Research on prediction of agricultural machinery total power based on grey model optimized by genetic algorithm

2009 ◽  
Author(s):  
Yan Xie ◽  
Mu Li ◽  
Jin Zhou ◽  
Chang-zheng Zheng
Keyword(s):  
Genetic Algorithm ◽  
Grey Model ◽  
Total Power ◽  
Agricultural Machinery

OPTIMIZATION OF THE C3MR CYCLE WITH GENETIC ALGORITHM

2010 ◽  
Vol 34 (3-4) ◽  
pp. 433-448 ◽  
Author(s):  
Hamidreza Taleshbahrami ◽  
Hamid Saffari
Keyword(s):  
Genetic Algorithm ◽  
Heat Exchange ◽  
Equation Of State ◽  
Heat Exchangers ◽  
Thermodynamic Simulation ◽  
Total Power ◽  
Simulation And Optimization ◽  
Best Value ◽  
Robinson Equation ◽  
Base Design

The aim of this paper is thermodynamic simulation and optimization of the C3MR system with Genetic Algorithm. For this purpose, in the first step, the Peng Robinson equation of state is simulated with a code in MATLAB and then used for simulating thermodynamic properties of natural gas and refrigerants that are used in the cycle. Following that the cycle is thermodynamically simulated and composite curves for subcooling and liquefaction heat exchangers are plotted. If composite curves in heat exchangers approach, the total power will decrease. Then, the total power used by the compressors is calculated. In the next step, the thermodynamic modeling is linked with Genetic Algorithm and the total power consumed by compressors is defined as objective function. The best value resulted from optimization has 23% lower power than the base design. In addition, heat exchange curves closed together.

scholarly journals Optimal sizing and location of multiple distributed generation for power loss minimization using genetic algorithm

2019 ◽  
Vol 16 (2) ◽  
pp. 956
Author(s):  
Abdulhamid Musa ◽  
Tengku Juhana Tengku Hashim
Keyword(s):  
Genetic Algorithm ◽  
Distributed Generation ◽  
Distribution System ◽  
Power Loss ◽  
Total Power ◽  
Power Losses ◽  
Loss Minimization ◽  
Power Constraints ◽  
Voltage Deviation ◽  
Power Limits

This paper presents a Genetic Algorithm (GA) for optimal location and sizing of multiple distributed generation (DG) for loss minimization. The study is implemented on a 33-bus radial distribution system to optimally allocate different numbers of DGs through the minimization of total active power losses and voltage deviation at power constraints of 0 – 2 MW and 0 – 3 MW respectively. The study proposed a PQ model of DG and Direct Load Flow (DLF) technique that uses Bus Incidence to Branch current (BIBC) and Branch Current to Bus Voltage (BCBV) matrices. The result obtained a minimum base case voltage level of 0.9898 p.u at bus 18 with variations of voltage improvements at other buses after single and multiple DG allocations in the system. Besides, the total power loss before DG allocation is observed as 0.2243 MW, and total power loss after DG allocation was determined based on the power constraints. Various optimal locations were seen depending on the power limits of different DG sizes. The results have shown that the impact of optimal allocation and sizing of three DG is more advantageous concerning voltage improvement, reduction of the voltage deviation and also total power loss in the distribution system. The results obtained in the 0 – 2 MW power limit is consistent to the 0 – 3 MW power limits regarding the influence of allocating DG to the network and minimization of total power losses.

Implement Multichannel Fractional Sample Rate Convertor using Genetic Algorithm

2017 ◽  
Vol 8 (2) ◽  
pp. 10-21
Author(s):  
Vivek Jain ◽  
Navneet Agrawal
Keyword(s):  
Genetic Algorithm ◽  
Power Consumption ◽  
Hamming Distance ◽  
Finite Impulse Response ◽  
Total Power ◽  
Dynamic Power ◽  
Total Power Consumption ◽  
Cmos Transistor ◽  
Sample Rate ◽  
Main Component

In this paper reduce power of multichannel fractional sample rate convertor by minimized hamming distance between consecutive coefficients of filter using Genetic algorithm. The main component of multichannel fractional sample rate convertor is Cascaded multiple architecture finite impulse response filter (CMFIR filter). CMFIR is implemented by cascading of cascaded integrator-comb (CIC) & multiply accumulate architecture (MAC) FIR filter. Genetic algorithm minimizes the hamming distance between consecutive coefficients of CMFIR filter. By Minimizing the hamming distance of consecutive filter coefficient reduces the transaction from 0 to 1 or 1 to 0. These techniques reduce the switching activity of CMOS transistor which is directly reduces Dynamic power consumption by multichannel sample rate convertor, it also minimizes the total power consumption of multichannel fractional sample rate convertor. later than use genetic algorithm on 1 to 128 channel Down sample rate convertor total power reduced by 3.44% to 61.56%, dynamic power reduced by 9.09% to 56.25% .1 to 128 channel Up sample rate convertor total power reduced by 2.81% to 45.42%, dynamic power reduced by 4.76% to 56%, 1 to 128 channel fractional sample rate convertor total power reduced by 1.44% to 17.17%, dynamic power reduced by 6.25% to 19.92%.

Multi-Objective Optimal Design of Automotive Engine Using Genetic Algorithm

1998 ◽  
Author(s):  
Kikuo Fujita ◽  
Noriyasu Hirokawa ◽  
Shinsuke Akagi ◽  
Shinji Kitamura ◽  
Hideaki Yokohata
Keyword(s):  
Genetic Algorithm ◽  
Engineering Design ◽  
Design Problem ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Total Power ◽  
Design Problems ◽  
Automotive Engine ◽  
Multi Objective ◽  
Engineering Design Problems

Abstract A genetic algorithm based optimization method is proposed for a multi-objective design problem of an automotive engine, that includes several difficulties in practical engineering optimization problems. While various optimization techniques have been applied to engineering design problems, a class of realistic engineering design problems face on a mixture of different optimization difficulties, such as the rugged nature of system response, the numbers of design variables and objectives, etc. In order to overcome such a situation, this paper proposes a genetic algorithm based multi-objective optimization method, that introduces Pareto-optimality based fitness function, similarity based selection and direct real number crossover. This optimization method is also applied to the design problem of an automotive engine with the design criteria on a total power train. The computational examples show the ability of the proposed method for finding a relevant set of Pareto optima.

A Genetic Algorithm-Based Multivariate Grey Model in Housing Demand Forecast in Turkey

2020 ◽  
pp. 476-500
Author(s):  
Miraç Eren ◽  
Ali Kemal Çelik ◽  
İbrahim Huseyni
Keyword(s):  
Genetic Algorithm ◽  
Growth And Development ◽  
Consumer Price Index ◽  
Housing Demand ◽  
Grey Model ◽  
Demand Forecast ◽  
Factors Affecting ◽  
Urbanization Rate ◽  
Housing Investments ◽  
Demand Forecasts

Housing sector is commonly considered as a very strong economic industry in terms of both its contribution to creating employment and its impact on other associated sectors. By means of its featured characteristics, the sector also plays an important role on economic growth and development of emerging countries. In this respect, any evidence that determines factors affecting housing investments and future demand behavior may be remarkably valuable for monitoring possible future excess supply and deficits. This chapter attempts to determine factors affecting housing demand in Turkey during a sample period of 2003-2011 using a genetic algorithm-based multivariate grey model. Housing demand forecasts are also employed until the year 2020. Results reveal that several factors including M2 money supply, consumer price index and urbanization rate have an impact on housing demand. According to housing demand forecasts, a significant housing demand increase is expected in Turkey.

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