Uludağ University Journal of The Faculty of Engineering, Vol. 24, No. 1, 2019 RESEARCH DOI: 10.17482/uumfd.506766 BUCK, BOOST AND BUCK-BOOST CONVERTER DESIGNS WITH VARIOUS METAHEURISTIC METHODS * Fahri VATANSEVER * Yiğit Çağatay KUYU Received: 02.01.2019 ; revised: 27.03.2019 ; accepted: 03.04.2019 Abstract: One of the basic circuit structures in the field of power electronics is DC-DC converters. As these design steps require many mathematical operations, these problems are hard to solve by hand. In addition, choosing the proper component values is always curial when adopting the computer-based designs to the real-world. In this study, the software is developed for the designs of buck, boost and buck- boost DC-DC converters via metaheuristic algorithms that calculate the parameters of the circuits. The components of the specified DC-DC converters are selected via the software with a user-friendly interface, under the desired criteria from the industrial series (E12, E24 and E96), by using eight different metaheuristic algorithms (artificial bee colony, differential evolution, genetic algorithm, particle swarm optimization, cuckoo search, harmony search, lightning search and gray wolf optimizer). The designs and analyses of DC-DC converters that are chosen according to the type and features (determining/selecting the components in accordance with the specified industrial series) can perform easily, fast and effectively through the software developed for this purpose. Keywords: Power electronic, DC-DC converter, metaheuristic algorithms. Çeşitli Metasezgisel Yöntemlerle Alçaltan, Yükselten ve Alçaltan-Yükselten Dönüştürücü Tasarımları Öz: Güç elektroniği alanındaki temel devre yapılarından birisi DA-DA (DC-DC) dönüştürücüleridir. Farklı türleri olan bu devrelerin tasarım aşamalarında, elle çözümü zor olan birçok matematiksel işlemler gerekmektedir. Ayrıca bilgisayar tabanlı tasarımları; gerçek dünyaya uyarlarken, uygun bileşen değerlerinin seçilmesi her zaman çok önemlidir. Gerçekleştirilen çalışmada; alçaltan, yükselten ve alçaltan-yükselten DA-DA dönüştürücü devrelerin tasarımı için metasezgisel algoritmalarla hesaplamaları gerçekleştiren yazılım geliştirilmiştir. Kullanıcı dostu arayüze sahip yazılım ile seçilen türdeki DA-DA dönüştürücü devre elemanları, istenilen endüstriyel serilere (E12, E24 ve E96) uygun olarak sekiz farklı metasezgisel algoritma (yapay arı kolonisi, diferansiyel gelişim, genetik algoritma, parçacık sürü optimizasyonu, guguk kuşu arama, harmoni arama, yıldırım arama ve gri kurt optimizasyonu) ile kolay, hızlı ve etkin bir şekilde elde edilmektedir. Anahtar Kelimeler: Güç elektroniği, DC-DC dönüştürücü, metasezgisel algoritmalar. 1. INTRODUCTION DC-DC converter circuits, which carry a certain level of DC voltage to an another level (boosting or stepping down of voltage), are very important in the field of power electronics. The main design steps can be summarized: achieving the desired voltage level at the output, * Bursa Uludag University, Faculty of Engineering, Electrical-Electronics Eng. Dept., 16059 Bursa/Turkey Corresponding author: Fahri Vatansever (fahriv@uludag.edu.tr) 383 Vatansever F.,Kuyu Y.Ç.: Buck, Boost and Buck-Boost Converter Designs with Metaheuristic Methods calculation of the components with different topologies of the circuits for minimum voltage fluctuation, determination of the critical components of the circuit for a continuous current and selection of the optimal component values in accordance with the industrial series. Circuit components calculated by solving the design steps consisted of the systems of equations with multiple variables may not be existed in the industrial series. This requires all calculations to be made again by selecting the appropriate industrial series. It is challenging and time-consuming to perform all the design steps by hand as the complexities of the problem rise. Metaheuristic algorithms are able to solve these kinds of the problems quickly (León-Aldaco et al., 2015). In addition, as conventional methods require derivative information and are highly sensitive to initial points in some