Optimization of electrostatic sensor electrodes using particle swarm optimization technique

Heydarianasl, Mozhde; Rahmat, M. F.

doi:10.1007/s00170-016-9076-4

Cited by 11 publications

(13 citation statements)

References 24 publications

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“…Considering the observed results, electrical field interaction was less in 15 cm separation in comparison to 5 and 10 cm separations, while the cross-correlation coefficient in 15 cm separation was more than the case of 20 cm separation. Consequently, the optimal value of sensor separation between two electrodes is 15 cm because of low electrical field interaction and low statistical error, and the optimal value of length and thickness of electrode have been, respectively, selected as 0.6 and 0.5 cm referring to the literature (Heydarianasl and Rahmat, 2017) and present experimentations for achieving maximum spatial sensitivity and minimum statistical error. As mentioned earlier, electrical field interaction leads to reduced signal similarity (Ma and Yan, 2000), as shown in these figures.…”

Section: Resultsmentioning

confidence: 99%

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Optimization of electrostatic sensor based on sensor separation

Heydarianasl

2019

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Purpose Electrostatic sensors are applied to measure velocity of solid particles in many industries because controlling the velocity particles improves product quality and process efficiency. The purpose of current paper is optimization of these sensors which is required to achieve maximum spatial sensitivity and minimum statistical error. Design/methodology/approach Different electrode of electrostatic sensors with different length, thickness and sensor separations were experimentally applied in laboratory. Then, correlation velocity, signal bandwidth and statistical error were calculated. Findings High sensor separation is a crucial factor because it would lead to increase signal similarity and decrease statistical error. This paper focuses on the effect of sensor separation on optimization of electrostatic sensors. Originality/value From observations, the optimal value for length, thickness and sensor separations was 0.6, 0.5 and 15 cm, respectively. Consequently, statistical error has improved by about 17 per cent. These results provided a significant basis of optimization of electrostatic sensors.

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Section: Resultsmentioning

confidence: 99%

“…A detailed mathematical model to calculate the equation of spatial sensitivity of circular-and quarter-ring electrode was stated in literature (Heydarianasl and Rahmat, 2017).…”

Section: Methodsmentioning

confidence: 99%

Optimization of electrostatic sensor based on sensor separation

Heydarianasl

2019

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“…Several researchers have been applied different methods including FEM [14,15], GA [3], ANSYS [3], and PSO [4] to optimize electrostatic sensor electrodes. MOPSO has been examined as a new approach in this study due to its advantages including simplicity, high accuracy, and good performance.…”

Section: Optimization Methodsmentioning

confidence: 99%

“…The main reason to optimize electrostatic sensor is that reducing discrepancy between the measured correlation velocity and true mean particle velocity to obtain uniform spatial sensitivity. For these relatively, optimization methods have been studied in terms of fundamental characteristics of electrostatic sensor in several configurations [3,4].…”

Section: Introductionmentioning

confidence: 99%

Optimized Rectangular Electrode of Electrostatic Sensor

Rahmat

2018

IJIE

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This paper focuses on optimization of rectangular electrodes of electrostatic sensors. Fundamental characteristics of these electrodes are significant in order to use them in many industries that deal with particles and powders. In this study, Multi-Objective Particle Swarm Optimization (MOPSO) technique has been employed to obtain optimal value of electrode designs including length and thickness. Commercial MATLAB software is used in the optimization. Special sensitivity and statistical error have been utilized as conflict objective functions to recognize optimal sizes of electrodes. The optimization results are compared with the experimental data available in current study. From the observed results, the optimum sizes of length and thickness of rectangular electrode was equal to 0.6 cm and 0.5 cm, respectively.

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“…Particle Swarm Optimisation (PSO) is a swarm intelligence based algorithm to find a solution to an optimisation problem in a search space, or model, and predict social behaviour in the presence of objectives as well as sharing many similarities with evolutionary computation techniques [19]. Furthermore, each particle of the PSO flies in the search area with a velocity based on its own previous ideal solution.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

A Novel Method to Enhance the Core Design of Power Transformers Using Particle Swarm Optimization (PSO) Technique

Alyozbaky

Kadir

Izadi

2019

International Journal of Simulation: Systems, Science &Amp; Technology

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The reduction of losses in a power transformer is a significant topic in a power transmission grids, with the design of the core being one of the important factors related to the losses. In this study, an intelligent algorithm is developed using Particle Swarm Optimization (PSO) technique to find the optimum core design. Using a smart algorithm we consider a novel method to reduce the losses. To achieve this target, a nonlinear B-H curve with a 3D Finite-Element Method (FEM) is used to simulate the proposed model of core coupling with the PSO technique. Steady state analysis is considered in the simulation and the losses of the core and windings were considered a heat source inside the transformer. The results show core losses are reduced by 11 % and 5 % was achieved by the proposed model compared with the 90° butt-lap and 45° mitred designs respectively. The results were validated against the manufacturer's experimental measurements. Our proposed optimization techniques can be used to improve performance of electrical power transformers and can be applied to various transformers of different sizes and power rating.

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Optimization of electrostatic sensor electrodes using particle swarm optimization technique

Cited by 11 publications

References 24 publications

Optimization of electrostatic sensor based on sensor separation

Optimization of electrostatic sensor based on sensor separation

Optimized Rectangular Electrode of Electrostatic Sensor

A Novel Method to Enhance the Core Design of Power Transformers Using Particle Swarm Optimization (PSO) Technique

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