, this curve type could be well represented and modeled by the clustering center fuzzy logic modeling than classical least square methodology. It is estimated that four or five cluster centers are enough for representing wind turbine power curve by running proposed method. (c) 2008 Elsevier Ltd. All rights reserved.