Optimal Determination of Size and Site of DGs in Mesh System Using PSO

Male, Mohammad Salehi; Majd, Adel Akbari; Nezhad, Ramtin Rasouli

doi:10.12928/eei.v3i2.266

Cited by 3 publications

(2 citation statements)

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“…Where d is the dimension of the problem. The weighted factors (wi) are positive and related to the fitness function, which their sum is [29], [30]:…”

Section: Tuning Pi Parameters Using Apsomentioning

confidence: 99%

Optimal tuning of PI controllers using adaptive particle swarm optimization for doubly-fed induction generator connected to the grid during a voltage dip

et al. 2021

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This paper proposes the adaptive particle swarm optimization (APSO) technique to control the active and reactive power produced by a variable wind energy conversion system and the exchanged power between the electric grid and the system during a voltage dip (VD). Besides, to get the variable speed wind energy maximum power, a maximum power point (MPP) methodology is utilized. The system under study is a 5 MW wind turbine connected via a gearbox to a doubly-fed induction generator (DFIG). The DFIG stator is branched directly to the electrical network, while the Back-to-Back converters couple the rotor to the grid. The decoupled vector control of the rotor side converter and the grid side converter is established primarily by a conventional proportional-integral (PI) and a second level by an intelligent PI whose gains are tuned using the proposed control. The performances and results obtained by APSO tuned PI controllers are analyzed and compared with those attained by classical PI controllers through the MATLAB/Simulink. The superiority of the advised technique is examined during a two-phase short-circuit fault condition and confirmed by the reduced oscillations.

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“…Where d is the dimension of the problem. The weighted factors (wi) are positive and related to the fitness function, which their sum is [29], [30]:…”

Section: Tuning Pi Parameters Using Apsomentioning

confidence: 99%

Optimal tuning of PI controllers using adaptive particle swarm optimization for doubly-fed induction generator connected to the grid during a voltage dip

et al. 2021

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“…Those approaches cover reconfiguration of distribution network using Genetic Algorithm, Binary Genetic Algorithm, and Particle Swarm Optimization to reduce power losses [6][7][8]. The implementation of several AI methods previously mentioned was also developed in Distributed Generation (DG) [9,10]. The implementation of several AI methods has increased the power quality and Load Balancing Index (LBI) based on PSO and BPSO methods [11,12].…”

Section: Introductionmentioning

confidence: 99%

Distribution Network Reconfiguration Using Binary Particle Swarm Optimization to Minimize Losses and Decrease Voltage Stability Index

Firdaus¹,

Penangsang²,

Soeprijanto³

et al. 2018

Bulletin EEI

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Power losses and voltage drop are existing problems in radial distribution networks. This power losses and voltage drop affect the voltage stability level. Reconfiguring the network is a form of approach to improve the quality of electrical power. The network reconfiguration aims to minimize power losses and voltage drop as well as decreasing the Voltage Stability Index (VSI). In this research, network reconfiguration uses binary particle swarm optimization algorithm and Bus Injection to Branch Current-Branch Current to Bus Voltage (BIBC-BCBV) method to analyze the radial system power flow. This scheme was tested on the 33-bus IEEE radial distribution system 12.66 kV. The simulation results show that before reconfiguration, the active power loss is 202.7126 kW and the VSI is 0.20012. After reconfiguration, the active power loss and VSI decreased to 139.5697 kW and 0.14662, respectively. It has decreased the power loss for 31.3136% significantly while the VSI value is closer to zero.

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Biogeography Based Optimization Algorithm and Neural Network to Optimize Place and Size of Distributed Generating System in Electrical Distribution

Suraya¹,

Irshad

Ramesh

et al. 2022

J. Electr. Eng. Technol.

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Optimal Determination of Size and Site of DGs in Mesh System Using PSO

Cited by 3 publications

References 0 publications

Optimal tuning of PI controllers using adaptive particle swarm optimization for doubly-fed induction generator connected to the grid during a voltage dip

Optimal tuning of PI controllers using adaptive particle swarm optimization for doubly-fed induction generator connected to the grid during a voltage dip

Distribution Network Reconfiguration Using Binary Particle Swarm Optimization to Minimize Losses and Decrease Voltage Stability Index

Biogeography Based Optimization Algorithm and Neural Network to Optimize Place and Size of Distributed Generating System in Electrical Distribution

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