A hybrid topology scale-free Gaussian-dynamic particle swarm optimization algorithm applied to real power loss minimization

Wang, Chuan; Liu, Yancheng; Zhao, Yong; Yang, Chen

doi:10.1016/j.engappai.2014.02.018

Cited by 28 publications

(10 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The objective of the reactive power optimization is to minimize the active power loss in the transmission network (Wang et al 2014;Zhao et al 2005), which is defined as follows: …”

Section: Problem Formulationmentioning

confidence: 99%

“…In recent years, particle swarm optimization (PSO) has been used increasingly as a novel technique for solving global optimization problem , multi-objective optimization problem (Zhan et al 2013) and many real world problems in different areas such as electric power system (Wang et al 2014), financial market (Chang and Shi 2011) and multicast routing problem (Shen et al 2014). The PSO, which is first introduced by Kennedy and Eberhart (1995), is a stochastic optimization technique that can be likened to the behavior of a flock of birds or the sociological behavior of a group of people.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Self-adapting hybrid strategy particle swarm optimization algorithm

et al. 2015

Self Cite

View full text Add to dashboard Cite

Particle swarm optimization (PSO) algorithm has shown promising performances on various benchmark functions and engineering optimization problems. However, it is still difficult to achieve a satisfying trade-off between exploration and exploitation for all the optimization problems and different evolving stages. Furthermore, control parameters of some related mechanisms need pre-experience by the requirement of trial-and-error scheme. This paper presents a novel PSO algorithm, which adaptively adopts various search strategies, called Self-adapting Hybrid Strategy PSO (SaH-SPS). Unlike some other peer PSO variants, this method dynamically changes the probabilities of different strategies according to their previous successful searching memories, without any additional control parameters. The probabilities of different strategies would be re-initialized according to a proposed dynamic probabilistic model to diverse the population. Besides, particles are updated by probabilistically selected strategies after niching PSO with Ring topology. Moreover, a dynamic updating mechanism by niching PSO is proposed to guarantee the parallel searching capability during the whole evolution process. Thus, this proposed algorithm might be problem-independent and search-stageCommunicated by independent, yielding more satisfying solutions on various optimization problems. A comprehensive experimental study is conducted on 28 benchmark functions of CEC 2013 special session on real-parameter optimization, including shifted, rotated, multi-modal, high conditioned, expanded and composition problems, compared with several state-of-the-art variants of PSO and differential evolution (DE) algorithms. Comparison results show that SaHSPS obtains outstanding performances on the majority of the test problems. Moreover, a practical engineering problem, real power loss minimization of IEEE 30-bus power system, is used to further evaluate SaHSPS. The numerical results, compared with other stochastic search algorithms, show that SaHSPS could find high-quality solutions with higher probability.

show abstract

“…The objective of the reactive power optimization is to minimize the active power loss in the transmission network (Wang et al 2014;Zhao et al 2005), which is defined as follows: …”

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Self-adapting hybrid strategy particle swarm optimization algorithm

et al. 2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, artificial bee colony (ABC) algorithm has been executed for handling the RPD problem in deregulated power systems after assuming an already established real power market [23]. A hybrid topology population and scale-free topology population have been combined for good population diversity to deal with the application of real power loss minimization in power system [24]. In [25], opposition-based self-adaptive modified gravitational search algorithm has been applied for the RPD problem, where its parameters are self-tuned and randomly generated.…”

Section: Q Cmentioning

confidence: 99%

“…2 explicates the probability density functions (PDFs) of them. BSO's mutation process (24) utilizes only one direction solution for each target solution, where historical experiences are employed to determine the search direction of the population solutions by taking into account the values of the historical population (P old ). BSO's mutation factor (F) provides control of the amplitude of the search-direction matrix (P old − P ) [30].…”

Section: Mutationmentioning

confidence: 99%

Integrated Strategies of Backtracking Search Optimizer for Solving Reactive Power Dispatch Problem

Shaheen

El‐Sehiemy

Farrag

2018

IEEE Systems Journal

View full text Add to dashboard Cite

This paper proposes backtracking search optimizer (BSO) for solving the reactive power dispatch (RPD) problem. The RPD problem is highly nonlinear, nonconvex optimization problem and is consisting of both continuous and discrete control variables. It aims to find the optimal settings of the generator voltages, tap positions of tap changing transformers, and the amount of reactive compensation that able to optimize the transmission power losses. BSO has simple structure and single control parameter. It has two new tuned mutation and crossover operators that control the amplitude of the search-direction matrix and search space boundaries. In this paper, five diversified generation strategies of mutation factor of BSO to solve the RPD. The proposed BSO strategies are applied to the IEEE standard 14-, 30-, 57-bus test systems, and a real power system at West Delta region system as a part of the unified Egyptian network. Simulation results show that the BSO strategy, which almost generates mutation factor concentrated on the mean value of the normal distribution (BSO 4) assigns multidirections through the search space and supports the diversity of populations. To show the effectiveness and superiority of proposed BSO, the obtained results are compared with different algorithms reported in the literature.Index Terms-Backtracking search optimizer (BSO), evolutionary algorithms, power losses, reactive power dispatch (RPD), statistical indices, tuned mutation strategies.

show abstract

“…Chuan Wang et al [26] have planned a new hybrid topology scale-free Gaussian-dynamic particle swarm (HTSFGDPS) optimization algorithm for real power loss minimization problem of power system. The swarm population was broken down directly into a couple of elements: hybrid topology population and scale-free topology population.…”

Section: Recent Research Work: a Brief Reviewmentioning

confidence: 99%

Bat Algorithm a nd Firefly Algorithm f or Improving Dynamic Stability o f Power Systems Using UPFC

Kumar¹,

Srikanth²

2016

ijeei

View full text Add to dashboard Cite

In this paper a hybrid method for improving the dynamic stability of the power system using UPFC is proposed. The novelty of the proposed method is combined performance of the Bat-inspired algorithm and Firefly Algorithm (FA), which provides improved searching ability, automatic subdivision and random reduction. Here, the bat-inspired algorithm optimizes the maximum power loss bus while the generator fault occurs, which inturn affects the power flow constraints like voltage, power loss, real and reactive power. For improving the dynamic performance, the optimum capacity of UPFC has been determined with minimum cost by using the FA algorithm. The attained capacity UPFC has been located in the affected location and analyzes the power flow of the system. The proposed method is implemented in the MATLAB/simulink platform with IEEE 30 and IEEE 14 standard bench mark system. The proposed method performance is evaluated by comparison with different techniques like hybridized Gravitational Search Algorithm (GSA) and Bat algorithm. The comparison results proved the effectiveness of the proposed method and confirm its potential to solve the problem.

show abstract

A hybrid topology scale-free Gaussian-dynamic particle swarm optimization algorithm applied to real power loss minimization

Cited by 28 publications

References 33 publications

Self-adapting hybrid strategy particle swarm optimization algorithm

Self-adapting hybrid strategy particle swarm optimization algorithm

Integrated Strategies of Backtracking Search Optimizer for Solving Reactive Power Dispatch Problem

Bat Algorithm a nd Firefly Algorithm f or Improving Dynamic Stability o f Power Systems Using UPFC

Contact Info

Product

Resources

About