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Novel bacterial foraging-based ANFIS for speed control of matrix converter-fed industrial BLDC motors operated under low speed and high torque

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NEURAL COMPUTING & APPLICATIONS 2018年第12期29卷 1411-1434页

作者： Sivarani, T. S. Jawhar, S. Joseph Kumar, C. Agees Kumar, K. Prem Arunachala Coll Engn Women Dept EEE Vellichanthai 629203 Tamil Nadu India Rajalakshmi Engn Coll Dept EEE Chennai Tamil Nadu India

In this paper, a novel adaptive neuro-fuzzy inference system (ANFIS)-based control technique optimized by bacterial foraging optimization algorithm for speed control of matrix converter (MC)-fed brushless direct current (BLDC) motor is presented. ANFIS is considered to be one of the most promising technologies for control of electrical drives fed by MC. Optimizing the training parameters of ANFIS, to improve its performance, is still being considered by several researchers recently. Parameters of the online ANFIS controller such as learning rate (eta), forgetting factor (lambda) and steepest descent momentum constant (alpha) are optimized by using the proposed algorithm. For the purpose of comparison, proportional integral derivative controller, fuzzy logic controller, PSO-ANFIS and BAT-ANFIS are considered. Set point tracking performances of the proposed system are carried out at various operating points for an industrial BLDC motor operating at a maximum rated speed of 380 rpm and torque of 6.4 N m. Time domain specifications such as rise time, settling time, peak time, steady-state error and peak overshoot in the presence and absence of load torque disturbances are presented. Time integral performance measures such as integral square error, integral absolute error, and integral time multiplied absolute error are analyzed for various operating conditions. Speed fluctuation in the output of BLDC motor is dependent on the source current harmonics of the inverter/converter. To illustrate this, total harmonic distortion (THD) analysis is carried out for the existing PWM inverter and the proposed MC, and it is proved that MC results in reduced THD, as compared to PWM inverter. Simulation results confirm that the proposed controller outperforms the other existing control techniques under various set speed and torque conditions. Statistical analysis is effectively carried out to prove the effectiveness of the proposed controller. Experimental analysis is performed to val

关键词： Brushless direct current motor Fuzzy logic controller bacterial foraging optimization algorithm Adaptive neuro-fuzzy inference system Particle swarm optimization Bat algorithm Matrix converter Total harmonic distortion Speed control Statistical measures

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Hybrid BFPSO Approach for Effective Tuning of PID Controller for Load Frequency Control Application in an Interconnected Power System

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2017年第3期12卷 1027-1037页

作者： Anbarasi, S. Muralidharan, S. Mepco Schlenk Engn Coll Dept Elect & Elect Engn Sivakasi India

Penetration of renewable energy sources makes the modern interconnected power systems to have more intelligence and flexibility in the control. Hence, it is essential to maintain the system frequency and tie-line power exchange at nominal values using Load Frequency Control (LFC) for efficient, economic and reliable operation of power systems. In this paper, intelligent tuning of the Proportional Integral Derivative (PID) controller for LFC in an interconnected power system is considered as a main objective. The chosen problem is formulated as an optimization problem and the optimal gain parameters of PID controllers are computed with three innovative swarm intelligent algorithms named Particle Swarm optimization (PSO), bacterial foraging optimization algorithm (BFOA) and hybrid bacterial foraging Particle Swarm optimization (BFPSO) and a comparative study is made between them. A new objective function designed with necessary time domain specifications using weighted sum approach is also offered in this report and compared with conventional objective functions. All the simulation results clearly reveal that, the hybrid BFPSO tuned PID controller with proposed objective function has better control performances over other optimization methodologies.

关键词： bacterial foraging optimization algorithm Hybrid bacterial foraging particle swarm optimization algorithm Load frequency control Particle swarm optimization algorithm PID controller

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A hybrid MACO and BFOA algorithm for power loss minimization and total cost reduction in distribution systems

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2017年第1期25卷 337-351页

作者： Sankaramoorthy, Muthubalaji Veluchamy, Malathi Lathamathavan Engn Coll Dept Elect & Elect Engn Madurai Tamil Nadu India Anna Univ Dept Elect & Elect Engn Reg Campus Madurai Tamil Nadu India

This paper presents a multiobjective optimization methodology to optimally place a STATCOM in electric power distribution networks. The combination of multiobjective ant colony optimization (MACO) and the bacterial foraging optimization algorithm (BFOA) is proposed to minimize the power loss and total cost. The main intention of this analysis is to optimally place the STATCOM at multiple locations such as the transmission side, middle, and load side. Identifying the type and location of the STATCOM is a combinatorial optimization problem in power systems. In order to overcome this problem, the combination of hybrid MACO and BFOA algorithms is applied in this analysis to minimize the total cost and power loss. The total cost of the overall network is calculated by using system average interruption duration index and system average interruption frequency index metrics. Moreover, the BFOA is used in this paper to minimize the power loss during distribution, which is adequate in searching for the optimal solution. The problem of reducing power losses in distribution systems through the BFOA approach is described here for a 5-bus system. Test results of a 5-bus network show that the proposed MACO with BFOA method can efficiently ensure the power loss and total cost minimization.

