Improving Power Harvesting Ability of Variable Speed Wind Turbine using Intelligent Soft Computing Technique

This paper presents a study carried out on maximizing energy harvesting of wind turbines. One way of improving the output power of the wind turbines is by optimizing the power conversion coefficient. The power conversion coefficient factor is expressed as a function of the wind turbine blade tip speed ratio and the turbine blade pitch angle. Optimization of the wind turbine generator output power is done by considering the effects of variations of wind speed, blade tip speed ratio, and pitch angle. An intelligent soft computing technique known as an adaptive neuro-fuzzy inference system (ANFIS) with a fuzzy logic controller for blade pitch actuator was applied to optimize the generator output power. The simulation result showed that the power conversion coefficient of 0.513 is achieved. The study was verified by using real-time wind speed data of Adama II wind farm in Ethiopia and specifications of the Gamesa G80 horizontal axis wind turbine generator unit by MATLAB software. Accordingly, a promising and satisfying improvement in power harvesting capacity is obtained. The output power of this generator is improved by 9.47% which is by far better result as compared to the existing literature.

Reliability analysis of soil slope stability using ANFIS soft computing technique

Soil is a heterogeneous medium and due to which the parameters on which soil slope stability depends are having high variability, which makes the analysis a complex problem. With time to take into account the variability in soil parameter the research approach has shifted from deterministic approach towards the probabilistic approach. This paper describes the application of probabilistic approach using soft-computing technique i.e. Adaptive Network Fuzzy Inference System (ANFIS) to study the soil slope reliability based on slope stability. The slope stability of soil depends on the parameters c (cohesion), ? (angle of shearing resistance) and ? (unit weight), which are taken as input variables and Factor of Safety of soil slope (FOS) as output. Also the model performance assessed using performance indices i.e. R2, VAF, MAPE, RPD, RMSE etc. The results analysis showed that ANFIS performed good. Therefore, ANFIS can be used as reliable soft computing technique for analyzing slope stability of soil.

Analysis of Beam-Column Designs by Varying Axial Load with Internal Forces and Bending Rigidity Using a New Soft Computing Technique

Design problems in structural engineering are often modeled as differential equations. These problems are posed as initial or boundary value problems with several possible variations in structural designs. In this paper, we have derived a mathematical model that represents different structures of beam-columns by varying axial load with or without internal forces including bending rigidity. We have also developed a novel solver, the LeNN-NM algorithm, which consists of weighted Legendre polynomials, and a single path following optimizer, the Nelder–Mead (NM) algorithm. To evaluate the performance of our solver, we have considered three design problems representing beam-columns. The values of performance indicators, MAD, TIC, NSE, and ENSE, are calculated for a hundred simulations. The outcome of our statistical analysis points to the superiority of the LeNN-NM algorithm. Graphical illustrations are presented to further elaborate on our claims.