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China International Conference on Electricity Distribution (CICED)

作者： Kong, Tao Jia, Mingna Sun, Guihua Shandong Univ Technol Zibo 255049 Peoples R China Shandong Kehui Power Automat Co LTD Zibo 255087 Peoples R China

ISBN: (纸本)9781467390682

For the difficulty of traditional fault location method brought by DG, an improved bacterial foraging algorithm is applied to fault location in distribution networks with DG. This method abandoned the construction of complex switching functions in the original intelligent bionic method. The relationship between the switch and the line is hided in the network relationship matrix, and the impact brought by DG is hided in the DG matrix. The mathematical model is improved so that it can be better adapt to multi-DG, multi-fault case. Even when the information is distorted, this method still has the ability of fault tolerance. Simulation examples demonstrate the feasibility of this program.

关键词： bacterial foraging algorithm distribution network DG fault location

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Proportional and Integral constants Optimization Using bacterial foraging algorithm for Boost Inverter 5th

Proportional and Integral constants Optimization Using Bacte...

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5th International Conference on Advances in Energy Research (ICAER)

作者： Arunkumar, G. Gnanambal, I Karthik, P. C. Naresh, S. Vellore Inst Technol Vellore Tamil Nadu India Anna Univ Govt Coll Engn Salem India

A boost dc-ac inverter is one which is capable of generating in a single stage ac voltage whose peak value can be higher or lower than the given input dc voltage. The major problem with this system is that the closed loop gain parameters kp and ki have to be optimized because these parameters help us to get desired result with better system response by lowering the rise time, settling time, peak overshoot and steady state error. Moreover when they are not optimized load line disturbances arise because of which the stability of output voltage decreases and THD value increases. So to overcome these difficulties bacterial foraging algorithm is being used. (C) 2016 The Authors. Published by Elsevier Ltd.

关键词： Boost Inverter bacterial foraging algorithm PI Controller

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Utilization of bacterial foraging algorithm for Optimization of Boost Inverter Parameters

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Circuits and Systems 2016年第8期7卷 1430-1440页

作者： G. Arunkumar Dr. I. Gnanambal School of Electrical Engineering VIT University Vellore India Department of Electrical and Electronics Engineering GCE Salem Anna University Chennai India

This paper proposes a boost inverter model capable of coping with changes in load as well as line parameters. In order to achieve an output AC voltage higher than the input DC voltage, we can use this model consisting of a pair of DC-DC converters with a load connected differentially across them. This paper aims at developing a boost inverter that is capable of achieving a very high gain, to obtain an AC voltage of 110 Vrms from a DC input of 36 V. This is exceptionally beneficial in renewable energy applications, where the input voltage garnered is quite small, and in need of stepping up for commercial use or transmission. However, aside from the voltage level itself, lowering the rise time, settling time, peak overshoot and steady state error of the system is of cardinal importance in order to maintain a reliable output voltage. Closed loop control of the differentially connected DC-DC converters is necessary to determine the optimal stable operating point. This paper addresses the above concerns through optimization of the proportional and integral constants using the novel bacterial foraging algorithm, ensuring operation at the required optimal stable operating point. Moreover, load/line disturbances may occur due to which the stability of output voltage may be compromised and THD value may increase to undesirable extents. In these cases, utilization of the output voltage is no longer viable for several applications sensitive to such voltage fluctuations. We have demonstrated that our proposed model is capable of restoring/reverting to the satisfactory sinusoidal waveform fashion within a single voltage cycle. The waveform results that demonstrate the resilience of our model to such disturbances are represented appropriately.

关键词： Boost Inverter bacterial foraging algorithm PI Controller

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Proportional and Integral Constants Optimization Using bacterial foraging algorithm for Boost Inverter

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Energy Procedia 2016年 90卷 535-539页

作者： G. Arunkumar I. Gnanambal P.C. Karthik S. Naresh Vellore Institute of Technology Vellore India Govt. College of Engineering Affiliated to Anna University Salem India

关键词： Boost Inverter bacterial foraging algorithm PI Controller.

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Quantum-inspired bacterial foraging algorithm for parameter adjustment in green cognitive radio

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Journal of Systems Engineering and Electronics 2015年第5期26卷 897-907页

作者： Hongyuan Gao Chenwan Li School of Information and Communication Engineering Harbin Engineering University

Parameter adjustment that maximizes the energy efficiency of cognitive radio networks is studied in this paper where it can be investigated as a complex discrete optimization problem. Then a quantum-inspired bacterial foraging algorithm(QBFA)is proposed. Quantum computing has perfect characteristics so as to avoid local convergence and speed up the optimization of QBFA. A proof of convergence is also given for this *** superiority of QBFA is verified by simulations on three test functions. A novel parameter adjustment method based on QBFA is proposed for resource allocation of green cognitive radio. The proposed method can provide a globally optimal solution for parameter adjustment in green cognitive radio networks. Simulation results show the proposed method can reduce energy consumption effectively while satisfying different quality of service(Qo S)requirements.