关键词： Multi-objective ant colony optimization bacterial foraging optimization algorithm system average interruption duration index system average interruption frequency index STATCOM electric power distribution system reliability

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Image Compression Using Shannon Entropy-Based Image Thresholding 1

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3rd International Conference on Computational Intelligence in Data Mining (ICCIDM)

作者： Chiranjeevi, Karri Jena, Uma Ranjan Harika, Asha VSSUT Dept Elect & Telecommun Engn Burla 768018 Odisha India

ISBN: (数字)9789811038747

ISBN: (纸本)9789811038747;9789811038730

In this paper, we proposed multilevel image thresholding for image compression using Shannon entropy which is maximized by the nature-inspired bacterial foraging optimization algorithm (BFOA). Ordinary threading methods are computationally expensive, while extending for multilevel image thresholding, so there is a need of optimization techniques to reduce the computational time. Particle swarm optimization undergoes instability when particle velocity is maximum. So we proposed a BFOA-based multilevel image thresholding by maximizing Shannon entropy and the results are compared with differential evolution and Particle swarm optimization and proved better in Peak signal-to-noise ratio (PSNR), Compression ratio and reconstructed image quality.

关键词： Image compression Image thresholding Shannon entropy bacterial foraging optimization algorithm Differential evolution

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Active Noise Control using bacterial foraging optimization algorithm

Active Noise Control using Bacterial Foraging Optimization A...

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IEEE 10th International Conference on Signal Processing

作者： Gholami-Boroujeny, Shiva Eshghi, Mohammad Shahid Beheshti Univ Elect & Comp Engn Fac Tehran Iran

ISBN: (纸本)9781424458981

This paper proposes a nonlinear ANC system in which a new evolutionary algorithm based on the foraging behavior of E. coli bacteria is used for adaptive nonlinear filter coefficients optimization, called BFA_ANC. While the standard LMS based nonlinear filters may converge to local minima, the evolutionary algorithms may handle this problem efficiently and converge to the global minima. In addition, this class of algorithms does not require the identification of the secondary paths. Computer simulations show the major improvements in final residual noise, as well as the processing time of the proposed ANC system in comparison to the other techniques in the literature.

关键词： active noise control evolutionary algorithm bacterial foraging optimization algorithm BFA_ANC

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Hybrid achievement oriented computational chemotaxis in bacterial foraging optimization: a comparative study on numerical benchmark

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SOFT COMPUTING 2015年第12期19卷 3647-3663页

作者： Yildiz, Y. Emre Altun, Oguz Epoka Univ Dept Comp Engn Tirana Albania Yildiz Tech Univ Dept Comp Engn Istanbul Turkey

The social foraging behavior of Escherichia coli bacteria has been recently used for solving complex real-world search and optimization problems. bacterial foraging optimization algorithm (BFOA) is an important global optimization method inspired from this behavior. In this paper, a novel method called chemotaxis differential evolution optimization algorithm (CDEOA), which augments BFOA with conditional introduction of differential evolution (DE) and Random Search operators, is proposed. Introduction of these operators is done considering the number of successful run and unsuccessful tumble steps of bacteria. CDEOA was compared with the classical BFOA, two variants of BFOA which use DE operators [Adaptive Chemotactic bacterial Swarm foraging optimization with Differential Evolution Strategy (ACBSFO_DES)], chemotaxis differential evolution (CDE), and the classical DE on all 30 numerical functions of the 2014 Congress on Evolutionary Computation (CEC 2014) Special Session and Competition on Single Objective Real Parameter Numerical optimization suite. CDEOA was also compared with four state-of-the-art DE variants that competed in CEC 2014. Statistics of the computer simulations over this benchmark suite indicate that CDEOA outperforms, or is comparable to, its competitors in terms of the quality of final solution and its convergence rates for high-dimensional problems.