cases, metaheuristic algorithms are one step ahead of them in solving complex problems. In these optimization processes, metaheuristic algorithms can be utilized effectively and efficiently (Simon, 2013; Price et al., 2005; Dasgupta, 1997; Yang, 2014; Yang, 2010; Kuyu and Vatansever, 2016; Kuyu and Vatansever, 2016; Vatansever et al., 2015). The basic properties and advantages of metaheuristics can be found in the related studies (Vasant, 2012; Du and Swamy, 2016). In this work, artificial bee colony (ABC) (Karaboga, 2005), differential evolution (DE) (Storn and Price, 1995), genetic (GA) (Goldberg, 1989), particle swarm optimization (PSO) (Kennedy and Eberhart, 1995), cuckoo search (CS) (Yang and Deb, 2009), harmony search (HS) (Geem et al., 2001), lightning search (LSA) (Shareef et al., 2015) and grey wolf optimizer (GWO) (Mirjalili et al., 2014) algorithms are used for the selection of the optimal component values of the DC-DC buck, boost and buck-boost converters. The component values of the converter circuits in the desired property can be calculated/chosen according to the industrial series via the metaheuristic algorithms easily, fast and effectively. The main objectives of this study are to reduce the time of design processes and to find proper component values automatically by combining a lot of metaheuristics with a software. Thus the most economical and convenient circuits can be designed according to available components. This paper is organized as follows: DC-DC converters and the used metaheuristic algorithms are briefly described in Section 2-3. The designed simulator and its applications (results) are given in Section 4 and finally, the study conclusions are detailed in Section 5. 2. DC-DC CONVERTERS The DC-DC converters changing the DC voltage levels at the input and output can be divided into three groups:  Buck (step-down): DC/DC switching voltage regulator that reduces the input voltage.  Boost (step-up): DC/DC switching voltage regulator that increases the input voltage.  Buck-boost: DC/DC switching voltage regulator that reduces or increases the input voltage. The main topologies and some equations of the converters are summarized in Table 1 (Rashid, 2013; Gürdal, 2008). 3. METAHEURISTIC ALGORITHMS Metaheuristic algorithms are inspired from various methodologies, such as biologic and social behaviors in the nature, as in the artificial bee colony, genetic, differential evolution, particle swarm optimization algorithms, or physic-based like the lightning search algorithm. These algorithms are frequently used in the field of optimization related to social and engineering sciences. The pseudo codes of the algorithms used in this study are also summarized in Table 2 (Karaboga, 2005; Storn and Price, 1995; Goldberg, 1989; Kennedy and Eberhart, 1995; Yang and Deb, 2009; Geem et al., 2001; Shareef et al., 2015; Mirjalili et al., 2014). 384 Uludağ University Journal of The Faculty of Engineering, Vol. 24, No. 1, 2019 Table 1. DC-DC converters DC-DC CONVERTER Buck Boost Buck-Boost Type Circuit Parameter Equation Duty ratio iS S i L L iR Minimum current { } iD iC E D C R Maximum current { } Critical inductance Voltage fluctuation Duty ratio L D i L iD iR Minimum current iS iC E S C R Maximum current Critical inductance Voltage fluctuation Duty ratio S D i i i S D R i Minimum current L iC E L C R Maximum current Critical inductance Voltage fluctuation 4. APPLICATIONS In this work, a graphical user interface was designed using MATLAB (MathWorks, 2013) to run the applications. The component values of the DC-DC converters for the critical/minimum continuous current condition are found by using ABC, DE, GA, PSO, CS, HS, LSA and GWO in the program designed. The flowchart and the main screenshot of the program can be shown in Figures 1-2, respectively. Start Select type of DC-DC converter Input desired properties of converter Select series type Compute the circuit components according to type of selected series with ABC, DE, GA, PSO, CS, HS, LSA and GWO Show results Stop Figure 1: Basic flow-chart of the designed program 385 Buck-Boost Boost Buck Vatansever F.,Kuyu Y.