Journal articles: 'DC algorithm'

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Allam, Allam M., A. S. Ibrahim, and Essam Nabil. "Single loop PID controller design based on optimization algorithms for parallelly connected dc-dc converters." Journal of Physics: Conference Series 2128, no. 1 (December 1, 2021): 012027. http://dx.doi.org/10.1088/1742-6596/2128/1/012027.

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Abstract This paper addresses a viable single loop PID controller on the bases of optimization algorithms for parallelly connected DC-DC converters to improve current sharing, improve the systems dynamics and guarantee good steady-state performance simultaneously. Because of inconvenience and lack of accuracy of Ziegler-Nichols rule in tuning PID controller parameters, an optimized controller design strategy with the purpose of enhancing the system performance is introduced in this paper. The PID is tuned by the traditional Ziegler -Nichols technique along with three other different algorithms: Genetic algorithm, whale algorithm and grey wolf algorithm. A comparison has been established between these algorithms based on the objective function value, execution time, overshoot, settling time and current sharing. The simulation results were collected to authenticate effectiveness of the proposed techniques and to evaluate the advantages of these optimization algorithms over the traditional tuning method.

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Kapgate, Deepak. "Predictive Data Center Selection Scheme for Response Time Optimization in Cloud Computing." International Journal of Cloud Applications and Computing 11, no. 1 (January 2021): 93–111. http://dx.doi.org/10.4018/ijcac.2021010105.

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The quality of cloud computing services is evaluated based on various performance metrics out of which response time (RT) is most important. Nearly all cloud users demand its application's RT as minimum as possible, so to minimize overall system RT, the authors have proposed request response time prediction-based data center (DC) selection algorithm in this work. Proposed DC selection algorithm uses results of optimization function for DC selection formulated based on M/M/m queuing theory, as present cloud scenario roughly obeys M/M/m queuing model. In cloud environment, DC selection algorithms are assessed based on their performance in practice, rather than how they are supposed to be used. Hence, explained DC selection algorithm with various forecasting models is evaluated for minimum user application RT and RT prediction accuracy on various job arrival rates, real parallel workload types, and forecasting model training set length. Finally, performance of proposed DC selection algorithm with optimal forecasting model is compared with other DC selection algorithms on various cloud configurations.

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Hamed, Salah Beni, Mouna Ben Hamed, and Lassaad Sbita. "Robust Voltage Control of a Buck DC-DC Converter: A Sliding Mode Approach." Energies 15, no. 17 (August 24, 2022): 6128. http://dx.doi.org/10.3390/en15176128.

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This paper deals with voltage control in a buck DC-DC converter. In fact, dynamic mathematical equations describing the principle behavior of the above system have been derived. Due to the nonlinearity of the established model, a nonlinear control algorithm is adopted. It is based on the sliding mode control approach. To highlight the performance of the latter, a comparative study with four control algorithms is carried out. The validity of the model and the performance of the conceived algorithms are verified in simulation. Both the system and the algorithm controls are implemented in the Matlab/Simulink environment. Extensive results under different operational conditions are presented and discussed.

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Luo, Derong, Ting Wu, Ming Li, Benshun Yi, and Haibo Zuo. "Application of VMD and Hilbert Transform Algorithms on Detection of the Ripple Components of the DC Signal." Energies 13, no. 4 (February 19, 2020): 935. http://dx.doi.org/10.3390/en13040935.

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Accurate detection of ripple components of the direct-current (DC) signals is essential for evaluating DC power quality. In this study, the combination algorithm based on variational mode decomposition (VMD) and Hilbert transform (HT) is applied to detect and analyze the characteristics of the ripple components of the DC disturbance signals. Firstly, the optimal modal number of VMD algorithms is comprehensively determined by observing the center frequencies of the mode components and the Index of Orthogonality (IO) of mode components. Through utilizing the VMD algorithm, the DC disturbance signal is accurately decomposed into a series of amplitude modulation-frequency modulation (AM-FM) functions. Then, the HT algorithm is applied to each AM-FM function to obtain the corresponding instantaneous amplitude and frequency, and the characteristics of DC disturbance signal are determined. Some case studies are implemented to analyze the ripple components of the DC disturbance signal with the VMD-HT and empirical mode decomposition (EMD) algorithm. Finally, the experiment results of Gree Photovoltaic Cabin have verified the feasibility and effectiveness of the proposed combination VMD-HT algorithm by comparison with EMD and the window interpolation fast Fourier transform (WIFFT) algorithms.

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Joo, Dong-Myoung, Dong-Hee Kim, and Byoung-Kuk Lee. "Input Current Ripple Reduction Algorithm for Interleaved DC-DC Converter." Transactions of the Korean Institute of Power Electronics 19, no. 3 (June 20, 2014): 220–26. http://dx.doi.org/10.6113/tkpe.2014.19.3.220.

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Le, Hoai Minh, Hoai An Le Thi, Tao Pham Dinh, and Van Ngai Huynh. "Block Clustering Based on Difference of Convex Functions (DC) Programming and DC Algorithms." Neural Computation 25, no. 10 (October 2013): 2776–807. http://dx.doi.org/10.1162/neco_a_00490.

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We investigate difference of convex functions (DC) programming and the DC algorithm (DCA) to solve the block clustering problem in the continuous framework, which traditionally requires solving a hard combinatorial optimization problem. DC reformulation techniques and exact penalty in DC programming are developed to build an appropriate equivalent DC program of the block clustering problem. They lead to an elegant and explicit DCA scheme for the resulting DC program. Computational experiments show the robustness and efficiency of the proposed algorithm and its superiority over standard algorithms such as two-mode K-means, two-mode fuzzy clustering, and block classification EM.

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Hunsberger, Luke, and Roberto Posenato. "Faster Dynamic-Consistency Checking for Conditional Simple Temporal Networks." Proceedings of the International Conference on Automated Planning and Scheduling 30 (June 1, 2020): 152–60. http://dx.doi.org/10.1609/icaps.v30i1.6656.

