A Hybrid Least Square-Fuzzy Bacterial Foraging Strategy for Harmonic Estimation

Mishra, Sukumar

doi:10.1109/tevc.2004.840144

Cited by 392 publications

(190 citation statements)

References 12 publications

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“…The BIA inspired from the foraging behaviour of bacteria (E.coli) is called Bacterial Foraging Optimization Algorithm (BFOA), proposed by Passino ([23]; [24]). Since its inception BFOA has been widely accepted as global optimization algorithm for real world problems such as transmission loss reduction [25], learning of artificial neural networks [26], active power filters [27], optimal controller design ( [23]), and harmonic estimation [28]. Foraging is a process of moving the bacteria towards more nutrients region through neutral medium, and avoiding the poisonous substances.…”

Section: Bacterial-foraging Optimization Algorithmmentioning

confidence: 99%

Distribution Network Reconfiguration for Power Loss Minimization Using Bacterial Foraging Optimization Algorithm

Mam¹,

G²,

Saxena³

2016

International Journal of Engineering and Manufacturing

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This paper presents a distribution network reconfiguration based on bacterial foraging optimization algorithm (BFOA) along with backward-forward sweep (BFS) load flow method and geographical information system (GIS). Distribution network reconfiguration (DNR) is a complex, non-linear, combinatorial, and nondifferentiable constrained optimization process aimed at finding the radial structure that minimized network power loss while satisfying all operating constraints. BFOA is used to obtain the optimal switching configuration which results in a minimum loss, BFS is used to optimize the deviation in node voltages, and GIS is used for planning and easy analysis purposes. Simulation is performed on the 33-bus system and results are compared with the other approaches. The obtained results show that the proposed approach is better in terms of efficiency and having good convergence criteria.

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Section: Bacterial-foraging Optimization Algorithmmentioning

confidence: 99%

Distribution Network Reconfiguration for Power Loss Minimization Using Bacterial Foraging Optimization Algorithm

Mam¹,

G²,

Saxena³

2016

International Journal of Engineering and Manufacturing

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“…According to cost function, EBFS algorithm proposed by Passino [4] gives the weight parameter 'w' for the best approximation.…”

Section: Cost Functionmentioning

confidence: 99%

“…Most of the researcher study the BFS algorithm characteristics and also apply in different applications [2][3][4]. In this paper, we have been motivated to use the EBFS for finding the optimum value of weight 'w' to minimize the error of approximation.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Nonlinear Signal Approximation Using Bacterial Foraging Strategy

Naik¹,

Panda

2010

Swarm, Evolutionary, and Memetic Computing

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Abstract. Uniform approximation of signals has been an area of interest for researchers working in different disciplines of science and engineering. This paper presents an adaptive algorithm based on E. coli bacteria foraging strategy (EBFS) for uniform approximation of signals by linear combinations of shifted nonlinear basis functions. New class of nonlinear basis functions has been derived from a sigmoid function. The weight factor of the newly proposed nonlinear basis functions has been optimized by using the EBFS to minimize the mean square error. Different test signals are considered for validation of the present technique. Results are also compared with Genetic algorithm approach. The proposed technique could also be useful in fractional signal processing applications.

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“…Some evolutional computation based algorithms such as bacterial foraging strategy [7], GA based algorithm [8], GSO based algorithm [4] and PSO based algorithm [3] have also been reported to accomplish the estimation of harmonics in power systems. Despite their feature of stochastic optimization, which makes them unlikely to be trapped in local minima, their mathematical theories are complex and the parameters needed are usually numerous, in comparison with the DFT based methods.…”

Section: Introductionmentioning

confidence: 99%

Power System Harmonic Estimation Based on Park Transform

Ya¹,

Ji²,

Li³

et al. 2016

Journal of Electrical Engineering and Technology

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-This paper presents a novel method for power system harmonic estimation based on the Park transform. The proposed method firstly extends the signal to a group of three-phase signals in a-b-c coordinate. Then, a linear fitting based method is adopted to estimate the fundamental frequency. Afterwards, the Park transform is utilized to convert the three-phase signals from a-b-c coordinate to d-q-0 coordinate. Finally, the amplitude and phase of a harmonic component of interest can be calculated using the d-axis and q-axis components obtained. Simulation studies have been conducted using matrix laboratory (MATLAB) and power system computer aided design/electromagnetic transients including direct current (PSCAD/EMTDC). Simulation studies in MATLAB have considered three scenarios, i.e., no-frequency-deviation scenario, frequency-deviation scenario and the scenario in the presence of inter-harminics. The results have demonstrated that the proposed method achieves very high accuracy in frequency, phase and amplitude estimation under noisy conditions, and suffers little influence of the inter-harmonics. Moreover, comparison studies have proved that the proposed method is superior to FFT and Interpolated FFT with the Hanning Window (IpFFTHW). Finally, a popular case in PSCAD/EMTDC has been employed to further verify the effectiveness of the proposed method.

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A Hybrid Least Square-Fuzzy Bacterial Foraging Strategy for Harmonic Estimation

Cited by 392 publications

References 12 publications

Distribution Network Reconfiguration for Power Loss Minimization Using Bacterial Foraging Optimization Algorithm

Distribution Network Reconfiguration for Power Loss Minimization Using Bacterial Foraging Optimization Algorithm

Adaptive Nonlinear Signal Approximation Using Bacterial Foraging Strategy

Power System Harmonic Estimation Based on Park Transform

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