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Multi-objective Allocation of Multi-type Distributed Generators along Distribution Networks Using backtracking search algorithm and Fuzzy Expert Rules

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2016年第3期44卷 252-267页

作者： El-Fergany, Attia Zagazig Univ Fac Engn Dept Elect Power & Machines POB 44519 Zagazig Egypt

This article presents a methodology for a multi-objective optimal allocation of multi-type distributed generators in radial distribution networks based on a backtracking search algorithm. The study aims to prove the validity and the strength of the backtracking search algorithm multi-objective method on distributed generator allocation. The multi-objective function is expressed to minimize the network power losses, to consolidate the static voltage stability indices, and to ameliorate the bus's voltage profile. The indicators of loss sensitivity factors and bus voltage magnitudes are incorporated to establish set of fuzzy expert rules to assort the preliminary buses for distributed generator placement. The proposed methodology allows the fuzzy decision maker to decide the best compromise solution among the offered Pareto-optimal solutions. The salient features of the backtracking search algorithm are demonstrated and marked on 33- and 94-node radial distribution networks with various scenarios. The cropped results are compared with those reported by others in the literature, validating and signifying the proposed approach. The study finds that the type-3 distributed generator unit (delivers P and injects Q) is most preferred to reduce power losses along network lines and to boost both the bus voltage profile and voltage stability indices.

关键词： multi-distributed generators backtracking search algorithm multi-objective optimization fuzzy expert rules loss minimization static voltage stability indices

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CMOS analogue amplifier circuits optimisation using hybrid backtracking search algorithm with differential evolution

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2016年第4期28卷 719-749页

作者： Mallick, S. Kar, R. Mandal, D. Ghoshal, S. P. NIT Durgapur Dept Elect & Commun Engn Durgapur W Bengal India NIT Durgapur Dept Elect Engn Durgapur W Bengal India

This paper proposes a novel hybrid optimisation algorithm which combines the recently proposed evolutionary algorithm backtracking search algorithm (BSA) with another widely accepted evolutionary algorithm, namely, Differential Evolution (DE). The proposed algorithm called BSA-DE is employed for the optimal designs of two commonly used analogue circuits, namely Complementary Metal Oxide Semiconductor (CMOS) differential amplifier circuit with current mirror load and CMOS two-stage operational amplifier (op-amp) circuit. BSA has a simple structure that is effective, fast and capable of solving multimodal problems. DE is a stochastic, population-based heuristic approach, having the capability to solve global optimisation problems. In this paper, the transistors' sizes are optimised using the proposed BSA-DE to minimise the areas occupied by the circuits and to improve the performances of the circuits. The simulation results justify the superiority of BSA-DE in global convergence properties and fine tuning ability, and prove it to be a promising candidate for the optimal design of the analogue CMOS amplifier circuits. The simulation results obtained for both the amplifier circuits prove the effectiveness of the proposed BSA-DE-based approach over DE, harmony search (HS), artificial bee colony (ABC) and PSO in terms of convergence speed, design specifications and design parameters of the optimal design of the analogue CMOS amplifier circuits. It is shown that BSADE- based design technique for each amplifier circuit yields the least MOS transistor area, and each designed circuit is shown to have the best performance parameters such as gain, power dissipation, etc., as compared with those of other recently reported literature.

关键词： CMOS circuit sizing two-stage operational amplifier differential amplifier backtracking search algorithm differential evolution evolutionary optimisation techniques

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A backtracking search algorithm for the Simultaneous Size, Shape and Topology Optimization of Trusses

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LATIN AMERICAN JOURNAL OF SOLIDS AND STRUCTURES 2016年第15期13卷 2622-2651页

作者： Souza, Rafael R. Fadel Miguel, Leandro F. Iopez, Rafael H. Torii, Andre J. Miguel, Leticia F. F. Univ Fed Santa Catarina CORE R JoaoPio Duarte Sn BR-88034000 Florianopolis SC Brazil RuaCasteloBranco BR-55051900 Joao Pessoa PB Brazil Av SarmentoLeite 425 2 Andar BR-90050170 Porto Alegre RS Brazil

This paper presents a backtracking search Optimization algorithm (BSA) to simultaneously optimize the size, shape and topology of truss structures. It focuses on the optimization of these three aspects since it is well known that the most effective scheme of truss optimization is achieved when they are simultaneously considered. The minimization of structural weight is the objective function, imposing displacement, stress, local buckling and/or kinematic stability constraints. The effectiveness of the BSA at solving this type of optimization problem is demonstrated by solving a series of benchmark problems comparing not only the best designs found, but also the statistics of 100 independent runs of the algorithm. The numerical analysis showed that the BSA provided promising results for the analyzed problems. Moreover, in several cases, it was also able to improve the statistics of the independent runs such as the mean and coefficient of variation values.

