A novel hybrid optimization methodology to optimize the total number and placement of wind turbines

Due to increasing penetration of wind energy in the recent times, wind farmers tend to generate increasing amount of energy out of wind farms. In order to achieve the target, many wind farms are operated with a layout design of numerous turbines placed close to each other in a limited land area leading to greater energy losses due to ‘wake effects’. Moreover, these turbines need to satisfy many other constraints such as topological constraints, minimum allowable capacity factors, inter-turbine distances, noise constraints etc. Thus, the problem of placing wind turbines in a farm to maximize the overall produced energy while satisfying all constraints is highly constrained and complex. Existing methods to solve the turbine placement problem typically assume knowledge about the total number of turbines to be placed in the farm. However, in reality, wind farm developers often have little or no information about the best number of turbines to be placed in a farm. This study proposes a novel hybrid optimization methodology to simultaneously determine the optimum total number of turbines to be placed in a wind farm along with their optimal locations. The proposed hybrid methodology is a combination of probabilistic genetic algorithms and deterministic gradient based optimization methods. Application of the proposed method on representative case studies yields higher Annual Energy Production (AEP) than the results found by using two of the existing methods

Incremental multiple objective genetic algorithms

This paper presents a new genetic algorithm approach to multi-objective optimization problemsIncremental Multiple Objective Genetic Algorithms (IMOGA). Different from conventional MOGA methods, it takes each objective into consideration incrementally. The whole evolution is divided into as many phases as the number of objectives, and one more objective is considered in each phase. Each phase is composed of two stages: first, an independent population is evolved to optimize one specific objective; second, the better-performing individuals from the evolved single-objective population and the multi-objective population evolved in the last phase are joined together by the operation of integration. The resulting population then becomes an initial multi-objective population, to which a multi-objective evolution based on the incremented objective set is applied. The experiment results show that, in most problems, the performance of IMOGA is better than that of three other MOGAs, NSGA-II, SPEA and PAES. IMOGA can find more solutions during the same time span, and the quality of solutions is better

Controls and guidance research

The objectives of the control group are concentrated on research and education. The control problem of the hypersonic space vehicle represents an important and challenging issue in aerospace engineering. The work described in this report is part of our effort in developing advanced control strategies for such a system. In order to achieve the objectives stated in the NASA-CORE proposal, the tasks were divided among the group based upon their educational expertise. Within the educational component we are offering a Linear Systems and Control course for students in electrical and mechanical engineering. Also, we are proposing a new course in Digital Control Systems with a corresponding laboratory

Differential Evolution Optimization Technique to Design Gear Train System

A high performance mechanical power transmission system needs least weight, minimum centre to centre distance and higher strength to maintain its performance. In the present paper the gear problem is solved by minimizing volume, centre to centre distance and maximizing gear strength of gear trains since they are crucial parameters of the gear design problem. Conventional optimisation techniques cannot be used to optimise multi- objective function with constraints easily. The expectation from a desired optimisation are it should find a true global minimum, convergence should be fast, have a minimum number of control parameters, simple and efficient to utilise. Differential evolution optimisation, a simple and effective technique for global optimisation over incessant space, doesn’t need the function have to be continuous or differential as usually required by classical optimization. Some system parameters represented as vector are chosen, are decision variables. a multi objective function taking into consideration of module, width factor, number of teeth like its parametric vector or decision variable. DE is a population based optimisation technique, tries to improve a candidate solution iteratively, accepts a solution vector and uses the formula in order to derive a new candidate solution from the existing candidates and find out the best function value from the existing functions by comparing. Penalty function is incorporated in order to handle constraints