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stochastic fractal search algorithm for reconfiguration of distribution networks with distributed generations

AIN SHAMS ENGINEERING JOURNAL 2020年第2期11卷 389-407页

作者： Tung The Tran Truong, Khoa Hoang Vo, Dieu Ngoc Ho Chi Minh City Univ Technol Dept Power Syst VNU HCM Ho Chi Minh City Vietnam Univ Teknol PETRONAS Dept Elect & Elect Engn Seri Iskandar Perak Malaysia

This paper presents an implementation of the stochastic fractal search (SFS) algorithm to solve the distribution network reconfiguration (DNR) problem in the presence of distributed generations (DGs). The SFS is a meta-heuristic algorithm inspired by the fractal theory for solving optimization problems. For dealing with the formulated DNR problem, the loss sensitivity factor (LSF) was first used to determine the optimal location of DGs and SFS was then applied to find the best size of DGs and configuration of the network. The proposed SFS algorithm was tested on 33-bus, 69-bus, 84-bus, 119-bus, and 136-bus distribution networks with seven different scenarios. The results obtained from SFS were compared to those from other methods in the literature and the result comparison has showed that SFS obtained better solutions than other methods. Therefore, SFS can be a very favorable method for solving the reconfiguration problem considering the participation of DGs in distribution networks. (C) 2019 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University.

关键词： Distributed generations Distribution network reconfiguration Diffusion process stochastic fractal search algorithm Updating process

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A novel stochastic fractal search algorithm for optimal allocation of distributed generators in radial distribution systems

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APPLIED SOFT COMPUTING 2018年 70卷 773-796页

作者： Tri Phuoc Nguyen Dieu Ngoc Vo Ho Chi Minh City Univ Technol Dept Power Syst Ho Chi Minh City Vietnam

This paper presents an application of a novel stochastic fractal search algorithm (SFSA) for solving the optimal allocation of distributed generators (OADG) problem in radial distribution systems (RDSs). The SFSA method is a newly efficient meta-heuristic algorithm inspired by the growing process of nature by using a mathematical idea known as fractal for solving optimization problems. The advantages of the SFSA method are simple implementation and few control parameters. The method is proposed to deal with the OADG problem by minimizing the active power loss, improving the voltage profile, and increasing the voltage stability while satisfying various constraints including active and reactive power balance, bus voltage limits, DG capacity limits, branch current limits, and DG penetration limit. The effectiveness of the proposed SFSA method has been verified on the IEEE 33-bus, 69-bus, and 118-bus RDSs and the obtained results have been validated via comparing to those from other methods in the literature. The result comparisons from the test systems have indicated that the proposed method can obtain higher quality solutions than many other methods for the considered scenarios from the test systems. Therefore, the proposed SFSA can be a very effective and favorable method for solving the OADG problem. (C) 2018 Published by Elsevier B.V.

关键词： Active power loss Radial distribution systems stochastic fractal search algorithm Voltage stability Voltage profile

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Incorporation of stochastic fractal search algorithm into efficient design of PID controller for an automatic voltage regulator system

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NEURAL COMPUTING & APPLICATIONS 2018年第6期30卷 1991-2002页

作者： Celik, Emre Gazi Univ Fac Technol Dept Elect & Elect Engn Ankara Turkey

Although most accepted control algorithm is of proportional+integral+derivative (PID) structure in automatic voltage regulator (AVR) systems due to its effectiveness and simplicity, it is still a challenging task for researchers to tune its parameters efficiently. It is therefore of great interest, and constructs the motivation of this paper to undertake this task by incorporating the recently introduced, powerful optimization technique named as stochastic fractal search (SFS), for the first time in literature, which has the feature of increased accuracy and reduced convergence time. After optimizing the controller gains of K-p, K-i, and K-d, their performance is checked by using transient response analysis and then compared with those offered by recently published studies that use six competitive algorithms for achieving an efficient AVR system. To appraise the stability of the concerned power system tuned by SFS algorithm, root locus analysis and bode analysis are conducted along with the robustness analysis, which examines the system response to variations in the system parameters. Obtained comparative results affirm the excellence of SFS algorithm in achieving better PID parameters, and accordingly this yields an improved voltage response and minimized integral of time squared error in the output voltage profile.

关键词： Automatic voltage regulator Proportional plus integral plus derivative controller stochastic fractal search algorithm Transient responses Stability analysis Robustness analysis

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Identification of Nonlinear Synchronous Generator Parameters Using stochastic fractal search algorithm

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2021年第6期32卷 1639-1651页

作者： Bendaoud, Elrachid Radjeai, Hammoud Boutalbi, Oussama Setif 1 Univ Elect Engn Dept Automat Lab LAS Setif Algeria Setif 1 Univ Elect Dept Intelligent Syst Lab Setif Algeria

This paper develops a general output error identification approach of nonlinear synchronous generator parameters. The considered method is based on a meta-heuristic optimization algorithm called stochastic fractal search (SFS). It consists of minimizing a quadratic criterion that represents the squared difference between the simulated model outputs and those obtained from the model to be identified by using the SFS algorithm. To highlight the performance of the proposed method, a deep comparison with the PSO algorithm was carried out. The obtained results proved the superiority of the proposed approach in terms of stability, robustness, estimation accuracy and convergence speed.

