Savas Karabulut

Geotechnical and Geophysical Studies for Wind Farms in Earhquake-Prone Areas

As Redlinger et al (2002) point out, since antiquity; people have used technology to transform the power of the wind into useful mechanical energy. Wind energy is accepted one of the world’s oldest forms of mechanic energy. The re-emergence of the wind as a significant source of the world’s energy must rank as one of the significant developments of the late 20th century (Manwell et al, 2009). Across the Earth’s surface, wind is in horizontal motion. Wind power is produced by differences in air pressure between two regions. Wind is a product of solar energy like most other forms of energy in use today. Wind is a clean, abundant, and renewable energy resource that can be tapped to produce electricity. Wind site assessments include: (1) high electricity rates, (2) rebates or tax credits from utilities or governments, (3) a good wind resource, and (4) a long-term perspective (Chiras, 2010). Procurement costs for critical components and subsystems are given in Table 1. The critical components of Wind Turbines include blades, rotor shaft, nacelle, gear box, generator, and pitch control unit. The tower, site foundation, and miscellaneous electrical and mechanical accessories are characterized as subsystem elements. As you can see in Table 1, medium percent cost of site and foundation is 17.3. For this reason, soil investigation should carefully be carried out for the wind energy systems.

A Review on Artificial Intelligence Based Parameter Forecasting for Soil-Water Content

The purpose of this study, by using an artificial intelligent approaches, is to compare a correlation between geophysical and geotechnical parameters. The input variables for this system are the electrical resistivity reading, the water content laboratory measurements. The output variable is water content of soils. In this study, our data sets are clustered into 120 training sets and 28 testing sets for constructing the fuzzy system and validating the ability of system prediction, respectively. Relationships between soil water content and electrical parameters were obtained by curvilinear models. The ranges of our samples are changed between 1 - 50 ohm.m (for resistivity) and 20 - 60 (%, for water content). An artificial intelligent system (artificial neural networks, Fuzzy logic applications, Mamdani and Sugeno approaches) are based on some comparisons about correlation between electrical resistivity and soil-water content, for Istanbul and Golcuk Soils in Turkey.