Reactive Power Optimization of Power Grid with Photovoltaic Generation Based on Improved Particle Swarm Optimization

Huibo, Liu; Huang, Guoying; Chün, Wang; Liu, Haixuan; Wang, Zizhao; Xu, Ziwei; Shi, Linjun

doi:10.1109/isgt-asia.2019.8880890

Cited by 13 publications

(11 citation statements)

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“…The position, and the rate at which the position of a particle in a given swarm changes at a given time, can best be described by velocity and position vectors [18] [28];…”

Section: Standard Pso Algorithmmentioning

confidence: 99%

“…Despite its fast convergence speed and its ease of implementation, it suffers from two major problems namely its easy drift into local optimum and its lacking in the convergence velocity at the last phase of convergence. In the attempt to proffer solution to these two major shortcomings, several variants and improvements have been put forward to the traditional PSO; which include the introduction of linearly decreasing inertia weightiness and constricting factor into the PSO velocity equation in [17][18][19][20][21][22][23][24][25][26][27][28], the use of a sinusoidally changing inertia weight technique in [29], the division of existing initial swarm into different sub-swarms in [30], the introduction of a new component of inertia called speed component to maintain particles' speed in [31], and the combination of PSO with other algorithms in [32][33][34][35][36][37][38][39][40][41]. A lot of these proposed improvements either suffer from too high computational time and computational complexity due to too many added parameters and even still fall easily into local optimum.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Reactive Power Dispatch Using Improved Chaotic PSO Algorithm with the Wingbeat Frequency

Adetona

John

Umar

2022

Acta Marisiensis. Seria Technologica

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The importance of reactive power to the economy and security of power systems cannot be overemphasized. For instance, Transmission losses increase when reactive power is unevenly distributed on transmission network; and power quality is affected as well. The cheapest way of reducing these transmission lines losses is via reactive power dispatch approach. This study therefore proposes an Improved Chaotic Particle Swarm Optimization algorithm (ICPSO) with the primary aim of reducing real power transmission line losses while adhering to system constraints. Although the traditional PSO has a fast convergence speed, it falls easily into local optimum and it is slow at the later stage of convergence. The ICPSO is proposed in this research to overcome these shortcomings. The approach combines PSO with chaotic map which increases particles’ diversity, allowing particles to explore the search region more; and a wingbeat frequency component which helps to sustain the rate of convergence of particles. MATPOWER 7.1 in MATLAB 2019a environment was utilized for the implementation. The purported algorithm was examined on IEEE14 and IEEE30 Test Beds respectively. When tried out on IEEE14 Test bed, real power loss was reduced from 13.393 MW to 12.260 MW; whereas real power transmission line loss was brought down from 17.557 MW to 15.977 MW when tried out on IEEE30 Test Bed. In terms of reducing real power transmission lines losses, the simulation results show that the proposed approach performs better when compared with other algorithms.

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“…The position, and the rate at which the position of a particle in a given swarm changes at a given time, can best be described by velocity and position vectors [18] [28];…”

Section: Standard Pso Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Reactive Power Dispatch Using Improved Chaotic PSO Algorithm with the Wingbeat Frequency

Adetona

John

Umar

2022

Acta Marisiensis. Seria Technologica

View full text Add to dashboard Cite

show abstract

“…The proposed method can satisfactorily minimize the losses but its fully automatic decision‐making structure precludes grid managers from allocating weights to the active and reactive power losses. Reference [16] employs particle swarm optimization (PSO) for reactive power planning and encompasses computer‐aided optimization for power loss minimization. However, PSO algorithm is vulnerable to being trapped in local optima while searching for the global optimum value.…”

Section: Introductionmentioning

confidence: 99%

A power loss minimization strategy based on optimal placement and sizing of distributed energy resources

Mirsaeidi

Devkota

et al. 2022

Int J Numerical Modelling

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Due to the enhanced price of electricity, the gradual depletion of fossil fuels, and the global warming concerns, power loss minimization through deployment of distributed generators (DGs) has attracted significant attention in recent decades. This paper proposes a genetic algorithm (GA) based strategy for minimization of active and reactive power losses through optimal location and size of DGs. It also quantifies and tallies the total network power losses for the cases with random as well as optimal allocation of DGs. To validate the accuracy of the obtained results from GA, another nature-inspired optimization algorithm, cuckoo search, is also deployed. The simulation results on IEEE 30 and 118 bus systems indicate that the proposed strategy not only can effectively reduce the total network active and reactive power losses but also lead to the improvement of network voltage profile.

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“…In [15,16], the author proposed a hybrid technique of grey wolf optimization and particle swarm optimization (GMO-PSO) to improve the performance of the algorithm by effectively controlling the local search and pushing the algorithm towards the direction of global optimization, and applied it to the solution of optimal reactive power scheduling (ORPD) problems within the grid. Liu et al [17] studied the reactive power optimization problem of photovoltaic power generation penetration distribution networks, and used the improved PSO to reduce the inertia weight factor at linear speed in the iterative process. The Taguchi method is a low-cost and high-efficiency quality engineering method, which uses an orthogonal array and the signal-to-noise ratio (SNR) to determine the optimal parameter setting of the system with the least number of tests, thereby improving the performance of the system [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Taguchi Particle Swarm Optimization Algorithm for Reactive Power Optimization of Deep-Water Semi-Submersible Platforms with New Energy Sources

Cheng

et al. 2022

Energies

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In order to realize the sustainable development of energy, the combination of new energy power generation technology and the traditional offshore platform has excellent research prospects. The access to new energy sources can provide a powerful supplement to the power grid of the offshore platform, but will also create new challenges for the planning, operation, and control of the power grid of the platform; hence, it is very important to optimize the reactive power of the offshore platform with new study, a mathematical model was first built for the reactive power optimization of offshore platform power systems with new energy sources, and the Taguchi method was then used to optimize the parameters and population of particle swarm optimization, thereby addressing a defect in particle swarm optimization, namely, that it can easily fall into local optimal solutions. Finally, the algorithm proposed in this paper was applied to solve the reactive power optimization problem of the offshore platform power system with new energy sources. The experimental results show that the proposed algorithm has stronger optimization ability, reduces the system active power loss to the greatest extent, and improves the voltage quality. These results provide theoretical support for the practical application and optimization of the deep-water semi-submersible production platform integrated with new energy sources.

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Reactive Power Optimization of Power Grid with Photovoltaic Generation Based on Improved Particle Swarm Optimization

Cited by 13 publications

References 0 publications

Optimal Reactive Power Dispatch Using Improved Chaotic PSO Algorithm with the Wingbeat Frequency

Optimal Reactive Power Dispatch Using Improved Chaotic PSO Algorithm with the Wingbeat Frequency

A power loss minimization strategy based on optimal placement and sizing of distributed energy resources

A Hybrid Taguchi Particle Swarm Optimization Algorithm for Reactive Power Optimization of Deep-Water Semi-Submersible Platforms with New Energy Sources

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