关键词： Synchronous generator stochastic fractal search algorithm Parameter identification Nonlinear model State space model

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Environmental Economic Dispatch Using stochastic fractal search algorithm

Environmental Economic Dispatch Using Stochastic Fractal Sea...

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International Symposium on Electrical and Electronics Engineering (ISEE)

作者： Thang Phan Van Hong Dieu Vo Ngoc Khanh Dang Tuan Ho Chi Minh City Univ Technol HCMUT Dept Power Syst 268 Ly Thuong Kiet StDist 10 Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh Ho Chi Minh City Vietnam

ISBN: (纸本)9781665414876

today, the issue of environmental protection is being urgently set. To ensure environmental factors and economic efficiency in the context which fossil energy resources are increasingly scarce, the control of fuel costs and emission costs are crucial factors in ensuring and increase profits of power plants. This paper uses stochastic fractal search algorithm (SFSA) for solving the economic emission dispatch (EED) problems - a multi object optimization problems with load balance, power generation limits, and fuel constraints. The proposed SFSA has been tested on 6-generator of environmental economic and IEEE 30 bus system where the single line diagram is shown in Fig 1. For each test system, the problem is analyzed in two cases of single and multi- objective functions. The obtained results from the test cases have been compared to those from other methods in the literature as evidence for the SFSA can be a new effective method for solving economic emission dispatch problems.

关键词： Economic dispatch emission dispatch diffusion process environmental economic stochastic fractal search algorithm updating process

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Applied stochastic fractal search algorithm to solve Economic Emission Dispatch Problems

Applied Stochastic Fractal Search Algorithm to solve Economi...

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International Conference on Green Energy, Computing and Sustainable Technology (GECOST)

作者： Thang Phan Van Hong Khanh Dang Tuan Dieu Vo Ngoc Ho Chi Minh City Univ Technol HCMUT Dept Power Syst 268 Ly Thuong Kiet StDist 10 Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Linh Trung Ward Ho Chi Minh City Vietnam

ISBN: (纸本)9781665486637

Nowadays using of energy sources to produce electricity has many changes in increasing the proportion of using renewable energy. However, with the stability in generating capacity, fossil energy resources still play an important role in the power system. Therefore, solving the economic dispatch problem in terms of ensuring environmental factors is always a difficult task. This paper contributes one of the new effective method for solving the environmental economic dispatch problems - stochastic fractal search algorithm (SFSA). The problems are solved in three optimization case: fuel cost, emission, and economic emission with the constraints on load balance, power generation limits, and fuel. The SFSA will be applied to electrical systems include: 5-unit;10-unit with power retaining in three weeks and IEEE 30 bus with wind turbines. These results have been compared with other algorithms in the same power system to demonstrate the effectiveness of SFSA.

关键词： Economic dispatch emission dispatch economic emission dispatch stochastic fractal search algorithm

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Multi-surrogate-assisted stochastic fractal search algorithm for high-dimensional expensive problems *

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INFORMATION SCIENCES 2023年第1期640卷

作者： Cheng, Xiaodi Yu, Yongguang Hu, Wei Beijing Jiaotong Univ Sch Math & Stat Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Syst Sci Beijing 100044 Peoples R China

Surrogate models have been radically used in metaheuristic algorithms owing to their capacity in solving computationally expensive problems. However, despite the promising performance of surrogate-assisted metaheuristic algorithms in coping with low-dimensional problems, they failed to tackle high-dimensional problems efficiently. Thus, a multi-surrogate-assisted stochastic fractal search algorithm (MSASFS) is proposed in this paper. Several improvements are integrated into the algorithm design: (1) By combining the original coordinate system with the eigencoordinate system, an improved surrogate-assisted differential evolution (SDE) updating mechanism is proposed to ameliorate the generalization ability of the algorithm and extend the scope of exploration. (2) A new expected improvement (EI) pre-screening strategy based on the Gaussian process (GP) model is employed to select promising candidate solutions. (3) Two different surrogate models are applied to enhance the robustness of the proposed algorithm. The effectiveness of MSASFS is further demonstrated by numerical experiments on some widely used benchmark problems with dimensions ranging from 30 to 200 and parameter estimation problem of fractional-order chaotic systems. The results reveal that, compared with state-of-the-art surrogate-assisted evolutionary algorithms (SAEAs), the proposed algorithm can effectively solve high-dimensional expensive problems. Furthermore, MSASFS shows a more significant efficiency when the dimension of problems becomes higher.