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A Conditional Simple Temporal Network (CSTN) is a structure for representing and reasoning about time in domains where temporal constraints may be conditioned on outcomes of observations made in real time. A CSTN is dynamically consistent (DC) if there is a strategy for executing its time-points such that all relevant constraints will necessarily be satisfied no matter which outcomes happen to be observed. The literature on CSTNs contains only one sound-and-complete DC-checking algorithm that has been implemented and empirically evaluated. It is a graph-based algorithm that propagates labeled constraints/edges. A second algorithm has been proposed, but not evaluated. It aims to speed up DC checking by more efficiently dealing with so-called negative q-loops.This paper presents a new two-phase approach to DC-checking for CSTNs. The first phase focuses on identifying negative q-loops and labeling key time-points within them. The second phase focuses on computing (labeled) distances from each time-point to a single sink node. The new algorithm, which is also sound and complete for DC-checking, is then empirically evaluated against both pre-existing algorithms and shown to be much faster across not only previously published benchmark problems, but also a new set of benchmark problems. The results show that, on DC instances, the new algorithm tends to be an order of magnitude faster than both existing algorithms. On all other benchmark cases, the new algorithm performs better than or equivalently to the existing algorithms.

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Hajighorbani, Shahrooz, Mohd Amran Mohd Radzi, Mohd Zainal Abidin Ab Kadir, and Suhaidi Bin Shafie. "DC-DC converter for photovoltaic powered battery charger." World Journal of Engineering 13, no. 6 (December 5, 2016): 516–23. http://dx.doi.org/10.1108/wje-09-2016-0085.

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Purpose The purpose of this study is to show in power-voltage curve, a unique maximum power point (MPP) is existed which has the maximum power. Design/methodology/approach This paper presents a MPP tracker algorithm for a standalone system includes DC-DC buck converter and battery storage. Findings By using this algorithm, the maximum available power is achieved and simultaneously, the battery is charged and also protected against overcharge and discharge. The operation of the proposed algorithm is evaluated in with Proteus software to be sure that it can be implemented in microcontroller in reality. Originality/value The simulations results show that the proposed algorithm is able to detect the MPP under different irradiations. Moreover, the battery is charged during the day by PV and protected against overcharge and discharge.

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Zumberge, Jon, Michael Boyd, and Raul Ordonez. "Validation of a DC-DC Boost Circuit Control Algorithm." SAE International Journal of Aerospace 9, no. 1 (September 20, 2016): 112–27. http://dx.doi.org/10.4271/2016-01-2030.

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Rabina, T. Alfa, P. Priyanka Darling Rosita, S. Ramesh, N. Ramani, and N. Ramani. "SYNCHRONOUS BUCK DC DC CONVERTER USING FIREFLY OPTIMIZATION ALGORITHM." INTERNATIONAL JOURNAL OF RECENT TRENDS IN ENGINEERING & RESEARCH 05, Special Issue 07 (March 4, 2019): 15–20. http://dx.doi.org/10.23883/ijrter.conf.20190304.003.fwkkr.

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Piao, Changhao, Hongtao Qiao, and Cong Teng. "Digital Control algorithm for Two-Stage DC-DC Converters." Energy Procedia 16 (2012): 265–71. http://dx.doi.org/10.1016/j.egypro.2012.01.044.

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12

Janarthanan, S. K., and C. Kathirvel. "Multi Input Modified DC-DC SEPIC Converter based Grid Connected System Using MPPT Algorithm." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12-SPECIAL ISSUE (December 31, 2019): 527–35. http://dx.doi.org/10.5373/jardcs/v11sp12/20193248.

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13

Shen, Feichao, Ying Zhang, and Xueyong Wang. "An Accelerated Proximal Algorithm for the Difference of Convex Programming." Mathematical Problems in Engineering 2021 (April 24, 2021): 1–9. http://dx.doi.org/10.1155/2021/9994015.

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In this paper, we propose an accelerated proximal point algorithm for the difference of convex (DC) optimization problem by combining the extrapolation technique with the proximal difference of convex algorithm. By making full use of the special structure of DC decomposition and the information of stepsize, we prove that the proposed algorithm converges at rate of O 1 / k 2 under milder conditions. The given numerical experiments show the superiority of the proposed algorithm to some existing algorithms.

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Lamorahan, Christine, Benny Pinontoan, and Nelson Nainggolan. "Data Compression Using Shannon-Fano Algorithm." d'CARTESIAN 2, no. 2 (October 1, 2013): 10. http://dx.doi.org/10.35799/dc.2.2.2013.3207.

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Abstract Communication systems in the world of technology, information and communication are known as data transfer system. Sometimes the information received lost its authenticity, because size of data to be transferred exceeds the capacity of the media used. This problem can be reduced by applying compression process to shrink the size of the data to obtain a smaller size. This study considers compression for data text using Shannon – Fano algorithm and shows how effective these algorithms in compressing it when compared with the Huffman algorithm. This research shows that text data compression using Shannon-Fano algorithm has a same effectiveness with Huffman algorithm when all character in string all repeated and when the statement short and just one character in the statement that repeated, but the Shannon-Fano algorithm more effective then Huffman algorithm when the data has a long statement and data text have more combination character in statement or in string/ word. Keywords: Data compression, Huffman algorithm, Shannon-Fano algorithm Abstrak Sistem komunikasi dalam dunia teknologi informasi dan komunikasi dikenal sebagai sistem transfer data. Informasi yang diterima kadang tidak sesuai dengan aslinya, dan salah satu penyebabnya adalah besarnya ukuran data yang akan ditransfer melebihi kapasitas media yang digunakan. Masalah ini dapat diatasi dengan menerapkan proses kompresi untuk mengecilkan ukuran data yang besar sehingga diperoleh ukuran yang lebih kecil. Penelitian ini menunjukan salah satu kompresi untuk data teks dengan menggunakan algoritma Shannon – Fano serta menunjukan seberapa efektif algoritma tersebut dalam mengkompresi data jika dibandingkan dengan algoritma Huffman. Kompresi untuk data teks dengan algoritma Shannon-Fano menghasilkan suatu data dengan ukuran yang lebih kecil dari data sebelumnya dan perbandingan dengan algoritma Huffman menunjukkan bahwa algoritma Shannon- Fano memiliki keefektifan yang sama dengan algoritma Huffman jika semua karakter yang ada di data berulang dan jika dalam satu kalimat hanya ada satu karakter yang berulang, tapi algoritma Shannon-Fano lebih efektif jika kalimat lebih panjang dan jumlah karakter di dalam kalimat atau kata lebih banyak dan beragam. Kata kunci: Algoritma Huffman, Algoritma Shannon-Fano, Kompresi data

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Kim, Dong-Eun, Seung-Hwa Park, Hoon-Yang Park, and Jin-Geun Shon. "Differential Power DC/DC Converter by using Active MPPT Algorithm for High Efficiency PV DC Generation." Transactions of The Korean Institute of Electrical Engineers 68, no. 12 (December 31, 2019): 1674–80. http://dx.doi.org/10.5370/kiee.2019.68.12.1674.

