Adaptive Weighted Improved Discrete Particle Swarm Optimization for Minimizing Load Balancing Index in Radial Distribution Network

Subramaniyan, Manikandan; Subramaniyan, Sasitharan; Veerasamy, Moorthy; Jawalkar, Viswanatha Rao

doi:10.22266/ijies2017.1231.14

Cited by 2 publications

(3 citation statements)

References 17 publications

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“…The classical PSO algorithm proposed in 1995 and sophisticated on by Kennedy and Eberhart is a stochastic optimization method that depends on the movement intelligence of particle [21][22][23]25]. It applies to the thought of the social conducts of swarms.…”

Section: Proposed Algorithm 31 Classical Psomentioning

confidence: 99%

A New Enhancement on PSO Algorithm for Combined Economic-Emission Load Dispatch Issues

Al-Rubayi¹,

Kdair²,

Flaih³

2020

IJIES

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Economic dispatch issues in power system aim to try getting an optimal plan for the power generators to minimize the fuel cost (FC) in parallel with satisfying system constraints. This paper proposes a new enhancement based on particle swarm optimization (PSO) algorithm called multiple inertia weight PSO (MIW-PSO) to solve the combined economic and emission load dispatch (CEELD) issues in the modern electrical power systems. Two electrical test systems are investigated in this study to validate the competence of the proposed algorithm. The obtained results for CEELD case using MIW-PSO compared with MOCPSO indicate a promising performance in terms of minimizing FC and pollutant emission (PE) are reduced 84.96 $/h and 12.01 kg/h for the first test system. As well as, for the second test system, compared with NSGA-RL are reduced 0.241 $/h and 3.15 kg/h. Moreover, the proposed algorithm has more accuracy, better convergence time, and higher quality solutions for the minimum CEELD compared with other methods.

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Section: Proposed Algorithm 31 Classical Psomentioning

confidence: 99%

A New Enhancement on PSO Algorithm for Combined Economic-Emission Load Dispatch Issues

Al-Rubayi¹,

Kdair²,

Flaih³

2020

IJIES

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“…Likewise, a mapping strategy is incorporated with the imperialist competitive algorithm and it becomes an improved adaptive imperialist competitive algorithm (IAICA) which adapts ICA into discrete nonlinear optimization problem [21], the step size of the E-coli of modified bacterial foraging optimization (MBFOA) has varied in each iteration [22], in case of theta-modified bat algorithm (T-BA) the Cartesian form is transformed into polar form results the velocity and position of each bat is updated using the phase angle vectors thus increases the convergence speed [23], as the genetic operator has augmented in an enhanced genetic algorithm (EGA), generating superior solutions for electrical distribution reconfiguration problem [24] and the inertia weight is adaptively varied in adaptive weighted improved discrete particle swarm optimization (AWIDPSO) for minimizing power loss [25] and load balancing index [26].…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, modified algorithms such as AACO [19], APSO [20], IAICA [21], MBFOA [22] T-BA [23] and AWIDPSO [25][26], hybrid algorithms MIHDE [27] and HPSO [28] have applied to ascertain optimal distribution reconfiguration under steady state condition.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Radial Distribution System for Minimizing Loss Reduction and Voltage Deviation Indices Using Modified Grey Wolf’s Algorithm

Nataraj¹,

Loganathan²,

Veerasamy³

et al. 2018

IJIES

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A novel metaheuristic optimization procedure for optimizing electrical distribution system is proposed that may help the system planners to know the minimum and maximum objective values. In which the optimal distribution network reconfiguration (DNR) framework and implementation of modified grey wolf optimization (mGWO) algorithm to minimize power loss reduction and node voltage deviation indices have delineated. The DNR involves nonlinear and multimodal function that has been optimized under practical constraints. For the purpose, mGWO algorithm is employed for ascertaining optimum switching position while reconfiguring the distribution system at a minimum fitness value. In fact grey wolf's update its position linearly from a higher value to zero in the search vicinity that provides perfect balance among intensification and diversification to ascertain the best fitness function and exhibits quick and steady convergence. Moreover, the proposed method seems to be a promising optimization tool for the electrical utilities, thereby modifying their operating strategy of distribution system under steady state conditions. This study is conducted on standard IEEE 33-bus and 69-bus distribution systems, the simulated results have analyzed and compared with several recent methods. It shows that the numerical results have provided, by mGWO is superior among the contestant algorithms.

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Adaptive Weighted Improved Discrete Particle Swarm Optimization for Minimizing Load Balancing Index in Radial Distribution Network

Cited by 2 publications

References 17 publications

A New Enhancement on PSO Algorithm for Combined Economic-Emission Load Dispatch Issues

A New Enhancement on PSO Algorithm for Combined Economic-Emission Load Dispatch Issues

Optimizing Radial Distribution System for Minimizing Loss Reduction and Voltage Deviation Indices Using Modified Grey Wolf’s Algorithm

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