关键词： stochastic fractal search algorithm High-dimensional expensive problems Differential evolution Multi-surrogate model Eigencoordinate system

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Economical operation of modern power grids incorporating uncertainties of renewable energy sources and load demand using the adaptive fitness-distance balance-based stochastic fractal search algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartA期117卷

作者： Duman, Serhat Kahraman, Hamdi Tolga Kati, Mehmet Bandirma Onyedi Eylul Univ Engn & Nat Sci Fac Elect Engn Bandirma Turkiye Karadeniz Tech Univ Technol Fac Software Engn Trabzon Turkiye HAVELSAN Ankara Turkiye

With the increase in the world population and the rapid developments in technology, the energy demands on modern electricity grids are also rising. In order to meet these demands, power systems are increasingly using renewable energy resources (RESs) in addition to traditional fossil fuel-powered generation units and thus, the structures being implemented in electricity grids are more complex. Consequently, the planning and operation of modern power systems presents important problems, one of which is that of the optimal power flow (OPF). With the integration of RESs, which are usually intermittent in nature, the OPF becomes a more difficult problem to solve. In this study, the OPF problem was designed under different operating cases, considering thermal, wind, solar, small-hydro, and tidal energy systems and load demand uncertainties. The adaptive fitness-distance balance selection-based stochastic fractal search (AFDB-SFS) algorithm was proposed to solve this designed OPF problem. The results for the proposed approach from the experimental studies were statistically evaluated and compared with the results obtained from competitive optimization algorithms in the literature. The comparison demonstrated that the proposed AFDB-SFS algorithm was able to outperform the other algorithms in finding the optimal solution, and convergence speed to the optimal solution. According to the experimental study results, the proposed AFDB-SFS algorithm was able to optimize cost by 5.7362%, 0.0954%, 7.6244%, 0.17871%, 2.4307%, 0.12585%, 2.01729%, 1.7408%, 1.95317%, 3.5486%, 2.2007%, and 1.5203% better than the AO, GBO, GPC, HGS, HHO, RUN, TSO, LSHADE, LSHADE-EPSIN, LSHADE-CNEPSIN, LSHADE-SPACMA, and MadDE optimization algorithms in the proposed OPF problem. The source codes of the AFDB-SFS algorithm (proposed method) can be accessed at this link: https://***/matlabcentral/ fileexchange/118485-afdb-sfs.

关键词： Optimal power flow Renewable energy sources Modern electricity grids Adaptive fitness-distance balance stochastic fractal search algorithm Optimization

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An improved stochastic fractal search algorithm for 3D protein structure prediction

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Journal of molecular modeling 2018年第6期24卷 125页

作者： Changjun Zhou Chuan Sun Bin Wang Xiaojun Wang Key Laboratory of Advanced Design and Intelligent Computing (Dalian University) Ministry of Education Dalian 116622 China. zhou-chang231@***. Key Laboratory of Advanced Design and Intelligent Computing (Dalian University) Ministry of Education Dalian 116622 China.

Protein structure prediction (PSP) is a significant area for biological information research, disease treatment, and drug development and so on. In this paper, three-dimensional structures of proteins are predicted based on the known amino acid sequences, and the structure prediction problem is transformed into a typical NP problem by an AB off-lattice model. This work applies a novel improved stochastic fractal search algorithm (ISFS) to solve the problem. The stochastic fractal search algorithm (SFS) is an effective evolutionary algorithm that performs well in exploring the search space but falls into local minimums sometimes. In order to avoid the weakness, Lvy flight and internal feedback information are introduced in ISFS. In the experimental process, simulations are conducted by ISFS algorithm on Fibonacci sequences and real peptide sequences. Experimental results prove that the ISFS performs more efficiently and robust in terms of finding the global minimum and avoiding getting stuck in local minimums.

关键词： AB off-lattice model Internal feedback information Lvy flight Protein structure prediction stochastic fractal search algorithm

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Frequency and Temperature control scheme for AGC of a CCGT and WTG integrated Isolated System

Frequency and Temperature control scheme for AGC of a CCGT a...

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International Conference on Computing, Power and Communication Technologies (GUCON)

作者： Saha, Debdeep Saikia, L. C. Tasnin, Washima Saha, Arindita Rajbongshi, Rumi Natl Inst Technol Silchar Dept Elect Engn Silchar Assam India

ISBN: (纸本)9781538644911

This article develops a control scheme for automatic generation control (AGC) of a realistic isolated power system comprising of combined cycle gas turbine (CCGT) plant and wind turbine generator (WTG). Robust AGC operation of the hybrid system is done by secondary control action in association with airflow and temperature control. Integral-derivative with filter coefficient (IDF) controller as the secondary, airflow and temperature controlling unit for the test system outperforms other classical controllers for variant perturbations. A novel control strategy has been proposed where IDF in association with integral (I) controller in different combination for the three controlling action has been proposed for the perturbed isolated system. The proposed combination satisfactorily mitigates the frequency and temperature deviation of the test system. The same combination has been simulated for random wind variation in WTG plant against low to high perturbation. Simulation results authenticate the efficacy of the proposed control strategy.

关键词： Automatic generation control Combined cycle gas turbine Inlet guide vane stochastic fractal search algorithm wind turbine generator

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