Optimization of the Active Composition of the Wind Farm Using Genetic Algorithms

The article presents the results of research on the possibilities of using genetic algorithms for solving the multicriteria optimization problem of determining the active components of a wind farm.Optimization is carried out on two parameters:efficiency factor of wind farm use(integrated parameter calculated on the basis of 6 parameters of each of the wind farm),average power deviation level(average difference between the load power and energy generation capabilities of the active wind farm).That was done an analysis of publications on the use of genetic algorithms to solve multicriteria optimization problems.Computer simulations were performed,which allowed us to analyze the obtained statistical data and determine the main optimization indicators.That was carried out a comparative analysis of the obtained results with other methods,such as the dynamic programming method;the dynamic programming method with the general increase of the set loading;the modified dynamic programming method,neural networks.It is established that the average power deviation for the genetic algorithm and for the modified dynamic programming method is located at the same level,33.7 and 28.8 kW,respectively.The average value of the efficiency coefficient of wind turbine used for the genetic algorithm is 2.4%less than for the modified dynamic programming method.However,the time of finding the solution by the genetic algorithm is 3.6 times less than for the modified dynamic programming method.The obtained results provide an opportunity to implement an effective decision support system in energy flow management.

Smart agriculture: a literature review

Industry 4.0 brings revolutionary changes to farming businesses by integrating emerging technologies such as the Internet of things(IoT),big data analytics(BDA),cloud computing(CC),and artificial intelligence(AI).These Emerging technologies are the potential enablers of data-driven smart farming.Realizing the importance of data-driven agriculture,we provide a complete picture of current literature in smart agriculture by using a review classification framework divided into four categories:(i)Smart Farming Activities,(ii)BDA Levels,(iii)BDA Models,and(iv)BDA Techniques.This work uses the preferred reporting items for systematic reviews(PRISMA)methodology to review the current literature on intelligent farming.A total of 90 papers have been identified,and content analysis was conducted to mine knowledge in the domain for 2011–2022.The primary intention of this review is to clarify the most prominent farming activity,level of analytics,BDA models,and techniques in smart farming.Finally,the findings of our review analysis are discussed,and work suggestions are addressed for further research.