scholarly journals Enhanced Particle Swarm Optimization-Based Feeder Reconfiguration Considering Uncertain Large Photovoltaic Powers and Demands

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Ying-Yi Hong ◽  
Faa-Jeng Lin ◽  
Fu-Yuan Hsu
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Distribution System ◽  
Urban Areas ◽  
Distribution Systems ◽  
Particle Swarm ◽  
Solar Pv ◽  
Swarm Optimization ◽  
Rural And Urban Areas ◽  
Enhanced Particle Swarm Optimization

The Kyoto protocol recommended that industrialized countries limit their green gas emissions in 2012 to 5.2% below 1990 levels. Photovoltaic (PV) arrays provide clear and sustainable renewable energy to electric power systems. Solar PV arrays can be installed in distribution systems of rural and urban areas, as opposed to wind-turbine generators, which cause noise in surrounding environments. However, a large PV array (several MW) may incur several operation problems, for example, low power quality and reverse power. This work presents a novel method to reconfigure the distribution feeders in order to prevent the injection of reverse power into a substation connected to the transmission level. Moreover, a two-stage algorithm is developed, in which the uncertain bus loads and PV powers are clustered by fuzzy-c-means to gain representative scenarios; optimal reconfiguration is then achieved by a novel mean-variance-based particle swarm optimization. The system loss is minimized while the operational constraints, including reverse power and voltage variation, are satisfied due to the optimal feeder reconfiguration. Simulation results obtained from a 70-bus distribution system with 4 large PV arrays validate the proposed method.

scholarly journals Optimasi Penempatan dan Kapasitas Bank Kapasitor Untuk Mereduksi Rugi-Rugi Daya Menggunakan Kombinasi Metode Loss Sensitivity Factors dan Particle Swarm Optimization (PSO)

Electrician ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 48
Author(s):  
Fikri Adi Anggara ◽  
Osea Zebua ◽  
Khairuddin Hasan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Distribution System ◽  
Distribution Systems ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Power Losses ◽  
Swarm Optimization ◽  
Sensitivity Factors ◽  
Active Power Losses

Intisari — Sistem distribusi yang memiliki saluran panjang biasanya memiliki masalah rugi-rugi daya dan votage drop. Masalah ini dapat diatasi dengan cara menempatkan bank kapasitor pada sistem distribusi. Masalah utama dalam penempatan bank kapasitor adalah menentukan lokasi dan kapasitas yang optimal. Penelitian ini menggunakan metode Loss Sensitivity Factors (LSF) untuk menentukan kandidat bus yang akan dipilih sebagai lokasi penempatan bank kapasitor dan metode algoritma Particle Swarm Optimization digunakan untuk mencari kapasitas optimal bank kapasitor yang dipasang. Simulasi dilakukan dengan menggunakan perangkat lunak MATLAB. Studi kasus yang digunakan adalah kasus uji IEEE 10 bus dan penyulang Pakis di gardu induk Menggala. Hasil simulasi menunjukkan bahwa kombinasi kedua metode dapat menentukan lokasi penempatan dan kapasitas optimal bank kapasitor dengan reduksi rugi-rugi daya yang minimum untuk kedua studi kasus.Kata kunci — Rugi-Rugi Daya, Bank Kapasitor, Loss Sensitivity Factors, Algoritma Particle Swarm Optimization. Abstract — Distribution systems that have long lines usually have problems with power losses and votage drop. This problem can be overcome by placing a bank capacitor in the distribution system. The main problem in the placement of bank capacitors is determining the optimal location and capacity. This study uses the Loss Sensitivity Factors (LSF) method to determine the candidate bus that will be selected as the location of the placement of bank capacitors and the Particle Swarm Optimization algorithm method is used to find the optimal capacity of the installed bank capacitor. Simulation is done using MATLAB software. The case studies used were the IEEE 10 bus and Pakter feeder test cases at the Menggala substation. The simulation results show that the combination of the two methods can determine the placement location and optimal capacitor capacity of the bank by reducing the minimum power losses for the two case studies.Keywords— Active power losses, Capacitor Bank, Loss Sensitivity Factors, Particle Swarm Optimization Algorithm.

scholarly journals An Enhanced Multi-Objective Particle Swarm Optimization in Water Distribution Systems Design

Water ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1334
Author(s):  
Mohamed R. Torkomany ◽  
Hassan Shokry Hassan ◽  
Amin Shoukry ◽  
Ahmed M. Abdelrazek ◽  
Mohamed Elkholy
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Particle Swarm ◽  
Large Network ◽  
Swarm Optimization ◽  
Original Algorithm ◽  
Multi Objective