Ç.: Buck, Boost and Buck-Boost Converter Designs with Metaheuristic Methods Table 2. The algorithms used in this work Algorithm Years Developers Sample pseudo code  Generate initial population  While (stopping criterion) o Produce new solutions (food source positions) o Apply the greedy selection process o Calculate the probability values for the solutions by means of their fitness values o Produce the new solutions (new positions) o Apply the greedy selection process o Determine the abandoned solution (source) o Memorize the best solution (food source position)  end while  Generate initial population  While (stopping criterion) o For each individual  Compute a mutant vector  For each dimension  Generate a trial vector by using recombination operator  end for  Select and update the solution o end for  end while  Generate initial population  While (stopping criterion) o Generate a new generation of population by using selection, crossover, mutation, elitism mechanisms o Calculate fitness values of individuals  end while  Initialize locations and velocity of particles  While (stopping criterion) o Calculate the fitness value of each particle o Determine the location of the particle with the highest fitness o For each particle calculate its velocity o Update the location of each particle  end while  Generate initial population  While (stopping criterion) o Calculate the fitness values of host nests o Apply the replacing process o Determine the cuckoo societies o Memorize the best cuckoo solution  end while  Initialize harmony memory  While (stopping criterion) o Calculate harmony memory solutions o Improve new harmonies o Update the harmonies o Memorize the best harmony  end while  Generate population of step leaders  Calculate projectiles energies  While (stopping criterion) o Update leader tips energies o Update best and worst step leaders o Calculate projectiles energies o Update step leaders  end while  Generate initial search agent positions  Calculate the fitness of each search agent  Rank search agents as alpha, beta, delta and omega based on fitness values  While (stopping criterion) o Update the locations of the search agents o Update the coefficient vectors o Calculate the fitness of all search agents  end while 386 Grey Wolf Optimizer Lightning search Harmony search Cuckoo search Particle swarm Genetic Differential evolution (DE) Artificial bee colony (ABC) (GWO) algorithm (LSA) (HS) (CS) optimization algorithm (GA) (PSO) 1960s and 2014 2015 2001 2009 1995 1996-1997 2005 1970s Seyedali Mirjalili, Hussain Shareef, Xin-She James Kennedy John Holland Seyed Mohammad Ahmad Asrul Zong Woo Geem Rainer Storn and Kenneth Yang,and Suash and Russell C. and his Dervis Karaboga Mirjalili, Andrew Ibrahim, Ammar and collaborators Price Deb Eberhart collaborators Lewis Hussein Mutlag Uludağ University Journal of The Faculty of Engineering, Vol. 24, No. 1, 2019 Figure 2: Screenshot of the designed program In the first application, the buck converter that reduces to (frequency of the circuit is , desired voltage fluctuation in the output is ) is designed by using E12 series. The results of the first application are given in Table 3. As can be shown in this table, considering component values found by the algorithms, ABC, GA and GWO reach the similar results and GWO appears to be slightly faster in terms of run-time. Besides, the resistor values of the algorithms are the same, except DE and LSA; however, their resistor values are still compatible with E12 series. Comparing the values of obtained by the algorithms, ABC, GA, HS and GWO algorithms achieve the similar results that are far from the results obtained by DE and LSA algorithms. When regarding the outcomes of , the highest value belongs to DE with 1.667 A. while the lowest one is obtained by PSO algorithm, having 0.942 A. In terms of run time, GWO is able to design the circuit faster than the other competitors, getting a run time of 0.091 sec., and DE is behind GWO with a small difference which is only 0.007 sec. It is worth mentioning that all the algorithms have a capable of designing the circuit in a reasonable