关键词： bacterial foraging optimization algorithm Differential evolution Nature-inspired algorithms Hybrid BFOA Metaheuristics

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Optimal size and siting of multiple distributed generators in distribution system using bacterial foraging optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2014年 15卷 58-65页

作者： Imran, Mohamed A. Kowsalya, M. VIT Univ Sch Elect Engn Vellore 632014 Tamil Nadu India

Optimal location and size of distributed generation (DG) in the distribution system play a significant role in minimizing power losses, operational cost and improving voltage stability. This paper presents a new approach to find the optimal location and size of DG with an objective of minimizing network power losses, operational costs and improving voltage stability. Loss sensitivity factor is used to identify the optimal locations for installation of DG units. bacterial foraging optimization algorithm (BFOA) is used to find the optimal size of DG. BFOA is a swarm intelligence technique which models the individual and group foraging policies of the Escherichia coli bacteria as a distributed optimization process. The technical constraints of voltage and branch current carrying capacity are included in the assessment of the objective function. The proposed method has been tested on IEEE 33-bus and 69-bus radial distribution systems with various load models at different load levels to demonstrate the performance and effectiveness of the technique. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Distributed generation bacterial foraging optimization algorithm Power loss minimization Voltage stability Operational cost minimization

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Adaptive fuzzy sliding mode control for synchronization of uncertain non-identical chaotic systems using bacterial foraging optimization

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2014年第5期26卷 2567-2576页

作者： Alfi, Alireza Kalat, Ali Akbarzadeh Khooban, Mohammad Hassan Shahrood Univ Technol Fac Elect & Robot Engn Shahrood Iran Shahrood Univ Technol Automat & Artificial Intelligence Res Ctr Shahrood Iran Islamic Azad Univ Sarvestan Branch Dept Elect Engn Sarvestan Iran

This paper introduces a novel hybrid control strategy, namely an optimal adaptive fuzzy sliding mode (OAFSM) control scheme, to realize the synchronization of general uncertain chaotic systems in master-slave configuration. The proposed controller not only guaranties the stability and robustness against the lumped uncertainties caused by unmodeled dynamics and external disturbances, but also significantly reduces the control chattering inherent in conventional sliding mode control. The chaos synchronization is obtained by optimal proper choice of the control parameters including the sliding surface and the reaching law parameters. To achieve this, a bio-mimetic algorithm namely bacterial foraging optimization algorithm (BFOA) is employed. An illustrative example is given to demonstrate the validity and confirm the performance of the proposed scheme.

关键词： Chaos synchronization sliding mode control fuzzy systems lyapunov stability theory uncertain nonlinear systems bacterial foraging optimization algorithm

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A Novel palmprint authetication system by XWT based feature extraction and BFOA based feature selection and optimization 2

A Novel palmprint authetication system by XWT based feature ...

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IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)

作者： Sanyal, Nandita Chatterjee, Amitava Munshi, Sugata BP Poddar Inst Management & Technol Dept Elect Engn 137 VIP Rd Kolkata 700052 India Jadavpur Univ Dept Elect Engn Kolkata 700032 India

ISBN: (纸本)9781479983490

This paper is about a new palm-print based biometric authentication system. It is low-cost as it employs a scanner coupled to a PC, for acquiring the image of the human palm. The histogram of the palm-image is cross wavelet transformed(XWT) with respect to that of a reference palm-image. Several features are extracted from the resulting spectrum, and a trained ANN is used to identify the subject, based on these features. bacterial foraging optimization algorithm(BFOA) is used to select the combination of features that results in the best performance. An Adaptive bacterial foraging optimization algorithm (ABFOA) is also introduced to further augment the performance of the system. Extensive experimental investigations reveal that an authentication accuracy of more than 97% can be obtained with the system.

关键词： ANN Biometric Authentication bacterial foraging optimization algorithm Cross wavelet transform Feature optimization Palm print identification

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Substation Expansion Planning Based on BFOA 30

Substation Expansion Planning Based on BFOA

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30th International Power System Conference (PSC)

作者： Rad, H. Kiani Moravej, Z. Semnan Univ Fac Elect & Comp Engn Semnan Iran

ISBN: (纸本)9781509027057

In recent years, significant research efforts have been devoted to the optimal planning of power systems. Substation Expansion Planning (SEP) as a sub-system of power system planning consists of finding the most economical solution with the optimal location and size of future substations and/or feeders to meet the future demand. The large number of design variables, and combination of discrete and continuous variables makes the substation expansion planning a very challenging problem. So far, various methods have been presented to solve such a complicated problem. Since the bacterial foraging optimization algorithm (BFOA) has been proper results in studies of power systems, and has not been applied to SEP problem yet, this paper develops a new BFO-based method to solve the SEP problem. The technique discussed in this paper uses BFOA to simultaneously optimize the sizes and locations of both the existing and new installed substation and feeders by considering reliability constraints. To clarify the capabilities of the presented method a typical network is considered and the results of applying GA and BFOA on the network are compared. The simulation results demonstrate that the BFOA has the potential to find more optimal results than the other algorithm under the same conditions. Also, the fast convergence, consideration of real-world networks limitations as problem constraints and simplicity in applying to large scale networks are the main features of the proposed method.

关键词： bacterial foraging optimization algorithm Genetic algorithm Substation Expansion Planning

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