关键词： backtracking search algorithm truss optimization topology optimization size optimization shape optimization mixed variable problem

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Enhanced backtracking search algorithm for Optimal Coordination of Directional Over-current Relays Including Distributed Generation

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2016年第3期44卷 278-290页

作者： Othman, Ahmed M. Abdelaziz, Almoataz Y. Zagazig Univ Elect Power & Machines Dept Fac Engn Zagazig Egypt Ain Shams Univ Dept Elect Power & Machines Fac Engn Abdo Basha Sq Cairo 11517 Egypt

In this article, the enhanced backtracking search algorithm is employed to achieve optimal coordination of directional over-current relays. A novel objective function is formulated to minimize the total operating times while maintaining the validity of the coordination time interval. The proposed technique is applied to optimize the influential variables of the coordination problem, which are plug tap setting, time dial setting, and type of inverse relay characteristics. Both old electromechanical and digital relays are considered in the study. The enhanced backtracking search algorithm is a recent heuristic-based optimization, and its performance in solving relay coordination problem is compared with other well-established algorithms to demonstrate its viability and effectiveness.

关键词： directional over-current relays optimal coordination plug tap setting time dial setting backtracking search algorithm

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Fuzzy logic speed controller optimization approach for induction motor drive using backtracking search algorithm

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MEASUREMENT 2016年 78卷 49-62页

作者： Abd Ali, Jamal Hannan, M. A. Mohamed, Azah Abdolrasol, Maher G. M. Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Elect Elect & Syst Engn Bangi 43600 Selangor Malaysia Minist Elect Gen Co Elect Prod Middle Reg Baghdad Iraq

This paper presents an adaptive fuzzy logic controller (FLC) design technique for controlling an induction motor speed drive using backtracking search algorithm (BSA). This technique avoids the exhaustive traditional trial-and-error procedure for obtaining membership functions (MFs). The generated adaptive MFs are implemented in speed controller design for input and output based on the evaluation results of the fitness function formulated by the BSA. In this paper, the mean absolute error (MAE) of the rotor speed response for three phase induction motor (TIM) is used as a fitness function. An optimal BSA-based FLC (BSAF) fitness function is also employed to tune and minimize the MAE to improve the performance of the TIM in terms of changes in speed and torque. Moreover, the measurement of the real TIM parameters via three practical tests is used for simulation the TIM. Results obtained from the BSAF are compared with those obtained through gravitational search algorithm (GSA) and particle swarm optimization (PSO) to validate the developed controller. Design procedure and accuracy of the develop FLC are illustrated and investigated via simulation tests for TIM in a MATLAB/Simulink environment. Results show that the BSAF controller is better than the GSA and PSO controllers in all tested cases in terms of damping capability, and transient response under different mechanical loads and speeds. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Fuzzy logic control backtracking search algorithm Gravitational search algorithm Particle swarm optimization Induction motor Scalar controller

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Parameter Identification of Nonlinear Muskingum Model with backtracking search algorithm

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WATER RESOURCES MANAGEMENT 2016年第8期30卷 2767-2783页

作者： Yuan, Xiaohui Wu, Xiaotao Tian, Hao Yuan, Yanbin Adnan, Rana Muhammad Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China Wuhan Univ Technol Sch Resource & Environm Engn Wuhan 430070 Peoples R China

Nonlinear Muskingum model is a popular approach widely used for flood routing in hydraulic engineering. An improved backtracking search algorithm (BSA) is proposed to estimate the parameters of nonlinear Muskingum model. The orthogonal designed initialization population strategy and chaotic sequences are introduced to improve the exploration and exploitation ability of BSA. At the same time, a selection strategy based individual feasibility violation is developed to ensure that the computed outflows are non-negative in the evolutionary process. Finally, three examples are employed to demonstrate the performance of the improved BSA. The comparison between the results of routing outflows and those of Wilcoxon signed ranks test shows that the improved BSA outperforms particle swarm optimization, genetic algorithm, differential evolution and other algorithms reported in the literature in terms of solution quality. Therefore, it is reasonable to draw the conclusion that the proposed BSA is a satisfactory and efficient choice for parameter estimation of nonlinear Muskingum model.

