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Subject: search.filters.subject.Genetic Algorithm

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    Electric Vehicle Impact Analysis in a Microgrid Using Optimized Bio-Inspired Non-Integer Controller
    (IEEE, 2019) Mathur, Hari Om
    During peak hours of load demand in micro-grid (MG), the power frequency faces more excursion from a nominal value thereby deteriorating the power quality. The main idea is to utilize the EV power during peak load in to a MG system to limit the under frequency deviation and to charge the EV during off peak hours when the generation is surplus. This paper simulates a MG model in MATLAB/Simulink with EV as one of the sources using PID and fractional order PID (FOPID) controller to regulate the frequency deviation. The parameters of PID and FOPID controller are being optimized using genetic algorithm (GA) for improvement in the time response of frequency deviation of MG. The total energy model (TEM) of EV has been used for MG simulation. The optimized parameters obtained for PID, GAPID, FOPID and GAFOPID has been recorded for analysis. The GAFOPID based controller gives the better response for the defined objective function.
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    Temperature Optimization in Non-isothermal Tubular Reactor using Genetic Algorithm
    (IEEE, 2020) Pani, Ajaya Kumar
    Genetic algorithm (GA) is a heuristic search algorithm that is inspired by evolution. It is a powerful optimization tool that uses the stochastic procedure with populations of initial guesses rather than using a single value like gradient-based methods. This prevents GA from being trapped in a local optimum. In the present work, GA applications to industrial optimization problems are thoroughly reviewed to get a perspective on different variations of genetic algorithms being used in industries. Subsequently, GA is applied to an industrial tubular reactor system where the technique is used to determine the optimum feed temperature at reactor inlet so that the product attains desirable temperature at the reactor outlet. In addition to successful application of GA, some other performances such as effect of mutation function and selection technique on the number of iterations are also investigated.