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bacterial foraging algorithm based solar PV parameter estimation

SOLAR ENERGY 2013年第Nov.期97卷 255-265页

作者： Rajasekar, N. Kumar, Neeraja Krishna Venugopalan, Rini VIT Univ Sch Elect Engn Vellore Tamil Nadu India

The abundance and non-polluting nature of solar energy has aroused the interest of many researchers. This worldwide attention of photovoltaic panels has led to the need of generating accurate model for solar photovoltaic (PV) module before proceeding to the installation part. However, accurate modeling of solar PV characteristics is difficult;since the manufacturer's datasheet provides only four values such as V-mp, I-mp, V-oc, and I-sc. Further, for accurate modeling precise estimation of model parameters at different environmental conditions are very essential. On the other hand, optimization technique is a very powerful tool to obtain solutions to complex non-linear problems. Hence, in this paper, bacterial foraging algorithm is proposed to model the solar PV characteristics accurately. A new equation has been evolved to determine the values of V-oc, V-mp accurately;since these values decides the closeness of the simulated characteristics. Model parameters are extracted for three different types of solar PV panels. A systematic evaluation and performance comparison of bacterial foraging algorithm with other optimization techniques such as Genetic algorithm and Artificial Immune System has been done and the best computational technique is derived based on performance criteria such as accuracy, consistency, speed of convergence and absolute error. Extensive computations are carried out for the proposed method, as well as for Genetic algorithm and Artificial Immune System to substantiate the findings. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： bacterial foraging algorithm Genetic algorithm Artificial Immune System Solar PV modeling

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bacterial foraging algorithm application for induction motor field efficiency estimation under unbalanced voltages

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MEASUREMENT 2013年第7期46卷 2232-2237页

作者： Sousa Santos, Vladimir Viego Felipe, Percy Gomez Sarduy, Julio Univ Cienfuegos Fac Engn Dept Mech Engn Cienfuegos Cuba Univ Cienfuegos Fac Engn Ctr Energy & Environm Studies CEEMA Cienfuegos Cuba

In electric supply systems of industry and other entities, it is very common to find problems of power quality. Among these problems, unbalance and voltage deviation is one of the most common. They primarily affect the operation of electric motors. This paper proposes the use of a bacterial foraging algorithm as an economic, accurate and low-invasive tool, to obtain in field conditions, the output power, losses and efficiency of induction motors feed with unbalanced voltages. The algorithm is based on estimating the motor parameters of positive and negative equivalent circuits. The tool was tested on a 7.5 kW motor for different load conditions and high levels of voltage unbalance, yielding to accurate results, and demonstrating its effectiveness for performing energy audits in situ on unbalanced voltages conditions. This application of bacterial foraging technique has not been reported in the literature. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： In-service motor bacterial foraging algorithm Unbalanced voltages Efficiency estimation

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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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bacterial foraging algorithm for Null Synthesizing of Linear Antenna Arrays by Controlling Only the Element Positions

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2011年第3期150卷 635-656页

作者： Guney, K. Basbug, S. Nuh Naci Yazgan Univ Fac Engn TR-38040 Kayseri Turkey Nevsehir Univ Dept Ind Elect Vocat Coll TR-50300 Nevsehir Turkey

A method based on the bacterial foraging algorithm (BFA) for the pattern synthesis of linear antenna arrays with the prescribed nulls is presented. Nulling of the pattern is achieved by controlling only the element positions. The BFA is a new evolutionary computing technique, based on the foraging behavior of Escherichia (E) coli bacteria in a human intestine. Simulation results for Chebyshev patterns with the imposed single, multiple and broad nulls are given to show the effectiveness of the proposed method. For practical consideration, due to small variations of the element positions, the sensitivity of the produced patterns is also examined by rounding the element position values to the second decimal position.

关键词： Antenna array Pattern nulling bacterial foraging algorithm Position-only control Pattern synthesis

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bacterial foraging algorithm with varying population

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BIOSYSTEMS 2010年第3期100卷 185-197页

作者： Li, M. S. Ji, T. Y. Tang, W. J. Wu, Q. H. Saunders, J. R. Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England Univ Liverpool Sch Biol Sci Liverpool L69 3GJ Merseyside England

Most of evolutionary algorithms (EAs) are based on a fixed population. However, due to this feature, such algorithms do not fully explore the potential of searching ability and are time consuming. This paper presents a novel nature-inspired heuristic optimization algorithm: bacterial foraging algorithm with varying population (BFAVP), based on a more bacterially-realistic model of bacterial foraging patterns, which incorporates a varying population framework and the underlying mechanisms of bacterial chemotaxis, metabolism, proliferation, elimination and quorum sensing. In order to evaluate its merits, BFAVP has been tested on several benchmark functions and the results show that it performs better than other popularly used EAs, in terms of both accuracy and convergency. Crown Copyright (C) 2010 Published by Elsevier Ireland Ltd. All rights reserved.

关键词： bacterial foraging algorithm bacterial colony behaviors Optimization

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bacterial foraging algorithm based on PSO with Adaptive Inertia Weigh for Solving Nonlinear Equations Systems

Bacterial foraging algorithm based on PSO with Adaptive Iner...

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3rd International Conference on Advances in Materials Manufacturing (ICAMMP 2012)

作者： Mai Xiongfa Li Ling Guangxi Teachers Educ Key Lab Sci Comp & Intelligent Informat Proc Univ Nanning 530001 Peoples R China

ISBN: (纸本)9783037856482

bacterial foraging algorithm (BFA) has recently emerged as a very powerful technique for optimization,but it also confronts the problems of slow convergence and premature convergence. To overcome the drawbacks of BFA, This article merge the idea of particle swarm optimization algorithm with adaptive inertia weigh into the bacterial foraging to improve the speed and convergence capabilities of BFA, and according to this a bacterial foraging algorithm based on PSO(APSO-BFA) is presented. Simulation results on five systems of nonlinear equations show that the proposed algorithm is superior to the other two kinds of bacterial foraging algorithm.