关键词： green cognitive radio parameter adjustment quantum computing bacterial foraging algorithm

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A chemotaxis-enhanced bacterial foraging algorithm and its application in job shop scheduling problem

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2015年第10期28卷 1106-1121页

作者： Zhao, Fuqing Jiang, Xin Zhang, Chuck Wang, Junbiao Lanzhou Univ Technol Sch Comp & Commun Lanzhou Peoples R China Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA Northwestern Polytech Univ Key Lab Contemporary Design & Integrated Mfg Tech Minist Educ Xian 710072 Peoples R China

In this article, a chemotaxis-enhanced bacterial foraging optimisation (CEBFO) is proposed to solve the job shop scheduling problem more effectively. The new approach, which is based on a new chemotaxis with the differential evolution (DE) operator added, aims at solving the tumble failure problem in the tumble step and accelerates the convergence speed of the original algorithm. The effectiveness of the new chemotaxis and the convergence are proved theoretically and tested in continuous problems. Furthermore, a local search operator was designed, which can improve the local search ability of novel algorithm greatly. Finally, the experiments were conducted on a set of 38 benchmark problems of job shop scheduling and the results demonstrated the outperformance of the proposed algorithm.

关键词： chemotaxis job shop scheduling hybrid optimisation difference mutation bacterial foraging algorithm

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Quantum-inspired bacterial foraging algorithm for parameter adjustment in green cognitive radio

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Journal of Systems Science & Complexity 2015年第5期28卷 897-907页

作者： Hongyuan Gao Chenwan Li School of Information and Communication Engineering Harbin Engineering University

关键词： green cognitive radio parameter adjustment quantumcomputing bacterial foraging algorithm

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bacterial foraging algorithm-based optimisation for controlling conditioner temperature of a ring die granulator

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2014年第4期22卷 357-365页

作者： Zhang, Kun Zhang, Peijian Wu, Jianguo Nia, Hossein Farid Ghassem Zhou, Huiyu Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China Nantong Univ Sch Elect Engn Nantong 226019 Jiangsu Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT3 9DT Antrim North Ireland

Support vector regression (SVR) is firmly grounded in the framework of statistical learning theory that has been deployed to solve many engineering problems in recent years. To optimise its parameters and achieve optimised results, this paper deploys a novel bacterial foraging algorithm (BFA), combining with support vector machine (SVM) to achieve the good condition temperature prediction of a ring die granulator. With a strong globally searching capability, the proposed method can achieve dynamic optimisation of systematic parameters and overcome the problem of inefficiency in selecting optimal parameters. Simulation results show that the proposed bacterial foraging algorithm is effective in parameter optimisation of the controller for a ring die granulator.

关键词： bio-inspired optimisation bacterial foraging algorithm BFA support vector machine SVM prediction model ring die granulator

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Control of voltage profile with optimal control and placement of distributed generation using the refined bacterial foraging algorithm

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JOURNAL OF VIBRATION AND CONTROL 2014年第13期20卷 2006-2018页

作者： Jaganathan, S. Palaniswami, S. Anna Univ Madras 600025 Tamil Nadu India Govt Coll Engn Sulur Coimbatore 641402 Tamil Nadu India

This paper proposes a refined bacterial foraging algorithm (RBFA) for solving the multi-objective based optimal power dispatch with optimal placement of distributed generation (DG) to minimize the total real power loss, generation cost, the environmental emission and considering various controls and limits. The RBFA is based on the social foraging behavior of the Escherichia coli bacteria and its improved version of the basic bacterial foraging algorithm. The RBFA provides natural selection to eliminate poor foraging strategies for bacteria and to propagate other successful foraging strategies where foraging is proceeded using a position updating process, step length, search dimension and search direction with adaptation of basic foraging principles. Initially, the algorithm randomly generates the particle positions representing the size and location of DG and its proposal to solve the simultaneous optimization of the multi-objective problem. The proposed RBFA is used to determine the optimal sizes and locations of multi-DGs;the different types of DG are considered and the load flow is used to calculate the exact loss and to minimize simultaneously the economic cost and the emission of thermal units by changing the location and varying the sizes of the DG units. The test results indicate that the RBFA method can obtain better results than similar social behavior algorithm method on the IEEE30-bus system. The results are compared with and without DG units. The proposed method found the optimal location and sizing of DG units with control of the voltage profile, control of the cost of generation and control and reduction of environmental pollution and transmission losses.

关键词： bacterial foraging algorithm control of voltage profile control of optimal power dispatch distributed generation load flow solution refined bacterial foraging algorithm stability indices

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Synergy of Adaptive bacterial foraging algorithm and Particle Swarm Optimization algorithm for Image Segmentation

Synergy of Adaptive Bacterial Foraging Algorithm and Particl...

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IEEE International Conference on Circuits, Power and Computing Technologies (ICCPCT)

作者： Sasithradevi, A. Singh, N. Nirmal VV Coll Engn Elect & Commun Engn Thisaiyanvilai India

ISBN: (纸本)9781479923977

Many practical applications such as medical image segmentation, object detection, recognition tasks and video surveillance have need for accurate image segmentation techniques. Hence image segmentation is an important technique for image processing which is regarded as first step for image analysis. In this paper an image segmentation technique based on bacterial foraging (BF) and Particle Swarm Optimization (PSO) algorithm is addressed. Initially adaptation is done on BF algorithm by computing the step length using the number of variables in the search space. Further, on exhaustive analysis of BF algorithm, it was revealed that the tumble behavior will lead to random delay in searching optimal solutions and premature convergence. This synergy algorithm makes use of PSO in providing social information and adaptive BF algorithm in finding new optimal threshold values using elimination and dispersal. The proposed method has been applied to few benchmark images with promising results.

关键词： Image segmentation bacterial foraging algorithm Particle Swarm Optimization Bi-level Thresholding

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