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Fehér, Áron, and Dénes Nimród Kutasi. "Modelling and Control of Bounded Hybrid Systems in Power Electronics." Acta Universitatis Sapientiae Electrical and Mechanical Engineering 9, no. 1 (December 1, 2017): 33–42. http://dx.doi.org/10.1515/auseme-2017-0008.

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Abstract In this work, an explicit Model Predictive Control algorithm is devised and compared to classical control algorithms applied to a series resonant DC/DC converter circuit. In the first part, a model of the converter as a hybrid system is created and studied. In the second part, the predictive algorithm is applied and tested on the model. Finally, the designed control algorithm is compared to classical PI and sliding mode controllers.

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Kuo, R. J., S. H. Lin, and Zhen-Yao Chen. "Integration of Particle Swarm Optimization and Immune Genetic Algorithm-Based Dynamic Clustering for Customer Clustering." International Journal on Artificial Intelligence Tools 24, no. 05 (October 2015): 1550019. http://dx.doi.org/10.1142/s0218213015500190.

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This study intends to present a dynamic clustering (DC) approach based on particle swarm optimization (PSO) and immune genetic (IG) (DCPIG) algorithm, which is able to cluster the data into adequate clusters through data characteristics with pre-specified numbers of clusters. The proposed DCPIG algorithm is compared with three DC algorithms in the literature using Iris, Wine, Glass and Vowel benchmark data sets. The experiment results show that the DCPIG algorithm can achieve higher stability and accuracy than the other algorithms. In addition, the DCPIG algorithm is also applied to a real-world problem considering the customer clustering for a cyber flower shop. Lastly, we recommend different products and services to customers based on the clustering results.

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Mahmudin, M. Zainal, Altien Rindengan, and Winsy Weku. "Penggunaan Association Rule Data Mining Untuk Menentukan Pola Lama Studi Mahasiswa F-MIPA UNSRAT." d'CARTESIAN 3, no. 1 (March 31, 2014): 1. http://dx.doi.org/10.35799/dc.3.1.2014.3777.

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Abstract The requirement of highest information sometimes is not balance with the provision of adequate information, so that the information must be re-excavated in large data. By using the technique of association rule we can obtain information from large data such as the college data. The purposes of this research is to determine the patterns of study from student in F-MIPA UNSRAT by using association rule method of data mining algorithms and to compare in the apriori method and a hash-based algorithms. The major’s student data of F-MIPA UNSRAT as a data were processed by association rule method of data mining with the apriori algorithm and a hash-based algorithm by using support and confidance at least 1 %. The results of processing data with apriori algorithms was same with the processing results of hash-based algorithms is as much as 49 combinations of 2-itemset. The pattern that formed between 7,5% of graduates from mathematics major that studied for more 5 years with confidence value is 38,5%. Keywords: Apriori algorithm, hash-based algorithm, association rule, data mining. Abstrak Kebutuhan informasi yang sangat tinggi terkadang tidak diimbangi dengan pemberian informasi yang memadai, sehingga informasi tersebut harus kembali digali dalam data yang besar. Dengan menggunakan teknik association rule kita dapat memperoleh informasi dari data yang besar seperti data yang ada di perguruan tinggi. Tujuan penelitian ini adalah menentukan pola lama studi mahasiswa F-MIPA UNSRAT dengan menggunakan metode association rule data mining serta membandingkan algoritma apriori dan algoritma hash-based. Data yang digunakan adalah data induk mahasiswa F-MIPA UNSRAT yang diolah menggunakan teknik association rule data mining dengan algoritma apriori dan algoritma hash-based dengan minimum support 1% dan minimum confidance 1%. Hasil pengolahan data dengan algoritma apriori sama dengan hasil pengolahan data dengan algoritma hash-based yaitu sebanyak 49 kombinasi 2-itemset. Pola yang terbentuk antara lain 7,5% lulusan yang berasal dari jurusan matematika menempuh studi selama lebih dari 5 tahun dengan nilai confidence 38,5%. Kata kunci : Association rule data mining, algoritma apriori, algoritma hash-based

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Shi, Yuhui. "An Optimization Algorithm Based on Brainstorming Process." International Journal of Swarm Intelligence Research 2, no. 4 (October 2011): 35–62. http://dx.doi.org/10.4018/ijsir.2011100103.

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In this paper, the human brainstorming process is modeled, based on which two versions of Brain Storm Optimization (BSO) algorithm are introduced. Simulation results show that both BSO algorithms perform reasonably well on ten benchmark functions, which validates the effectiveness and usefulness of the proposed BSO algorithms. Simulation results also show that one of the BSO algorithms, BSO-II, performs better than the other BSO algorithm, BSO-I, in general. Furthermore, average inter-cluster distance Dc and inter-cluster diversity De are defined, which can be used to measure and monitor the distribution of cluster centroids and information entropy of the population over iterations. Simulation results illustrate that further improvement could be achieved by taking advantage of information revealed by Dc and/or De, which points at one direction for future research on BSO algorithms.

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Zhang, Wangdong, Bo Fan, Chao Lu, and Huimin Liu. "LADRC Based Digital Control DC-DC ASIC Design." Journal of Physics: Conference Series 2290, no. 1 (June 1, 2022): 012074. http://dx.doi.org/10.1088/1742-6596/2290/1/012074.