time. Table 3. The results of the first application ABC DE GA PSO CS HS LSA GWO 12 6.8 12 12 12 12 8.2 12 4999.927 2461.067 5000.000 4191.247 4625.968 4985.408 3001.274 5000.000 12.000 24.379 12.000 14.315 12.970 12.035 19.991 12.000 0.952 1.667 0.952 0.942 0.948 0.951 1.543 0.952 1.048 1.862 1.048 1.057 1.051 1.048 1.383 1.048 Time (sec) 0.142 0.098 0.184 0.5902 0.3081 0.3375 0.282 0.091 In the second application, the boost converter that raises the voltage from to (frequency of the circuit is , desired voltage fluctuation in the output is ) is designed in accordance with E24 series and the comparative results can be shown in Table 4. The resistor values of the boost converters are found to be 36 Ω by ABC, DE, HS and LSA algorithms, 27 Ω by GA, 51 Ω by PSO algorithm and 47 Ω by GWO algorithm, respectively. In terms of and values, it can be seen that ABC, DE and HS algorithms achieve very similar results in the range of 14 to 17 A. As different to the first application, DE is the fastest algorithm which 387 Vatansever F.,Kuyu Y.Ç.: Buck, Boost and Buck-Boost Converter Designs with Metaheuristic Methods achieves the results in 0.058 seconds whereas GWO is the second fastest with a run time of 0.095 sec, and ABC comes after GWO, which is almost two times slower than DE. Table 4. The results of the second application ABC DE GA PSO CS HS LSA GWO 36 36 27 51 33 36 36 47 3487.883 3503.567 2782.241 4954.074 3223.961 3549.415 3569.358 4657.413 6111.111 6111.111 8148.148 4313.725 6666.666 6111.111 6111.111 4680.851 14.783 14.787 19.779 10.437 16.135 14.798 16.446 11.338 16.466 16.462 21.887 11.621 17.955 16.451 14.803 12.597 Time (sec) 0.124 0.058 0.243 0.432 0.3382 0.3158 0.298 0.095 In the third application, the buck converter is designed with E96 series by using the same input values of the first application. The results given in Table 5 show that DE is the best algorithm with respect to run time as similar to second applications whereas PSO is the slowest one with a run time of 0.527 sec. which is close to that of GA. When comparing Table 5 with Table 3 in terms of the resistor values found by the algorithms, it can be seen that the probability of the finding different resistor rises as the number of E-series (from E12 to E96) used increases. This is because E96 series have more possible combinations of component values than E12 series. It can be observed from the both tables that the run time of most of the algorithms slightly increases. Generally speaking, the results show that the resistor values found by the algorithms are totally compatible with the industrial series and the run time of all the algorithms is remarkably low for all the applications. Table 5. The results of the third application ABC DE GA PSO CS HS LSA GWO 13.3 7.68 13 11.8 11.8 9.31 12.1 10.5 4999.342 2885.139 4882.611 4518.699 4517.772 3519.183 4558.361 3980.694 12.001 20.796 12.288 13.278 13.477 17.049 13.162 15.072 0.854 1.479 0.873 0.964 0.963 1.221 0.939 1.0826 0.950 1.645 0.972 1.070 1.071 1.357 1.044 1.2031 Time (sec) 0.162 0.074 0.355 0.527 0.341 0.340 0.269 0.118 5. CONCLUSION Determining proper component values is a crucial part on the design of the circuits and components chosen from the industrial series make the designs easier, more reliable and flexible to realize. Besides, obtaining the component values with parallel or series connections can be time-consuming while designing the circuits. In this work, the resistor values of DC-DC converters, which are compatible with the industrial series (E12, E24, E96), have been found and some current values have been calculated via the eight different algorithms called ABC, DE, GA, PSO, CS, HS, LSA and GWO. The software developed for this purpose can design the related circuits comparatively with the selected metaheuristic algorithms and is able to find proper component values automatically, easily, fast and effectively. As a future work, it is planned to develop a web platform that will automatically identify all components of DC-DC converters with up-to-date metaheuristic algorithms. REFERENCES 1. 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