Optimal sizing of photovoltaic distributed generators in a distribution system with consideration of solar radiation and harmonic distortion

Hengsritawat, Vichakorn; Tayjasanant, Thavatchai; Nimpitiwan, N.

doi:10.1016/j.ijepes.2012.01.002

Cited by 68 publications

(23 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…PV modules are typically connected in series and parallel to obtain the expected voltage and current level [25,39,40]. For PV array, every PV module may receive different amount of sunlight as some modules may be partially shaded.…”

Section: Pv Arrays Under Partial Shaded Conditionsmentioning

confidence: 99%

Photovoltaic Multiple Peaks Power Tracking Using Particle Swarm Optimization with Artificial Neural Network Algorithm

Ngan¹,

Tan

2016

Advances in Solar Photovoltaic Power Plants

View full text Add to dashboard Cite

Photovoltaic (PV) array may receive different level of solar irradiance, such as partially shaded by clouds or nearby building. Multiple peak power points occur when PV module is under partially shaded conditions, which would significantly reduce the energy produced by PV without proper control. Therefore, a Maximum Power Point Tracking (MPPT) algorithm is used to extract maximum available PV power from the PV array. However, most of the conventional MPPT algorithms are incapable to detect global peak (GP) power point with the presence of several local peaks (LP). A hybrid Particle Swarm Optimization and Artificial Neural Network (PSO-ANN) algorithm is proposed in this article to detect the GP power. The PV system which consists of PV array, DC-DC boost converter, and a resistive load, were simulated using MATLAB/Simulink. The performance of the proposed algorithm is compared with that of the standard PSO algorithm. The proposed algorithm is tested and verified by hardware experiment. The simulation results and the experimental results are compared and discussed. It shows that the proposed algorithm performs well to detect the GP of the PV array under partial shaded conditions. In this work, the tracking efficiency of the proposed algorithm is in the range of 92.7-99.7 %.

show abstract

Section: Pv Arrays Under Partial Shaded Conditionsmentioning

confidence: 99%

Photovoltaic Multiple Peaks Power Tracking Using Particle Swarm Optimization with Artificial Neural Network Algorithm

Ngan¹,

Tan

2016

Advances in Solar Photovoltaic Power Plants

View full text Add to dashboard Cite

show abstract

“…However, the location given by the GA/PSO was different than that of the GA/GA result. In other papers, the performances of the optimization methods were compared with similar DG locations in the network [14][15][16][17][18]. With this in mind, the performance of the proposed algorithm in determining the optimal value, computing time, and number of iterations will be compared with existing optimization methods for the similar location of DGs (fair comparison).…”

Section: Related Researchmentioning

confidence: 99%

“…Therefore, the new parameter, E best , in the CLGPSO is defined as the value of G best that is randomly selected among the existing particles at any iteration until the current iteration (1:k) within its own dimension (d), as shown in Eq. (14). The acceleration coefficient equation for the E best parameter (c 3 ) is shown in Eq.…”

Section: Proposed Clgpsomentioning

confidence: 99%

Comparative learning global particle swarm optimization for optimal distributed generations' output

Jamian¹,

Mokhlis²,

Mustafa³

et al. 2014

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Abstract:The appropriate output of distributed generation (DG) in a distribution network is important for maximizing the benefit of the DG installation in the network. Therefore, most researchers have concentrated on the optimization technique to compute the optimal DG value. In this paper, the comparative learning in global particle swarm optimization (CLGPSO) method is introduced. The implementation of individual cognitive and social acceleration coefficient values for each particle and a new fourth term in the velocity formula make the process of convergence faster. This new algorithm is tested on 6 standard mathematical test functions and a 33-bus distribution system. The performance of the CLGPSO is compared with the inertia weight particle swarm optimization (PSO) and evolutionary PSO methods. Since the CLGPSO requires fewer iterations, less computing time, and a lower standard deviation value, it can be concluded that the CLGPSO is the superior algorithm in solving small-dimension mathematical and simple power system problems.

show abstract

“…A GAbased methodology considering system losses, reliability, and voltage profile for finding the optimal allocation and sizing of DGs was proposed in [16]. In [17][18][19][20][21][22], some optimal algorithms were used to determine the optimal allocation of DGs to reduce system loss. In [23], an analytical method based on the loss sensitivity factor was utilized to determine the optimal size and location of DGs so as to minimize system loss.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization for upgrading primary feeders with distributed generators from normally closed loop to mesh arrangement

Chen

Lin

Yang

et al. 2013

International Journal of Electrical Power & Energy Systems

View full text Add to dashboard Cite

Optimal sizing of photovoltaic distributed generators in a distribution system with consideration of solar radiation and harmonic distortion

Cited by 68 publications

References 12 publications

Photovoltaic Multiple Peaks Power Tracking Using Particle Swarm Optimization with Artificial Neural Network Algorithm

Photovoltaic Multiple Peaks Power Tracking Using Particle Swarm Optimization with Artificial Neural Network Algorithm

Comparative learning global particle swarm optimization for optimal distributed generations' output

Multi-objective optimization for upgrading primary feeders with distributed generators from normally closed loop to mesh arrangement

Contact Info

Product

Resources

About