关键词： bacterial foraging algorithm Particle Swarm Optimization adaptive inertia weigh

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bacterial foraging algorithm based Image Fusion in Contourlet Domain

Bacterial Foraging Algorithm based Image Fusion in Contourle...

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International Conference on Advanced Materials and Information Technology Processing (AMITP 2011)

作者： Ma, Miao Liu, Yanli Dang, Xiaofei Shaanxi Normal Univ Coll Comp Sci Xian Shaanxi Peoples R China

ISBN: (纸本)9783037851579

To improve the overall performance of image fusion, this paper proposes a bacterial foraging algorithm (BFA) based method. First, this method selects several objective standards to construct an index representing the overall performance of fused images. Second, after the source images were decomposed by Contour let transform, we obtain the coefficients of low-frequency and high-frequency in Contour let domain. Then, the swarm intelligence of BFA is introduced to determine the proportions of high-frequency coefficients, i.e. the optimal weights. Finally, we use the high-frequency coefficients fused by the optimal weights and the average of low-frequency coefficients to reconstruct the optimal fused image. Experimental results show that the method not only can provide with good visual effects, but also is superior to eight widely-used methods.

关键词： image fusion bacterial foraging algorithm Contourlet transform overall performance

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bacterial foraging algorithm based parameter estimation of solar PV model

Bacterial Foraging Algorithm based parameter estimation of s...

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Annual International Conference on Emerging Research Areas (AICERA) / International Conference on Microelectronics, Communication and Renewable Energy (ICMiCR)

作者： Krishnakumar, Neeraja Venugopalan, Rini Rajasekar, N. VIT Univ Sch Elect Engn Vellore Tamil Nadu India

ISBN: (纸本)9781467351492;9781467351508

The abundance and non-polluting nature of solar energy has aroused the interest of many researchers. This worldwide attention of photovoltaic panels has led to the need of generating accurate model of photovoltaic module before proceeding to the installation part. However, the modeling of solar PV characteristics is difficult since the manufacturer's datasheet provides only four values such as V-mp, I-mp, V-oc, and I-sc. For the accurate modeling of photovoltaic panels, the precise estimation of model parameters at different environmental conditions is very essential. Optimization technique is a very powerful tool to obtain the solution of complex non-linear problems. In this paper, the application of the bacterial foraging algorithm (BFA) for the accurate extraction of model parameters has been discussed. A systematic evaluation and performance comparison of BFA with other optimization techniques such as Genetic algorithm, Artificial Immune System etc. has been done and the best computational technique is determined based on certain criteria such as accuracy, consistency, speed of convergence etc. The computed data is compared with experimental data and the results are validated using two photovoltaic modules of different nature (multicrystalline and thin film).

关键词： bacterial foraging algorithm Genetic algorithm Artificial Immune algorithm Photovoltaic module

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bacterial foraging algorithm Based on Quantum-Behaved Particle Swarm Optimization for Global Optimization

Bacterial Foraging Algorithm Based on Quantum-Behaved Partic...

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3rd International Conference on Advances in Materials Manufacturing (ICAMMP 2012)

作者： Li Ling Mai Xiongfa Guangxi Teachers Educ Key Lab Sci Comp & Intelligent Informat Proc Univ Nanning 530001 Peoples R China

ISBN: (纸本)9783037856482

bacterial foraging Optimization(BFA) algorithm has recently emerged as a very powerful technique for real parameter optimization,but the *** algorithm depends on random search directions which may lead to delay in reaching the global solution. The quantum-behaved particle swarm optimization (QPSO) algorithm may lead to possible entrapment in local minimum solutions. In order to overcome the delay in optimization and to further enhance the performance of BFA,a bacterial foraging algorithm based on QPSO(QPSO-BFA) is presented. The new algorithm is proposed to combines both algorithms' advantages in order to get better optimization values. Simulation results on eight benchmark functions show that the proposed algorithm is superior to the BFA,QPSO and BF-PSO.

关键词： bacterial foraging algorithm Quantum-behaved Particle Swarm Optimization Benchmark

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bacterial foraging algorithm Application for Induction Motor Field Efficiency Estimation under Harmonics and Unbalanced Voltages

Bacterial Foraging Algorithm Application for Induction Motor...

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IEEE International Electric Machines and Drives Conference (IEMDC)

作者： Sousa Santos, Vladimir Quispe, Enrique C. Gomez Sarduy, Julio R. Viego, Percy R. Lemozy, Norberto Jurado, Alejandro Brugnoni, Mario Univ Cienfuegos Fac Engn Cienfuegos Cuba

ISBN: (纸本)9781467349741;9781467349758

The time harmonic flow in industrial electrical grids with voltage deviations and unbalance, signifiantly affects three phase induction motor operation and makes difficult energy characterization. There are different methods for in situ estimation of motors efficiency. However, none of them have been sufficiently precise for the analysis under non-sinusoidal, deviated and unbalanced voltage conditions. This paper presents a method based on the equivalent circuits, with losses segregation and using bacterial foraging algorithm (BFA). It allows determining under field conditions and low invasiveness, energy efficiency of induction motors operating under the before mentioned conditions. This method was tested successfully on a 0.55 kW motor, fed with significant levels of unbalance and voltage harmonics.

关键词： bacterial foraging algorithm efficiency harmonic with unbalance voltage induction motor

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