Phasor particle swarm optimization: a simple and efficient variant of PSO

Ghasemi, Maryam; Akbari, Ebrahim; Rahimnejad, Abolfazl; Razavi, S. Ehsan; Ghavidel, Sahand; Li, Li

doi:10.1007/s00500-018-3536-8

Cited by 150 publications

(63 citation statements)

References 75 publications

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“…PPSO is a new simple adaptive PSO based on modeling the particle acceleration coefficients ( C 1 and C 2 ) considering phase angle ( θ ) to obtain a nonparametric PSO. The authors proposed an applicable and proficient function for PSO acceleration coefficients based on periodic trigonometric functions of the phase angle ( θ ). The phase angle is identified for each particle so that the i th particle is presented through a magnitude xi and phase angle θ i .…”

Section: Solution Methodsmentioning

confidence: 99%

“…In the proposed PPSO, the value of inertia weight is set to w = 0. The velocity in each iteration is updated using the following Equation :

\begin{matrix} v_{i} ((), t + 1) & = {|\cos θ_{i} (t)|}^{2 \cdot normalsin θ_{i} ((), t)} \cdot ((), {pbest}_{i} ((), t) - x_{i} ((), t)) \\ + {|\sin θ_{i} (t)|}^{2 \cdot normalcos θ_{i} ((), t)} \cdot ((), boldgbest ((), t) - x_{i} ((), t)) \end{matrix}

…”

Section: Solution Methodsmentioning

confidence: 99%

“…This has motivated some of the researchers to propose a modification of basic PSO by the introduction of adaptive acceleration coefficients and inertia weight. The most recent version of such improvement is the PPSO algorithm proposed by Ghasemi et al [31]. PPSO is a new simple adaptive PSO based on modeling the particle acceleration coefficients (C 1 and C 2 ) considering phase angle (θ) to obtain a nonparametric PSO.…”

Section: Phasor Particle Swarm Optimization the Phasementioning

confidence: 99%

“…In the proposed PPSO, the value of inertia weight is set to w = 0. The velocity in each iteration is updated using the following Equation [31]:…”

Section: Phasor Particle Swarm Optimization the Phasementioning

confidence: 99%

See 3 more Smart Citations

A Novel Method Based on PPSO for Optimal Placement and Sizing of Distributed Generation

Ullah

Wang

Radosavljević³

2019

IEEJ Transactions Elec Engng

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Energy loss minimization, voltage profile improvement, and increasing reliability of the power system are the prominent advantages of distributed generation (DG) unit's integration in distribution systems. Therefore, optimal placement and sizing of DG become a critical issue. This article proposes a recently developed adaptive particle swarm optimization (PSO) algorithm known as phasor particle swarm optimization (PPSO) to solve the problem of optimal placement and sizing of DG units in radial distribution networks. The PPSO algorithm is based on modeling the particle control parameters with a phase angle (θ) transforming standard PSO into a self‐adaptive and parametric independent metaheuristic optimization algorithm. The main objective is to minimize the energy loss in the network considering various technical constraints and load variations. In particular, the proposed approach is implemented on a practical radial distribution feeder located at the Haripur area in Pakistan taking into account the hourly actual load values. Comparison of the devised PPSO algorithm with other techniques in the literature was done via an IEEE 33 bus test system. The results obtained by the proposed PPSO algorithm show that it enables a significant reduction in power loss for all the analyzed cases, and outperforms many of other solution techniques applied for the same problem. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Section: Solution Methodsmentioning

confidence: 99%

“…In the proposed PPSO, the value of inertia weight is set to w = 0. The velocity in each iteration is updated using the following Equation :

\begin{matrix} v_{i} ((), t + 1) & = {|\cos θ_{i} (t)|}^{2 \cdot normalsin θ_{i} ((), t)} \cdot ((), {pbest}_{i} ((), t) - x_{i} ((), t)) \\ + {|\sin θ_{i} (t)|}^{2 \cdot normalcos θ_{i} ((), t)} \cdot ((), boldgbest ((), t) - x_{i} ((), t)) \end{matrix}

…”

Section: Solution Methodsmentioning

confidence: 99%

Section: Phasor Particle Swarm Optimization the Phasementioning

confidence: 99%

“…In the proposed PPSO, the value of inertia weight is set to w = 0. The velocity in each iteration is updated using the following Equation [31]:…”

Section: Phasor Particle Swarm Optimization the Phasementioning

confidence: 99%

See 2 more Smart Citations

A Novel Method Based on PPSO for Optimal Placement and Sizing of Distributed Generation

Ullah

Wang

Radosavljević³

2019

IEEJ Transactions Elec Engng

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“…For instance, the PSO hybridized with DE, fuzzy logic, Pareto optimal set and GSA are the recently developed competitive hybrid strategies with ability to evade local trapping and premature convergence [17][18][19][20]. While, the new variant of PSO and other hybrid solution mechanisms by relating these concepts are studied in [21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Design of Fractional Particle Swarm Optimization Gravitational Search Algorithm for Optimal Reactive Power Dispatch Problems

et al. 2020

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In fact, optimal RPD is one of the most critical optimization matters related to electrical power stability and operation. The minimization of overall real power losses is obtained by adjusting the power systems control variables, for instance; generator voltage, compensated reactive power and tap changing of the transformer. In this search, a new heuristic computing method named as fractional particle swarm optimization gravitational search algorithm (FPSOGSA) is presented by introducing fractional derivative of velocity term in standard optimization mechanism. The designed FPSOGSA is implemented for the optimal RPD problems with IEEE-30 and IEEE-57 standards by attaining the near finest outcome sets of control variables along with minimization of two fitness objectives; active power transmission line losses (Ploss, MW) and voltage deviation (VD). The superior performance of the proposed FPSOGSA is verified for both single and multiple runs through comparative study with state of art counterparts for each scenario of optimal RPD problems.

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Multicounty fuel constraint‐based dynamic economic dispatch problem using horse herd optimization

Jena,

Basu,

Khan

2023

Energy Science & Engineering

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Due to the diminution of fossil fuels, the profitable usage of fuel for the generation of power is reducing. It is an extremely vital concern for power generation companies. The fossil fuel‐based power plant must operate within its fuel limits and contractual restrictions. Therefore, economic optimization is required to optimize the fuel cost for generation under fuel constraints. To address this issue, in this work, a horse herd optimization algorithm (HOA) is developed to solve multicounty fuel‐constrained dynamic economic dispatch with demand‐side management incorporating wind turbine generators, solar photovoltaic plants, and pumped hydro storage plants. The efficiency of the suggested HOA algorithm has been revealed on a selected test system. The HOA technique shows better convergence as compared with the other methods. Moreover, the computation time requirement of the HOA technique is also lower than that of other compared methods. From numerical results, it is observed that the consumption of fuel is sufficiently high if constraints are incorporated into the problem. Simulation results are matched up to those obtained from hierarchical particle swarm optimization with time‐varying acceleration coefficients, fast convergence evolutionary programming, and differential evolution. After comparison, it is observed that the recommended HOA offers a better‐quality solution.

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Phasor particle swarm optimization: a simple and efficient variant of PSO

Cited by 150 publications

References 75 publications

A Novel Method Based on PPSO for Optimal Placement and Sizing of Distributed Generation

A Novel Method Based on PPSO for Optimal Placement and Sizing of Distributed Generation

Design of Fractional Particle Swarm Optimization Gravitational Search Algorithm for Optimal Reactive Power Dispatch Problems

Multicounty fuel constraint‐based dynamic economic dispatch problem using horse herd optimization

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