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Abstract The Internet of Things (IoT) systems require power management modules to have the characteristics of low power consumption, high conversion efficiency, high integration, stable output, and good dynamic response. Due to its ease of tuning and superior control performance, linear active disturbance rejection control (LADRC) is an attractive design option. Yet, to date, due to its high complexity, LADRC has been used in software systems, while little effort has been made in its hardware implementation. In this work, we investigate and explore efficient hardware implementation of the LADRC algorithm. A fully integrated digital LADRC controller for DC-DC Buck converters is synthesized with UMC 55 nm CMOS process. Its input voltage is 5V, stable output voltage is 2V, and rated operating current is 100mA. To our best knowledge, this is the first work to implement LADRC algorithms in hardware. Simulation results show that the LADRC controller is superior to the PID controller in the terms of start-up time, line regulation, load regulation.

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Hamed, Salah Beni, Mouna Ben Hamed, Lassaad Sbita, Mohit Bajaj, Vojtech Blazek, Lukas Prokop, Stanislav Misak, and Sherif S. M. Ghoneim. "Robust Optimization and Power Management of a Triple Junction Photovoltaic Electric Vehicle with Battery Storage." Sensors 22, no. 16 (August 16, 2022): 6123. http://dx.doi.org/10.3390/s22166123.

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This paper highlights a robust optimization and power management algorithm that supervises the energy transfer flow to meet the photovoltaic (PV) electric vehicle demand, even when the traction system is in motion. The power stage of the studied system consists of a triple-junction PV generator as the main energy source, a lithium-ion battery as an auxiliary energy source, and an electric vehicle. The input–output signal adaptation is made by using a stage of energy conversion. A bidirectional DC-DC buck-boost connects the battery to the DC-link. Two unidirectional boost converters interface between the PV generator and the DC link. One is controlled with a maximum power point tracking (MPPT) algorithm to reach the maximum power points. The other is used to control the voltage across the DC-link. The converters are connected to the electric vehicle via a three-phase inverter via the same DC-link. By considering the nonlinear behavior of these elements, dynamic models are developed. A robust nonlinear MPPT algorithm has been developed owing to the nonlinear dynamics of the PV generator, metrological condition variations, and load changes. The high performance of the MPPT algorithm is effectively highlighted over a comparative study with two classical P & O and the fuzzy logic MPPT algorithms. A nonlinear control based on the Lyapunov function has been developed to simultaneously regulate the DC-link voltage and control battery charging and discharging operations. An energy management rule-based strategy is presented to effectively supervise the power flow. The conceived system, energy management, and control algorithms are implemented and verified in the Matlab/Simulink environment. Obtained results are presented and discussed under different operating conditions.

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Et. al., Subbarao Mopidevi,. "High Gain DC/DC Converter with IC MPPT Algorithm For DC Micro-Grid Applications." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (March 24, 2021): 244–50. http://dx.doi.org/10.17762/itii.v9i2.340.

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High gain DC-DC converter with IC MPPT algorithm, solar & wind as inputs in conjunction with this converter are presented in this paper. Recently, due to increase in the green energy demand the renewable energy resources are widely used.DC micro grids are now of significant importance due to the rise in DC loads and the demand for high power quality. These DC loads require various levels of voltage based on their power ratings. The proposed step-up DC-DC converter therefore has a high voltage gain with optimum duty ratio characteristics. In addition, the additional benefit of delivering electricity at two distinct voltage levels is that it is more suitable for DC micro grid applications and all the switches are controlled using single control signal which reduces complexity. The proposed converter is implemented and theoretically verified in SIMULINK MATLAB software for two different load power ratings of 100W and 200W.

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de Oliveira, Welington, and Michel P. Tcheou. "An Inertial Algorithm for DC Programming." Set-Valued and Variational Analysis 27, no. 4 (October 25, 2018): 895–919. http://dx.doi.org/10.1007/s11228-018-0497-0.

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M., Venkateswaran, Govindaraju C., and Santhosh T.K. "Integrated predictive control and fault diagnosis algorithm for single inductor-based DC-DC converters for photovoltaic systems." Circuit World 47, no. 1 (June 5, 2020): 105–16. http://dx.doi.org/10.1108/cw-11-2019-0166.

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Purpose Power converters are an integral part of the energy conversion process in solar photovoltaic (PV) systems which is used to match the solar PV generation with the load requirements. The increased penetration of renewable invokes intermittency in the generated power affecting the reliability and continuous energy supply of such converters. DC-DC converters deployed in solar PV systems impose stringent restrictions on supplied power, continuous operation and fault prediction scenarios by continuously observing state variables to ensure continuous operation of the converter. Design/methodology/approach A converter deployed for a mission-critical application has to ensure continuous regulated output for which the converter has to ensure fault-free operation. The fault diagnostic algorithm relies on the measurement of a state variable to assess the type of fault. In the same line, a predictive controller depends on the measurement of a state variable to predict the control variable of a converter system to regulate the converter output around a fixed or a variable reference. Consequently, both the fault diagnosis and the predictive control algorithms depend on the measurement of a state variable. Once measured, the available data can be used for both algorithms interchangeably. Findings The objective of this work is to integrate the fault diagnostic and the predictive control algorithms while sharing the measurement requirements of both these control algorithms. The integrated algorithms thus proposed could be applied to any converter with a single inductor in its energy buffer stage. Originality/value laboratory prototype is created to verify the feasibility of the integrated predictive control and fault diagnosis algorithm. As the proposed method combine the fault detection algorithm along with predictive control, a load step variation and manual fault creation methods are used to verify the feasibility of the converter as with the simulation analysis. The value for the capacitors and inductors were chosen based on the charge-second and volt-second balance equations obtained from the steady-state analysis of boost converter.

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Duan, Chun, and Di Wu. "Nonlinear Voltage Regulation Algorithm for DC-DC Boost Converter with Finite-Time Convergence." Journal of Control Science and Engineering 2019 (April 1, 2019): 1–5. http://dx.doi.org/10.1155/2019/6761784.

