Optimal probabilistic planning of passive harmonic filters in distribution networks with high penetration of photovoltaic generation

Jannesar, Mohammad Rasol; Sedighi, Alireza; Savaghebi, Mehdi; Anvari‐Moghaddam, Amjad

doi:10.1016/j.ijepes.2019.03.025

Cited by 45 publications

(30 citation statements)

References 48 publications

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“…where L represents the longest radius in the solution space and div(t) within the range of [0,1] measures the distance from each bacterium to the center of the population, which is irrelevant to the size of the solution space or the number of bacteria. e iteration of the BFO algorithm is expressed by a parameter T, which is defined as an expression in the range of (0,1] in (8), where t and T max represent the index of the current chemotaxis and the maximum iteration, respectively.…”

Section: Self-adaptive Swimming Based On Bacterial Search Statementioning

confidence: 99%

“…where J max and J min show the maximum and minimum of the fitness, respectively. erefore, in this paper, the three variables, the population diversity in (7), iterations in (8), and the mean fitness of bacteria in (9), are set as inputs of the multidimension fuzzy logic controller (MFLC), which is designed to investigate the search status of the algorithm in this paper. en, with the two outputs of the MFLC, the chemotaxis swimming processes are adjusted in the following:…”

Section: Self-adaptive Swimming Based On Bacterial Search Statementioning

confidence: 99%

“…erefore, the BFO algorithm has been successfully applied in many fields. Literature in [6][7][8] adopted the BFO algorithm to optimize the probabilistic planning, load dispatch, reconstruction, and loss minimization of the power energy network. Artificial intelligence learning and robot automatic control can also refer to the BFO algorithm [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bacterial Foraging Optimization Based on Self-Adaptive Chemotaxis Strategy

Chen

Wang

et al. 2020

Computational Intelligence and Neuroscience

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Bacterial foraging optimization (BFO) algorithm is a novel swarm intelligence optimization algorithm that has been adopted in a wide range of applications. However, at present, the classical BFO algorithm still has two major drawbacks: one is the fixed step size that makes it difficult to balance exploration and exploitation abilities; the other is the weak connection among the bacteria that takes the risk of getting to the local optimum instead of the global optimum. To overcome these two drawbacks of the classical BFO, the BFO based on self-adaptive chemotaxis strategy (SCBFO) is proposed in this paper. In the SCBFO algorithm, the self-adaptive chemotaxis strategy is designed considering two aspects: the self-adaptive swimming based on bacterial search state features and the improvement of chemotaxis flipping based on information exchange strategy. The optimization results of the SCBFO algorithm are analyzed with the CEC 2015 benchmark test set and compared with the results of the classical and other improved BFO algorithms. Through the test and comparison, the SCBFO algorithm proves to be effective in reducing the risk of local convergence, balancing the exploration and the exploitation, and enhancing the stability of the algorithm. Hence, the major contribution in this research is the SCBFO algorithm that provides a novel and practical strategy to deal with more complex optimization tasks.

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Section: Self-adaptive Swimming Based On Bacterial Search Statementioning

confidence: 99%

Section: Self-adaptive Swimming Based On Bacterial Search Statementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bacterial Foraging Optimization Based on Self-Adaptive Chemotaxis Strategy

Chen

Wang

et al. 2020

Computational Intelligence and Neuroscience

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“…In (9), the scale of ( ) 2) To guarantee the sufficient V DCxf , the peak value of fundamental inverter voltage needs to be considered…”

Section: Design Of Vdcf Cpf and Lpf Based On Power Flow Analysis Undmentioning

confidence: 99%

H-Infinity Control of an Adaptive Hybrid Active Power Filter for Power Quality Compensation

Mouodo¹,

Tamba²,

Mayi³

et al. 2020

EPE

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“…To illustrate previous research works which have been conducted regarding the methodology and different types of the filters used in the harmonic compensation framework, Table 1 comprehensively shows the harmonic compensation taxonomy including various studies. [ 7–52 ] Based on the magnitude of the harmonic pollution by NLs, it was decided to utilize one or multiple PPFs and APLCs to participate in harmonic compensation. This framework can consist of an objective function (OF) or multiOFs, uncertainty consideration, single‐objective or multiobjective solution method, etc.…”

Section: Introductionmentioning

confidence: 99%

A New Optimization Framework for Harmonic Compensation Considering Plug‐in Electric Vehicle Penetration Using Adaptive Particularly Tunable Fuzzy Chaotic Particle Swarm Optimization

2021

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Plug‐in electric vehicles (PEVs) can contribute to eliminating undesirable harmonics generated by nonlinear loads. In this study, a novel stochastic optimization approach for harmonic compensation is proposed which is capable of optimizing contrary objectives, including total harmonic distortion and harmonic inject current, simultaneously, while meeting the relevant constraints. This problem can be influenced by the uncertainty of PEVs which is reflected in the force outage rate concept. The Monte–Carlo simulation technique is implemented to consider the uncertainty associated with PEVs by generating plausible scenarios with the aim of converting the mentioned framework to the respective deterministic equivalents. Afterward, adaptive particularly tunable fuzzy chaotic particle swarm optimization (APTFCPSO) is utilized, based on the weighted sum method, and the acquired results are compared with those obtained by other implemented swarm intelligence‐based algorithms. Accordingly, at first, several benchmark optimization functions are considered to verify the performance of the APTFCPSO. Afterward, active power line conditioners (APLCs) and PEVs are separately employed for harmonics cancellation in the deterministic form. After adopting the scenario reduction technique, the optimization framework is solved for each remaining scenario by the mentioned procedure. The statistical analysis reveals that PEVs outperform APLCs to cancel harmonic orders defined in a 14‐node micro‐grid.

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Optimal probabilistic planning of passive harmonic filters in distribution networks with high penetration of photovoltaic generation

Cited by 45 publications

References 48 publications

Bacterial Foraging Optimization Based on Self-Adaptive Chemotaxis Strategy

Bacterial Foraging Optimization Based on Self-Adaptive Chemotaxis Strategy

H-Infinity Control of an Adaptive Hybrid Active Power Filter for Power Quality Compensation

A New Optimization Framework for Harmonic Compensation Considering Plug‐in Electric Vehicle Penetration Using Adaptive Particularly Tunable Fuzzy Chaotic Particle Swarm Optimization

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