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Proceedings ArticleDOI

Composite active and reactive power compensation of distribution networks

TL;DR: The active and reactive power compensation can be obtained by the optimal placement of DG and Capacitor and the evaluation of optimal power factor of the system has been carried out.
Abstract: In this paper the application of Particle Swarm Optimization (PSO) technique for active and reactive power compensation of power distribution loss has been proposed. The active and reactive power compensation can be obtained by the optimal placement of DG and Capacitor. The evaluation of optimal power factor of the system has also been carried out in this work. To solve the optimal placement problem PSO technique has been used. The locations of DG and capacitor play an important role in maintaining voltage profiles. The results obtained from PSO have also been compared with the fast analytical approach. The proposed technique is tested on 33-bus power distribution system.
Citations
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Journal ArticleDOI
TL;DR: The particle swarm optimization (PSO) technique has been used to solve the optimal placement of DGs and the optimal power factor for DG supplying, both real and reactive power, has been obtained.

322 citations

Journal ArticleDOI
TL;DR: In this paper, the Particle Artificial Bee Colony (PABC) is utilized to enhance the harmony memory vector to minimize power losses in radial distribution networks and facilitate an enhancement in bus voltage profile by determining optimal locations, optimally sized distributed generators and shunt capacitors.

180 citations

Journal ArticleDOI
TL;DR: In this paper, the Particle Swarm Optimization (PSO) technique has been used to find the near-optimal solutions for the capacitor allocation problem in distribution systems for the modified IEEE 16-bus distribution system connected to wind energy generation based on a cost function.

98 citations


Cites methods from "Composite active and reactive power..."

  • ...have presented the optimal allocation of different DG using PSO technique for active and reactive power compensation to minimize the real power losses in the primary distribution networks [31,32]....

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Proceedings ArticleDOI
22 Mar 2013
TL;DR: Weight-Improved Particle Swarm Optimization Algorithm (WIPSO) for optimal location and sizing of compensating devices is proposed and results illustrates improvement in network voltage profile, reduction in system real and reactive power loss and reduction in cost of service provided by these local devices.
Abstract: The real and reactive power service by local devices plays an important role in continuous electric energy supply and energy management of the distribution system under peak load or over load conditions. The compensating devices are greatly utilized to provide the necessary real and reactive power support and to share the peak load demand. This paper presents optimal location and sizing of Distributed Generation (DG) and capacitor in distribution network to provide the necessary active and reactive power support, to minimize the system real and reactive power loss and to maintain the network voltage level within a desired range. The main objective of this paper is to minimize the cost of service provided by these local devices. This paper proposes Weight-Improved Particle Swarm Optimization Algorithm (WIPSO) for optimal location and sizing of compensating devices. The proposed method is efficiently examined in test system and comparative studies before and after installation of Distributed Generators and capacitors is made. Results illustrates improvement in network voltage profile, reduction in system real and reactive power loss and reduction in cost of service provided by these local devices.

22 citations


Cites methods from "Composite active and reactive power..."

  • ...Here the active and reactive power compensation is obtained through DG and capacitor respectively [12]....

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  • ...DG and capacitor are integrated to compensate active and reactive power loss in the distribution network using PSO technique and optimal power factor is also evaluated [12]....

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Journal ArticleDOI
TL;DR: A novel efficient and vigorous moth–flame optimization (MFO) algorithm for solving the optimization problem of DGs and SCBs allocation and has notability with high accuracy to estimate the optimal solution for minimizing the system power losses, enhancing the voltage profile and maximizing the net savings as compared to other techniques.
Abstract: In recent years, due to growth in electricity demand, distribution networks suffer from increased power losses, decreased voltage levels, and increased power quality problems. The distributed generations (DGs) and shunt capacitor banks (SCBs) are considered the convenient sources of active and reactive power compensation in distribution networks, respectively. Moreover, the optimum allocation of DGs and SCBs plays a sufficient role in improving voltage profile and voltage stability that lead to ameliorate power quality and minimize the system power losses. In this respect, this article provides a novel efficient and vigorous moth–flame optimization (MFO) algorithm for solving the optimization problem of DGs and SCBs allocation. Furthermore, a loss–voltage–cost index (LVCI) approach has been incorporated into the proposed optimization methodology as an effective objective function to enhance the voltage profile and minimize the system power losses and the total annual operating cost. Moreover, the proposed scheme is implemented in two stages. In the first stage, the most candidate buses for installing DGs and SCBs are evaluated using loss sensitivity factors (LSFs). In the second stage, the MFO optimization algorithm is implemented to estimate the optimal placement of DGs and SCBs besides their sizing from the nominated buses based on LVCI as the main objective function. The suggested scheme has been tested on 33-bus and 69-bus IEEE standard radial distribution networks with different load levels. Furthermore, it is applied on a practical case study of Moscow region network that consists of 111-bus radial distribution network under different load levels. To insure the validation and accuracy of the proposed algorithm, the acquired results have been compared with other methods and techniques. The numerical results proved that the suggested optimization scheme has notability with high accuracy to estimate the optimal solution of DGs and SCBs allocation for minimizing the system power losses, enhancing the voltage profile and maximizing the net savings as compared to other techniques.

20 citations

References
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Proceedings ArticleDOI
06 Aug 2002
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.

35,104 citations

Proceedings ArticleDOI
16 Jul 2000
TL;DR: It is concluded that the best approach is to use the constriction factor while limiting the maximum velocity Vmax to the dynamic range of the variable Xmax on each dimension.
Abstract: The performance of particle swarm optimization using an inertia weight is compared with performance using a constriction factor. Five benchmark functions are used for the comparison. It is concluded that the best approach is to use the constriction factor while limiting the maximum velocity Vmax to the dynamic range of the variable Xmax on each dimension. This approach provides performance on the benchmark functions superior to any other published results known by the authors.

2,922 citations

Journal ArticleDOI
TL;DR: In this article, the relevant issues and aims at providing a general definition for distributed power generation in competitive electricity markets are discussed, which can be defined as electric power generation within distribution networks or on the customer side of the network.

2,484 citations


"Composite active and reactive power..." refers background in this paper

  • ...Though the DG is consider as a viable solution to most of the problems that the utility are facing....

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Journal ArticleDOI
TL;DR: In this article, an analytical expression to calculate the optimal size and an effective methodology to identify the corresponding optimum location for DG placement for minimizing the total power losses in primary distribution systems is proposed.

1,060 citations


"Composite active and reactive power..." refers methods in this paper

  • ...…for achieving the optimal placement of DG in the distribution generation systems for loss reduction can be listed as, fuzzy-GA method [5], genetic algorithm and Hereford Ranch algorithm [6], the genetic algorithm [7] improved Tabu Search [8], ant colony search algorithm [9], PSO technique…...

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  • ...Many researchers have applied other techniques such as fuzzy expert system [17], Tabu search [18], and dynamic programming [19] for finding the best locations for the placement of capacitors to reduce losses....

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Journal ArticleDOI
TL;DR: In this article, the optimal location to place a DG in radial as well as networked systems to minimize the power loss of the system has been investigated to obtain the maximum potential benefits.
Abstract: Power system deregulation and the shortage of transmission capacities have led to increased interest in distributed generation (DG) sources. Proper location of DGs in power systems is important for obtaining their maximum potential benefits. This paper presents analytical methods to determine the optimal location to place a DG in radial as well as networked systems to minimize the power loss of the system. Simulation results are given to verify the proposed analytical approaches.

1,042 citations