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For the DC-DC Boost converter system, this paper employs the finite-time control technique to design a new nonlinear fast voltage regulation control algorithm. Compared with the existing algorithm, the main advantage of the proposed algorithm lies in the fact that it can offer a fast convergent rate, i.e., finite-time convergence. Based on the average state space model of Boost converter system and finite-time control theory, rigorous stability analysis showed that the output voltage converges to the reference voltage in a finite time. Simulation results demonstrate the efficiency of the proposed method. Compared with PI control algorithm, it is shown that the proposed algorithm has a faster regulation performance and stronger robust performance on load-variation.

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Aribowo, Widi, Joko Joko, Subuh Isnur, Aditya Chandra Hermawan, Fendi Achmad, and Yuli Sutoto Nugroho. "ATOM SEARCH OPTIMIZATION – NEURAL NETWORK FOR DRIVING DC MOTOR." SINERGI 25, no. 3 (July 30, 2021): 259. http://dx.doi.org/10.22441/sinergi.2021.3.003.

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DC motor applications are very widely used because DC motors are very suitable for applications, especially control. Thus, a proper DC motor controller design is required. DC motor speed control is very important to maintain the stability of motor operation. A recent type of metaheuristic algorithm that mimics the motion of atoms is introduced. Atom search optimization (ASO) is a mathematical model and duplicates the behavior of atoms in nature. Atoms intercommunicate with each other via the delivering contact force in the form of the Lennard-Jones potential and the constraint force produced from the potential bond length. The algorithm is simple and easy to be applied. In this study, the atomic search optimization (ASO) algorithm is proposed as a speed controller for the control dc motor. First, the ASO proposed by the algorithm is applied for the optimization of the neural network. Second, the ASO-NN proposal was the result compared to other algorithms. This paper compares the performance of two different control techniques applied to DC motors, namely the ASO-NN and proportional integral derivative (PID) methods. The results show that the proposed method has effectiveness. The calculation of the proposed ASO-NN control shows the best performance in the settling time. The ASO-NN method has the capability of settling time 0.04 seconds faster than the PID method.

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Zhang, Liang, Kang Chen, Ling Lyu, and Guowei Cai. "Research on the Operation Control Strategy of a Low-Voltage Direct Current Microgrid Based on a Disturbance Observer and Neural Network Adaptive Control Algorithm." Energies 12, no. 6 (March 25, 2019): 1162. http://dx.doi.org/10.3390/en12061162.

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Low-voltage direct current (DC) microgrid based on distributed generation (DG), the problems of load mutation affecting the DC bus under island mode, and the security problems that may arise when the DC microgrid is switched from island mode to grid-connected mode are considered. Firstly, a DC bus control algorithm based on disturbance observer (DOB) was proposed to suppress the impact of system load mutation on DC bus in island mode. Then, in a grid-connected mode, a pre-synchronization control algorithm based on a neural network adaptive control was proposed, and the droop controller was improved to ensure better control accuracy. Through this pre-synchronization control, the microgrid inverters output voltage could quickly track the power grid’s voltage and achieve an accurate grid-connected operation. The effectiveness of the algorithms was verified by simulation.

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Hunsberger, Luke, and Roberto Posenato. "Speeding Up the RUL¯ Dynamic-Controllability-Checking Algorithm for Simple Temporal Networks with Uncertainty." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (June 28, 2022): 9776–85. http://dx.doi.org/10.1609/aaai.v36i9.21213.

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A Simple Temporal Network with Uncertainty (STNU) includes real-valued variables, called time-points; binary difference constraints on those time-points; and contingent links that represent actions with uncertain durations. STNUs have been used for robot control, web-service composition, and business processes. The most important property of an STNU is called dynamic controllability (DC); and algorithms for checking this property are called DC-checking algorithms. The DC-checking algorithm for STNUs with the best worst-case time-complexity is the RUL¯ algorithm due to Cairo, Hunsberger and Rizzi. Its complexity is O(mn + k²n + kn log n), where n is the number of time-points, m is the number of constraints, and k is the number of contingent links. It is expected that this worst-case complexity cannot be improved upon. However, this paper provides a new algorithm, called RUL2021, that improves its performance in practice by an order of magnitude, as demonstrated by a thorough empirical evaluation.

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Wang, Meihua, Fengmin Xu, and Chengxian Xu. "A Branch-and-Bound Algorithm Embedded with DCA for DC Programming." Mathematical Problems in Engineering 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/364607.

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The special importance of Difference of Convex (DC) functions programming has been recognized in recent studies on nonconvex optimization problems. In this work, a class of DC programming derived from the portfolio selection problems is studied. The most popular method applied to solve the problem is the Branch-and-Bound (B&B) algorithm. However, “the curse of dimensionality” will affect the performance of the B&B algorithm. DC Algorithm (DCA) is an efficient method to get a local optimal solution. It has been applied to many practical problems, especially for large-scale problems. A B&B-DCA algorithm is proposed by embedding DCA into the B&B algorithms, the new algorithm improves the computational performance and obtains a global optimal solution. Computational results show that the proposed B&B-DCA algorithm has the superiority of the branch number and computational time than general B&B. The nice features of DCA (inexpensiveness, reliability, robustness, globality of computed solutions, etc.) provide crucial support to the combined B&B-DCA for accelerating the convergence of B&B.

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Chen, Weile, Zhanzhan Ge, Yingying Cheng, Haibo Du, Qingqiu Du, and Miao Yu. "Current-constrained finite-time control algorithm for DC-DC Buck converter." Journal of the Franklin Institute 358, no. 18 (December 2021): 9467–82. http://dx.doi.org/10.1016/j.jfranklin.2021.09.017.

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Fang, Wei, Xiao-Dong Liu, Su-Cheng Liu, and Yan-Fei Liu. "A Digital Parallel Current-Mode Control Algorithm for DC–DC Converters." IEEE Transactions on Industrial Informatics 10, no. 4 (November 2014): 2146–53. http://dx.doi.org/10.1109/tii.2014.2358455.

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Mandal, Kuntal, Soumitro Banerjee, and Chandan Chakraborty. "A New Algorithm for Small-Signal Analysis of DC–DC Converters." IEEE Transactions on Industrial Informatics 10, no. 1 (February 2014): 628–36. http://dx.doi.org/10.1109/tii.2013.2277942.

