Multiple DG Placements in Distribution System for Power Loss Reduction Using PSO Algorithm

Prakash, D.B.; Lakshminarayana, C.

doi:10.1016/j.protcy.2016.08.173

Cited by 119 publications

(54 citation statements)

References 17 publications

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“…He supposed that in a D-dimensional search space, there is a particle population with a population size of N, the position of each particle i (i = 1,2, ..., N) in space can be expressed as xi = (xi1, xi2, ..., xiD). The velocity of particle i is expressed by vi = (vi1, vi2, ..., viD) [8,9]. When the particle i is updated to t generation, d-th (d = 1,2, ..., D) dimensional velocity in the subspace is shown in the following equation:…”

Section: Standard Pso Algorithmmentioning

confidence: 99%

“…Then, the population is initialized, with T tasks, R resources, S particles, and T>R. The i-th particle position is represented by the vector xi, defined as xi = {xi1, xi2, ..., xid} [9]. 1 ≤ i ≤ S, 1 ≤ d ≤T, 1 ≤ xij ≤ R. The i-th particle speed is represented by the vector vi, defined as vi = (vi1, vi2, ..., vid).…”

Section: Particle Coding and Initializationmentioning

confidence: 99%

“…The position and the speed of each particle are assigned random integers between [1, R] and [-R, R], respectively [9].…”

Section: Particle Coding and Initializationmentioning

confidence: 99%

“…Finally, updates the inertia weight. S(i,t) is the success value of the particle i in the t-th iteration [9]; pbest is the optimal position of the particle i at the t-th iteration [9]; fitness(pbest) is the fitness of the optimal position of the particle i at the t-th iteration. When the fitness of the current particle is less than the last optimal position, the success value of the particle in this iteration is 1.…”

Section: Traditional Adaptive Inertia Weightmentioning

confidence: 99%

“…By using Cloudsim to build the cloud computing environment and eclipse as the development platform, there are five algorithms: the improved adaptive inertial weight particle swarm optimization algorithm, the standard PSO algorithm, the traditional adaptive inertial weight particle swarm algorithm, the NAIWPS algorithm proposed in [9], and Sequential scheduling algorithm. The convergence and optimal fitness of each algorithm are compared and analyzed respectively.…”

Section: Simulation Of Experimental Environment and Algorithm Parametersmentioning

confidence: 99%

See 4 more Smart Citations

Research on Cloud Computing Task Scheduling based on Improved Particle Swarm Optimization

Zhao¹

2017

IJPE

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Particle swarm optimization (PSO) is a popular intelligent algorithm to solve the task scheduling optimization problem of work-flow system in cloud computing environment. However, this algorithm is easy to fall into the local optimality. It is the reason that the execution time and cost of the scheduling scheme are higher than other methods. Therefore, by improving the calculation method of the single particle success value, the traditional adaptive inertia weight particle group task scheduling algorithm is optimized. Through each particle fitness and local optimal value and global optimal value that divided into four cases to compare, the inertia weight improved can be used to adjust the particle velocity more accurately. It can better equilibrate search capacity of particles between global and local, and avoid the local maximum of the particles. In this paper, we more accurately describe the particle state and improve the inertia weight. We can get a scheduling scheme with lower execution time and lower cost. The analog results show that the improved algorithm is stable. The convergence accuracy is obviously improved. It can effectively avoid prematurely falling into the local optimality.

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Section: Standard Pso Algorithmmentioning

confidence: 99%

Section: Particle Coding and Initializationmentioning

confidence: 99%

“…The position and the speed of each particle are assigned random integers between [1, R] and [-R, R], respectively [9].…”

Section: Particle Coding and Initializationmentioning

confidence: 99%

Section: Traditional Adaptive Inertia Weightmentioning

confidence: 99%

Section: Simulation Of Experimental Environment and Algorithm Parametersmentioning

confidence: 99%

See 3 more Smart Citations

Research on Cloud Computing Task Scheduling based on Improved Particle Swarm Optimization

Zhao¹

2017

IJPE

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show abstract

Islanding‐based reliability enhancement and power loss minimization by network reconfiguration using PSO with multiple DGs

Ramachandradurai

Kumarappan

2021

Int Trans Electr Energ Syst

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The distributed generation (DG) units are used more in the electrical networks to reduce the energy demand along with reducing the system losses and increasing the voltage profile of the load points. This paper presents an unintentional islanding detection, reliability enhancement, and power loss minimization of the restored system after an islanding event. The detection of islanding is performed by passive detection method of simultaneous measurement of voltage and frequency (vf)-based technique. The reliability of the system is increased along with the power loss minimization through reconfiguration by Particle Swarm Optimization (PSO) technique. The network reconfiguration is performed by identifying the most vulnerable buses, and reliability indices of the system are evaluated after the islanding event. The reliability indices act as a marker for validating the proposed reconfiguration strategy. The proposed method for islanding detection, reliability enhancement, and power loss minimization is compared with the existing methods available in the literature. The proposed method is tested on two different radial distribution system such as IEEE-69 and 118 bus systems, and the results obtained after the validation are promising.

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Optimal Planning of Renewable DG and Reconfiguration of Distribution Network Considering Multiple Objectives Using PSO Technique for Different Scenarios

Kumar¹,

Bohre²

2022

Hybrid Intelligent Approaches for Smart Energy

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Multiple DG Placements in Distribution System for Power Loss Reduction Using PSO Algorithm

Cited by 119 publications

References 17 publications

Research on Cloud Computing Task Scheduling based on Improved Particle Swarm Optimization

Research on Cloud Computing Task Scheduling based on Improved Particle Swarm Optimization

Islanding‐based reliability enhancement and power loss minimization by network reconfiguration using PSO with multiple DGs

Optimal Planning of Renewable DG and Reconfiguration of Distribution Network Considering Multiple Objectives Using PSO Technique for Different Scenarios

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