Parametric Performance Evaluation of Different Types of Particle Swarm Optimization Techniques Applied in Distributed Generation System

Kumar, Sajjan; Sau, Suman; Pal, Dipayan; Tudu, Bipan; Mandal, Kamal Krishna; Chakraborty, Nilanjan

doi:10.1007/978-3-642-35314-7_40

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…It is an evolutionary algorithm that is based on swarm intelligence. Since its development, PSO has been used for many engineering applications and it has also undergone tremendous mutations due to the tuning of the PSO parameters and has led to variants of the algorithm such as Binary PSO, Stochastic inertia weight (Sto-IW) PSO, Hierarchical PSO (HPSO), Self-organizing hierarchical PSO with time-varying acceleration coefficients (HPSO-TVAC), amongst other [33]. These new variants of PSO have been developed to solve the technique's problem of being trapped in the local optimum solution instead of obtaining the global best solution [34].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Optimal Placement of Electric Vehicle Charging Stations in a Distribution Network With Randomly Distributed Rooftop Photovoltaic Systems

Fokui¹,

Saulo

Ngoo

2021

IEEE Access

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The increasing number of electric vehicles (EVs) in today's transport sector is gradually leading to the phasing out of petroleum-based vehicles. However, the rapid deployment of EVs largely depends on the coordinated and fast expansion of EV charging stations (EVCSs). The integration of EVCSs in the modern distribution network characterized by increased penetration of randomly distributed photovoltaic (PV) systems is challenging as they can lead to excessive power losses and voltage deviations beyond acceptable limits. In this paper, a hybrid bacterial foraging optimization algorithm and particle swarm optimization (BFOA-PSO) technique is proposed for the optimal placement of EVCSs into the distribution network with high penetration of randomly distributed rooftop PV systems. The optimization problem is formulated as a multi-objective problem minimizing active and reactive power losses, average voltage deviation index, and maximizing voltage stability index. The IEEE 69 node distribution network is used as the case network. The simulation is done using MATLAB to integrate the EVCSs in five cases of randomly sized and placed PV systems in the distribution network. For all five cases, a minimal increase in power losses is recorded with minor changes in the voltage deviation and stability indices due to the placement of the EVCSs. But for the voltages of nodes 29 to 48, the other node voltages remain unchanged upon placement of the EVCSs. The largest increase in power losses due to the EVCSs being brought into the network with PVs was noticed in case 3 (from 142.27kW, and 62.90kVar to 147.65kW, and 72.48kVar).

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Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Optimal Placement of Electric Vehicle Charging Stations in a Distribution Network With Randomly Distributed Rooftop Photovoltaic Systems

Fokui¹,

Saulo

Ngoo

2021

IEEE Access

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“…22 They are electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. 23 Kumar et al (2013) examines the performance of various PSO algorithms: Canonical PSO, Hierarchical PSO (HPSO), Time varying acceleration coefficient (TVAC) PSO, Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC), Stochastic inertia weight (Sto-IW) PSO, and Time varying inertia weight (TVIW) PSO have been used for comparative study. These versions of PSO vary only in parameterization.…”

Section: (Ii) a Pso Demonstrationmentioning

confidence: 99%

An Artificial Intelligence Approach to the Valuation of American-style Derivatives: A Use of Particle Swarm Optimization

Chen

2020

SSRN Journal

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“…

is derived through:

where:

where

and

. The particle inertia,

, varies from 0.9 in the first iteration to 0.4 in the L -th iteration, e.g., [ 27 ]. This induces high movement of the particles at the beginning of the simulation to explore large source regions and focus the search around the optimum location, then moving the particles slowly at the end of the simulation.…”

Section: Localization Techniquesmentioning

confidence: 99%

Survey on the Performance of Source Localization Algorithms

Fresno

Robles

Martínez-Tarifa

et al. 2017

Sensors

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The localization of emitters using an array of sensors or antennas is a prevalent issue approached in several applications. There exist different techniques for source localization, which can be classified into multilateration, received signal strength (RSS) and proximity methods. The performance of multilateration techniques relies on measured time variables: the time of flight (ToF) of the emission from the emitter to the sensor, the time differences of arrival (TDoA) of the emission between sensors and the pseudo-time of flight (pToF) of the emission to the sensors. The multilateration algorithms presented and compared in this paper can be classified as iterative and non-iterative methods. Both standard least squares (SLS) and hyperbolic least squares (HLS) are iterative and based on the Newton–Raphson technique to solve the non-linear equation system. The metaheuristic technique particle swarm optimization (PSO) used for source localisation is also studied. This optimization technique estimates the source position as the optimum of an objective function based on HLS and is also iterative in nature. Three non-iterative algorithms, namely the hyperbolic positioning algorithms (HPA), the maximum likelihood estimator (MLE) and Bancroft algorithm, are also presented. A non-iterative combined algorithm, MLE-HLS, based on MLE and HLS, is further proposed in this paper. The performance of all algorithms is analysed and compared in terms of accuracy in the localization of the position of the emitter and in terms of computational time. The analysis is also undertaken with three different sensor layouts since the positions of the sensors affect the localization; several source positions are also evaluated to make the comparison more robust. The analysis is carried out using theoretical time differences, as well as including errors due to the effect of digital sampling of the time variables. It is shown that the most balanced algorithm, yielding better results than the other algorithms in terms of accuracy and short computational time, is the combined MLE-HLS algorithm.

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Parametric Performance Evaluation of Different Types of Particle Swarm Optimization Techniques Applied in Distributed Generation System

Cited by 5 publications

References 8 publications

Optimal Placement of Electric Vehicle Charging Stations in a Distribution Network With Randomly Distributed Rooftop Photovoltaic Systems

Optimal Placement of Electric Vehicle Charging Stations in a Distribution Network With Randomly Distributed Rooftop Photovoltaic Systems

An Artificial Intelligence Approach to the Valuation of American-style Derivatives: A Use of Particle Swarm Optimization

Survey on the Performance of Source Localization Algorithms

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