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Nechadi, E., M. N. Harmas, N. Essounbouli, and A. Hamzaoui. "Optimal Synergetic Control based Bat Algorithm for DC-DC Boost Converter." IFAC-PapersOnLine 49, no. 12 (2016): 698–703. http://dx.doi.org/10.1016/j.ifacol.2016.07.792.

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Le Thi, Hoai An, Xuan Thanh Vo, and Tao Pham Dinh. "Efficient Nonnegative Matrix Factorization by DC Programming and DCA." Neural Computation 28, no. 6 (June 2016): 1163–216. http://dx.doi.org/10.1162/neco_a_00836.

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In this letter, we consider the nonnegative matrix factorization (NMF) problem and several NMF variants. Two approaches based on DC (difference of convex functions) programming and DCA (DC algorithm) are developed. The first approach follows the alternating framework that requires solving, at each iteration, two nonnegativity-constrained least squares subproblems for which DCA-based schemes are investigated. The convergence property of the proposed algorithm is carefully studied. We show that with suitable DC decompositions, our algorithm generates most of the standard methods for the NMF problem. The second approach directly applies DCA on the whole NMF problem. Two algorithms—one computing all variables and one deploying a variable selection strategy—are proposed. The proposed methods are then adapted to solve various NMF variants, including the nonnegative factorization, the smooth regularization NMF, the sparse regularization NMF, the multilayer NMF, the convex/convex-hull NMF, and the symmetric NMF. We also show that our algorithms include several existing methods for these NMF variants as special versions. The efficiency of the proposed approaches is empirically demonstrated on both real-world and synthetic data sets. It turns out that our algorithms compete favorably with five state-of-the-art alternating nonnegative least squares algorithms.

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Iman, M. I., M. F. Roslan, Pin Jern Ker, and M. A. Hannan. "An Intelligent Maximum Power Point Tracking Algorithm for Photovoltaic System." International Journal of Engineering & Technology 7, no. 4.35 (November 30, 2018): 457. http://dx.doi.org/10.14419/ijet.v7i4.35.22861.

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This work comprehensively demonstrates the performance analysis of Fuzzy Logic Controller (FLC) with Particle Swarm Optimization (PSO) Maximum Power Point Tracker (MPPT) algorithm on a stand-alone Photovoltaic (PV) applications systems. A PV panel, DC-DC Boost converter and resistive load was utilized as PV system. Three different MPPT algorithms were implemented in the converter. The result obtained from the converter was analyzed and compared to find the best algorithm to be used to identify the point in which maximum power can be achieve in a PV system. The objective is to reduce the time taken for the tracking of maximum power point of PV application system and minimize output power oscillation. The simulation was done by using MATLAB/Simulink with DC-DC Boost converter. The result shows that FLC method with PSO has achieved the fastest response time to track MPP and provide minimum oscillation compared to conventional P&O and FLC techniques.

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Dołęgowski, Michał, and Mirosław Szmajda. "A Novel Algorithm for Fast DC Electric Arc Detection." Energies 14, no. 2 (January 7, 2021): 288. http://dx.doi.org/10.3390/en14020288.

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Electric arcing is a common problem in DC power systems. To overcome this problem, the electric arc detection algorithm has been developed as a faster alternative to existing algorithms. The following issues are addressed in this paper: The calculation of the proposed algorithm of incremental decomposition of the signal over time; the computational complexity of Fast Fourier Transform (FFT) and the incremental decomposition; the test bench used to measure electric arcs at given parameters; the analysis of measurements using FFT; and the analysis of measurements using incremental decomposition. The parameters are the DC voltage, electric load, and width of the gap between electrodes. The results showed that the proposed algorithm allows for a faster calculation—about seven times faster than FFT—and cheaper implementation in electric arc detection devices than FFT.

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Dołęgowski, Michał, and Mirosław Szmajda. "A Novel Algorithm for Fast DC Electric Arc Detection." Energies 14, no. 2 (January 7, 2021): 288. http://dx.doi.org/10.3390/en14020288.

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Electric arcing is a common problem in DC power systems. To overcome this problem, the electric arc detection algorithm has been developed as a faster alternative to existing algorithms. The following issues are addressed in this paper: The calculation of the proposed algorithm of incremental decomposition of the signal over time; the computational complexity of Fast Fourier Transform (FFT) and the incremental decomposition; the test bench used to measure electric arcs at given parameters; the analysis of measurements using FFT; and the analysis of measurements using incremental decomposition. The parameters are the DC voltage, electric load, and width of the gap between electrodes. The results showed that the proposed algorithm allows for a faster calculation—about seven times faster than FFT—and cheaper implementation in electric arc detection devices than FFT.

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Wang, Lin, and Anke Xue. "Equivalent Modeling of Microgrids Based on Optimized Broad Learning System." Energies 14, no. 23 (November 25, 2021): 7911. http://dx.doi.org/10.3390/en14237911.

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The DC microgrid is an important structure of microgrids. Aiming at the problem of the grid-connected DC microgrid modeling, a grid-connected DC microgrid equivalent modeling method based on the optimized Broad Learning System (BLS) is proposed. Taking the electrical parameter data of the grid-connected DC microgrid access point as the training data set of BLS, the equivalent model of the grid-connected equivalent model is constructed. In order to further improve the accuracy and generalization performance of the model, the shark smell optimization (SSO) algorithm is used to optimize the input weights and thresholds of the BLS. Furthermore, the shark smell optimization-Broad Learning System (SSO-BLS) algorithm is proposed. SSO-BLS is compared with RBF, BLS, BFO-ELM, and other algorithms. The results show that the grid-connected DC microgrid model based on SSO-BLS has good accuracy and generalization characteristics.

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Ambrisi, Angelo, Massimiliano De Magistris, and Raffaele Fresa. "Multi-objective Optimization Based Design of High Efficiency DC-DC Switching Converters." International Journal of Power Electronics and Drive Systems (IJPEDS) 7, no. 2 (June 1, 2016): 379. http://dx.doi.org/10.11591/ijpeds.v7.i2.pp379-386.