The scarcity of water resources nowadays lays stress on researchers to develop strategies aiming at making the best benefit of the currently available resources. One of these strategies is ensuring that reliable and near-optimum designs of water distribution systems (WDSs) are achieved. Designing WDSs is a discrete combinatorial NP-hard optimization problem, and its complexity increases when more objectives are added. Among the many existing evolutionary algorithms, a new hybrid fast-convergent multi-objective particle swarm optimization (MOPSO) algorithm is developed to increase the convergence and diversity rates of the resulted non-dominated solutions in terms of network capital cost and reliability using a minimized computational budget. Several strategies are introduced to the developed algorithm, which are self-adaptive PSO parameters, regeneration-on-collision, adaptive population size, and using hypervolume quality for selecting repository members. A local search method is also coupled to both the original MOPSO algorithm and the newly developed one. Both algorithms are applied to medium and large benchmark problems. The results of the new algorithm coupled with the local search are superior to that of the original algorithm in terms of different performance metrics in the medium-sized network. In contrast, the new algorithm without the local search performed better in the large network.

scholarly journals Proportional-Integral Controllers Performance of a Grid-Connected Solar PV System with Particle Swarm Optimization and Ziegler–Nichols Tuning Method

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2516
Author(s):  
Klemen Deželak ◽  
Peter Bracinik ◽  
Klemen Sredenšek ◽  
Sebastijan Seme
Keyword(s):  
Particle Swarm Optimization ◽  
Distribution Network ◽  
Particle Swarm ◽  
Open Loop ◽  
Limited Area ◽  
Solar Pv ◽  
Pv System ◽  
Swarm Optimization ◽  
Proportional Integral ◽  
Tuning Method

This paper deals with photovoltaic (PV) power plant modeling and its integration into the medium-voltage distribution network. Apart from solar cells, a simulation model includes a boost converter, voltage-oriented controller and LCL filter. The main emphasis is given to the comparison of two optimization methods—particle swarm optimization (PSO) and the Ziegler–Nichols (ZN) tuning method, approaches that are used for the parameters of Proportional-Integral (PI) controllers determination. A PI controller is commonly used because of its performance, but it is limited in its effectiveness if there is a change in the parameters of the system. In our case, the aforementioned change is caused by switching the feeders of the distribution network from an open-loop to a closed-loop arrangement. The simulation results have claimed the superiority of the PSO algorithm, while the classical ZN tuning method is acceptable in a limited area of operation.

Optimal restoration of power distribution system through particle swarm optimization

2015 ◽  
Author(s):  
Leonardo W. Oliveira ◽  
Edimar J. Oliveira ◽  
Ivo C. Silva ◽  
Flavio V. Gomes ◽  
Thiago T. Borges ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Power Distribution ◽  
Distribution System ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Power Distribution System

Optimal Location and Size of Multiple Renewable Distributed Generation Units in Power Systems Using an Improved Version of Particle Swarm Optimization

2021 ◽  
pp. 15-27
Author(s):  
Mamdouh Kamaleldin AHMED ◽  
◽  
Mohamed Hassan OSMAN ◽  
Nikolay V. KOROVKIN ◽  
◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Distributed Generation ◽  
Particle Swarm ◽  
Optimal Location ◽  
Particle Swarm Algorithm ◽  
Power Losses ◽  
Negative Effects ◽  
Swarm Optimization ◽  
Distributed Generations

The penetration of renewable distributed generations (RDGs) such as wind and solar energy into conventional power systems provides many technical and environmental benefits. These benefits include enhancing power system reliability, providing a clean solution to rapidly increasing load demands, reducing power losses, and improving the voltage profile. However, installing these distributed generation (DG) units can cause negative effects if their size and location are not properly determined. Therefore, the optimal location and size of these distributed generations may be obtained to avoid these negative effects. Several conventional and artificial algorithms have been used to find the location and size of RDGs in power systems. Particle swarm optimization (PSO) is one of the most important and widely used techniques. In this paper, a new variant of particle swarm algorithm with nonlinear time varying acceleration coefficients (PSO-NTVAC) is proposed to determine the optimal location and size of multiple DG units for meshed and radial networks. The main objective is to minimize the total active power losses of the system, while satisfying several operating constraints. The proposed methodology was tested using IEEE 14-bus, 30-bus, 57-bus, 33-bus, and 69- bus systems with the change in the number of DG units from 1 to 4 DG units. The result proves that the proposed PSO-NTVAC is more efficient to solve the optimal multiple DGs allocation with minimum power loss and a high convergence rate.

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