关键词： Nonlinear Muskingum model Parameter identification backtracking search algorithm Flood routing Wilcoxon signed ranks test

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Optimal power flow of two-terminal HVDC systems using backtracking search algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2016年 78卷 326-335页

作者： Ayan, Kursat Kilic, Ulas Sakarya Univ Fac Comp & Informat Sci Dept Comp Engn Sakarya Turkey Celal Bayar Univ Fac Hasan Ferdi Turgutlu Technol Dept Mechatron Engn Turgutlu Manisa Turkey

This paper presents a solution technique for optimal power flow (OPF) of high-voltage direct current (HVDC) power systems using a backtracking search algorithm (BSA). BSA is a population-based evolutionary algorithm (EA), and it is not sensitive to initial conditions, contrary to most other meta-heuristic algorithms. The proposed algorithm is applied to three different test systems as follows: the modified 5-bus test system, the modified WSCC 9-bus test system, and the modified New England 39-bus test system. As a result of the simulations, minimum, maximum, and average production costs and CPU times are obtained for different cases of each of the three test systems. These results are also compared to those of the Artificial Bee Colony (ABC) algorithm, the Genetic algorithm (GA), and the unified method provided in literature. In regard to the comparative results, it can be said that the proposed method has a shorter CPU time and is more efficient than the others. Thus, the applicability and efficiency of the proposed method in this field are demonstrated. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： backtracking search algorithm Optimal power flow HVDC Power system

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Energy-efficient multi-pass turning operation using multi-objective backtracking search algorithm

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JOURNAL OF CLEANER PRODUCTION 2016年第0期137卷 1516-1531页

作者： Lu, Chao Gao, Liang Li, Xinyu Chen, Peng Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan Peoples R China

Energy savings have become an essential consideration in sustainable manufacturing projects due to the associated environmental impacts and constraints on carbon emissions. In the past, machining operations primarily examined technological consideration (e.g., machining quality) and neglected energy consumption. Therefore, this paper investigates an energy-efficient multi-pass turning operation problem and establishes a multi-objective multi-pass turning operations model. Energy consumption and machining quality are both considered in this problem. Although several models of this problem have considered these criteria, the objectives are usually combined into a single objective using a weighted sum approach, which results in poor non-dominated solutions. To obtain high quality trade-offs between the two challenging objectives, a novel multi-objective backtracking search algorithm is proposed to solve this multi-objective optimization problem. To verify the feasibility and validity of the proposed algorithm, it is compared with other classical multi-objective metaheuristics on multi-objective multi pass turning operations. This study's experimental results demonstrate that the proposed algorithm significantly outperforms other algorithms for this optimization problem, which is a significant result regarding practical application. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Multi-objective evolutionary algorithm Energy consumption Parameter optimization backtracking search algorithm

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Learning backtracking search optimisation algorithm and its application

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INFORMATION SCIENCES 2017年 376卷 71-94页

作者： Chen, Debao Zou, Feng Lu, Renquan Wang, Peng Huaibei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China

The backtracking search algorithm (BSA) is a recently proposed evolutionary algorithm (EA) that has been used for solving optimisation problems. The structure of the algorithm is simple and has only a single control parameter that should be determined. To improve the convergence performance and extend its application domain, a new algorithm called the learning BSA (LBSA) is proposed in this paper. In this method, the globally best information of the current generation and historical information in the BSA are combined to renew individuals according to a random probability, and the remaining individuals have their positions renewed by learning knowledge from the best individual, the worst individual, and another random individual of the current generation. There are two main advantages of the algorithm. First, some individuals update their positions with the guidance of the best individual (the teacher), which makes the convergence faster, and second, learning from different individuals, especially when avoiding the worst individual, increases the diversity of the population. To test the performance of the LBSA, benchmark functions in CEC2005 and CEC2014 were tested, and the algorithm was also used to train artificial neural networks for chaotic time series prediction and nonlinear system modelling problems. To evaluate the performance of LBSA with some other EAs, several comparisons between LBSA and other classical algorithms were conducted. The results indicate that LBSA performs well with respect to other algorithms and improves the performance of BSA. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Evolutionary algorithm backtracking search algorithm Teaching-learning based optimisation Artificial neural network training Optimisation problems

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Wind Thermal Economic Emission Dispatch Solution Using Multi-Objective backtracking search algorithm

Wind Thermal Economic Emission Dispatch Solution Using Multi...

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International Renewable and Sustainable Energy Conference

作者： Fatima Daqaq Mohamed Ouassaid Rachid Ellaia Engineering for Smart and Sustainable Systems Research Center Mohammed V University Rabat Morocco

This paper treats the problem of Economic Emission Dispatch considering the wind power. This model of Wind Thermal Economic Emission Dispatch "WTEED" takes into account both wind turbine and thermal generators. The aim of "WTEED" is minimizing the level of atmospheric pollution and fuel cost. To characterize the impact of wind power, a closed form is derived in terms of the Incomplete Gamma Function (IGF). To solve the problem and find the optimum fuel cost, optimum emission and best compromise solution, a new algorithm has been applied named Multi-Objectives backtracking search algorithm "MOBSA". The proposed approach has been examined for standard IEEE 30-Bus test system, comprising six conventional thermal generators and two wind generators. The results found approve the effectiveness and robustness of the proposed technique and it was compared with other algorithms as SPEA, MALO, and GAEPSO.

关键词： Economic dispatch Emission dispatch Wind power backtracking search algorithm Multi-Objectives Optimization

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