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In this paper we explore the feasibility of applying multi objective stochastic optimization algorithms to the optimal design of switching DC-DC converters, in this way allowing the direct determination of the Pareto optimal front of the problem. This approach provides the designer, at affordable computational cost, a complete optimal set of choices, and a more general insight in the objectives and parameters space, as compared to other design procedures. As simple but significant study case we consider a low power DC-DC hybrid control buck converter. Its optimal design is fully analyzed basing on a Matlab public domain implementations for the considered algorithms, the GODLIKE package implementing Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Simulated Annealing (SA). In this way, in a unique optimization environment, three different optimization approaches are easily implemented and compared. Basic assumptions for the Matlab model of the converter are briefly discussed, and the optimal design choice is validated “a-posteriori” with SPICE simulations.

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Ji, Ying, and Shaojian Qu. "Proximal Point Algorithms for Vector DC Programming with Applications to Probabilistic Lot Sizing with Service Levels." Discrete Dynamics in Nature and Society 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/5675183.

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We present a new algorithm for solving vector DC programming, where the vector function is a function of the difference of C-convex functions. Because of the nonconvexity of the objective function, it is difficult to solve this class of problems. We propose several proximal point algorithms to address this class of problems, which make use of the special structure of the problems (i.e., the DC structure). The well-posedness and the global convergence of the proposed algorithms are developed. The efficiency of the proposed algorithm is shown by an application to a multicriteria model stemming from lot sizing problems.

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Yu, Ting Chung, Yih Bin Lin, and Yu Cheng Lin. "Comparisons of the Power-Feedback MPPT Algorithms for the Photovoltaic Systems." Applied Mechanics and Materials 229-231 (November 2012): 1047–51. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.1047.

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The purpose of this paper is to compare three power-feedback maximum power point tracking (MPPT) algorithms used in photovoltaic systems. The Matlab/Simulink is used in this paper to establish a model of photovoltaic system with MPPT function. This system is developed by combining the models of established PV module and DC-DC boost converter with the power-feedback MPPT algorithms of perturbation and observation, incremental conductance and hill climbing, respectively. The system will be simulated under different weather conditions and MPPT algorithms. According to the comparisons of the simulation results, it can be observed that the photovoltaic simulation system can track the maximum power accurately using the three MPPT algorithms discussed in this paper. HC algorithm possesses fast dynamic response, but P&O algorithm is well regulated PV output voltage and power than HC algorithm. The tracking time spent by INC algorithm is a little longer than the other two algorithms. The HC algorithm is suitable for the cases of battery charging which doesn't need extremely stable voltage, and the P&O algorithm is more suitable for the system with precision electric instruments. Furthermore, the INC algorithm is adaptable to the cases of fast changing weather conditions owing to its advantage of no misjudgment.

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Changizi, Nemat, Mahbubeh Moghadas, Mohamad Reza Dastranj, and Mohsen Farshad. "Design a Fuzzy Logic Based Speed Controller for DC Motor with Genetic Algorithm Optimization." Applied Mechanics and Materials 110-116 (October 2011): 2324–30. http://dx.doi.org/10.4028/www.scientific.net/amm.110-116.2324.

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In this paper, an intelligent speed controller for DC motor is designed by combination of the fuzzy logic and genetic algorithms. First, the speed controller is designed according to fuzzy rules such that the DC drive is fundamentally robust. Then, to improve the DC drive performance, parameters of the fuzzy speed controller are optimized by using the genetic algorithm. Simulation works in MATLAB environment demonstrate that the genetic optimized fuzzy speed controller became very strong, gives very good results and possesses good robustness.

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Alshamary, Haider Ali Jasim, Ahmad Sulaiman Abdullah, and Sadeq Adnan Hbeeb. "Nearest-neighbor field algorithm based on patchMatch for myocardial perfusion motion estimation/correction." Bulletin of Electrical Engineering and Informatics 12, no. 2 (April 1, 2023): 843–50. http://dx.doi.org/10.11591/eei.v12i2.4216.

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Deformation correction and recovery of dynamic magnetic resonance images (DMRI) with low complexity algorithms without compromising image quality is a challenging problem. We proposed a motion estimation deformation-correction compressive sensing (DC-CS) scheme to recover dynamic images from its undersampled measurements. We simplify the complex optimization problem into three sub-problems. The contributions of this research are: introducing a global search strategy instead of the DC registration step, guaranteeing a non-explicit motion estimation that avoids any spatial alignment or registration of the images, and lowering the computational cost to the minimum by using PatchMatch (PM). The simulation result shows that the PM algorithm accelerates the recovery time without losing the quality in comparison with the DC algorithm.

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Jin, Tao, Wei Chen, and Tao Ning. "Application of Genetic Algorithms on Parameter Estimation of HVDC Circuit Breaker." Applied Mechanics and Materials 52-54 (March 2011): 1009–14. http://dx.doi.org/10.4028/www.scientific.net/amm.52-54.1009.

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In order to estimate the parameters of capacitance, inductance and resistance in oscillation circuit DC circuit breaker a method of parameter estimation of High-Voltage circuit break based on genetic algorithms is proposed and which can properly assess the performance of DC circuit breaker. In this paper A genetic algorithm is introduced and applied in parameter estimation of High-Voltage circuit breaker and then compare it with the traditional MATLAB curve fitting, the result clearly reveal the advantages of genetic algorithm.

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Lobov, Evgeniy M., Vladimir S. Priputin, Elizaveta O. Lobova, and Efim A. Liberman. "DISPERSION DISTORTION TRACKING COMPENSATOR BASED ON THE SIGMA-POINT KALMAN." T-Comm 15, no. 1 (2021): 41–47. http://dx.doi.org/10.36724/2072-8735-2021-15-1-41-47.

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The article proposes an algorithm for nonlinear optimal filtering of the dispersion characteristic slope of the ionospheric channel (DC slope) based on the Kalman sigma-point filter when sounding the channel with broadband signals. The relevance of the applied algorithm can be explained by the fact that at present in the modern world the relevance of the transmission of broadband information signals in the decameter range has increased. The reception of such signals is complicated by the effects caused by the influence of the frequency dispersion of the ionospheric channel on the signal. The above influence leads to significant distortions of the useful signal and a decrease in the quality of information reception in general. The above influence leads to significant distortions of the useful signal and a decrease in the quality of information reception in general. Therefore, the effect of frequency dispersion cannot be neglected and must be taken into account when developing algorithms for optimal reception. Thus, estimation of the parameters of the frequency dispersion of the ionospheric channel becomes an urgent task. The article provides recurrent formulas for calculating the optimal filtration of the DC slope. Moreover, an algorithm for joint estimation of dispersion distortions and their compensation is presented, which allows improving the quality of estimation. The results of simulation modeling of the algorithms are given: the obtained estimate and the true value of the frequency dispersion parameters for two cases – a Markov damped process and a process with random increments. The absolute error in estimating the DC slope is also considered. The computational efficiency of the algorithm for estimating the DC slope using the Kalman sigma-point filter with and without compensation is calculated. In addition, functional diagrams of optimal filtering of the DC slope with compensation and without compensation are given. The ways of implementing the proposed algorithm on an ARM processor are considered.

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HE, MINGZHI, and JIANPING XU. "ELIMINATION OF TIME DELAY IN DIGITAL CONTROL LOOP OF SWITCHING DC–DC CONVERTERS." Journal of Circuits, Systems and Computers 17, no. 03 (June 2008): 525–37. http://dx.doi.org/10.1142/s0218126608004368.

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Time delay introduced by sampling and calculation exists between the perturbation and the duty ratio updating in the digital control loop. This time delay degrades the transient performance of digital control switching power supplies. By overcoming the sampling and calculation time effect and to calibrate the ripple that occurs during switching cycles, new control algorithm is proposed in this paper to improve the transient performance of switching power supplies by eliminating the time delay in the control loop. Simulation results show the improvement of load transient performance by the proposed algorithms. Experimental system is also set up to verify the analysis and computer simulation results.

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Le Thi, Hoai An, and Vinh Thanh Ho. "Online Learning Based on Online DCA and Application to Online Classification." Neural Computation 32, no. 4 (April 2020): 759–93. http://dx.doi.org/10.1162/neco_a_01266.

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We investigate an approach based on DC (Difference of Convex functions) programming and DCA (DC Algorithm) for online learning techniques. The prediction problem of an online learner can be formulated as a DC program for which online DCA is applied. We propose the two so-called complete/approximate versions of online DCA scheme and prove their logarithmic/sublinear regrets. Six online DCA-based algorithms are developed for online binary linear classification. Numerical experiments on a variety of benchmark classification data sets show the efficiency of our proposed algorithms in comparison with the state-of-the-art online classification algorithms.

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Boussaid, Brahim, Abdelkader Harrouz, Djamel Benmenine, Fadila Tahiri, and Fatiha Bekraoui. "Optimal PI Parameters Tuning for a DC-DC Boost Converter." Algerian Journal of Renewable Energy and Sustainable Development 3, no. 2 (December 15, 2021): 223–29. http://dx.doi.org/10.46657/ajresd.2021.3.2.11.

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This paper designs a nonlinear PI-type controller for the robust control of a boost DC-DC converter using a particle swarm optimization (PSO) algorithm for increasing the output voltage of the photovoltaic (PV) system. In addition, the PI controller is used to tracking the maximum power from the PV panel, at different atmospheric condition. For tuning PI controllers is a tedious work and it is difficult to tune the PI gains optimally due to the nonlinearity. This paper presents an approach to use the particle swarm optimization algorithm to design the optimal PI controllers. The Simulations results show that the proposed controller exhibits better behavior in terms of settling time and overshoot and better performance of the converter.

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Chen, Chuandong, Rongshan Wei, Shaohao Wang, and Wei Hu. "Novel Verification Method for Timing Optimization Based on DPSO." VLSI Design 2018 (March 21, 2018): 1–8. http://dx.doi.org/10.1155/2018/8258397.

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Timing optimization for logic circuits is one of the key steps in logic synthesis. Extant research data are mainly proposed based on various intelligence algorithms. Hence, they are neither comparable with timing optimization data collected by the mainstream electronic design automation (EDA) tool nor able to verify the superiority of intelligence algorithms to the EDA tool in terms of optimization ability. To address these shortcomings, a novel verification method is proposed in this study. First, a discrete particle swarm optimization (DPSO) algorithm was applied to optimize the timing of the mixed polarity Reed-Muller (MPRM) logic circuit. Second, the Design Compiler (DC) algorithm was used to optimize the timing of the same MPRM logic circuit through special settings and constraints. Finally, the timing optimization results of the two algorithms were compared based on MCNC benchmark circuits. The timing optimization results obtained using DPSO are compared with those obtained from DC, and DPSO demonstrates an average reduction of 9.7% in the timing delays of critical paths for a number of MCNC benchmark circuits. The proposed verification method directly ascertains whether the intelligence algorithm has a better timing optimization ability than DC.

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Van Nguyen, Thanh, and Kyeong-Hwa Kim. "Power Flow Control Strategy and Reliable DC-Link Voltage Restoration for DC Microgrid under Grid Fault Conditions." Sustainability 11, no. 14 (July 10, 2019): 3781. http://dx.doi.org/10.3390/su11143781.

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In this paper, an effective power flow control strategy (PFCS) based on the centralized control method and a reliable DC-link voltage (DCV) restoration algorithm for a DC microgrid (DCMG) under grid fault conditions are proposed. Considering the relationship of supply-demand power and the statuses of system units, thirteen operating modes are presented to ensure the power balance in DCMG under various conditions. In the PFCS, the battery charging/discharging procedure is implemented considering the battery power limit to avoid overheating and damage. Moreover, load shedding and load reconnection algorithms are presented to maintain the system power balance, even in critical cases. To prevent the system power imbalance in DCMG caused by the delay of grid fault detection, a reliable DCV restoration algorithm is also proposed in this paper. In the proposed scheme, as soon as abnormal behavior of the DCV is detected, the battery or wind power generation system instantly enters a local emergency control mode to restore the DCV rapidly to the nominal value, regardless of the control mode assigned from the central controller. Comprehensive simulations and experiments based on the DCMG testbed are carried out to prove the effectiveness of the PFCS and the proposed DCV restoration algorithm.

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Journal articles on the topic 'DC algorithm'

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