Reliability improvement of distribution system by optimal placement of DGs using PSO and neural network

Reddy, Sir C. R.; Prasad, P.V.N.; Laxmi, A. Jaya

doi:10.1109/icceet.2012.6203836

Cited by 12 publications

(6 citation statements)

References 11 publications

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“…In [18], a study by Saeed Amin Hajizadeh and Ehsan Hajizadeh Poor on shunt capacitor placement and distributed generation plants, reported the effect of genetic algorithms on the reduction of power loss and voltage profile in radial distribution systems. In the authors' research, they found that locating distributed generation plants and capacitors at the best locations leads to voltage profiles with lower losses.…”

Section: Genetic Algorthmmentioning

confidence: 99%

Power Loss Minimization and Voltage Profile Improvement by System Reconfiguration, DG Sizing, and Placement

2022

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A number of algorithms that aim to reduce power system losses and improve voltage profiles by optimizing distributed generator (DG) location and size have already been proposed, but they are still subject to several limitations. Hence, new algorithms can be developed or existing ones can be improved so that this important issue can be addressed more appropriately and effectively. This study proposes a reconfiguration methodology based on a hybrid optimization algorithm, consisting of a combination of the genetic algorithm (GA) and the improved particle swam optimization (IPSO) algorithm for minimizing active power loss and maintaining the voltage magnitude at about 1 p.u. The buses at which DGs should be injected were identified based on optimal real power loss and reactive power limit. When applying the proposed optimization algorithm for DGs allocation in power system, the search space or number of iterations was reduced, increasing its convergence rate. The proposed reconfiguration methodology was test in an IEEE-30 bus electrical network system with DGs allocations and the simulations were conducted using MATLAB software compared to other optimization algorithms, such as GA, PSO, and IPSO, the combination of GA and IPSO or Hybrid GA & IPSO (HGAIPSO) method has a smaller number of iterations and is more effective in optimization problems. The effectiveness of the proposed HGAIPSO has been tested on IEEE-30 bus network system with DGs allocations, and the obtained test results have been compared to those from other methods (i.e., GA, PSO, and IPSO). The simulation results show that the proposed HGAIPSO can be an efficient and promising optimization algorithm for distribution network reconfiguration problems. The IEEE-30 bus test system with DGs integrated at various location revealed reductions in overall real power loss of 40.7040%, 36.2403%, and 42.9406% for type 1, type 2, and type 3 DGs allocation, respectively. The highest bus voltage profile goes to 1.01 pu in the IEEE-30 bus.

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Section: Genetic Algorthmmentioning

confidence: 99%

Power Loss Minimization and Voltage Profile Improvement by System Reconfiguration, DG Sizing, and Placement

2022

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“…The next benefit of DG application is considered reduction in system losses due to power generation in loads local and elimination of transmission lines. The losses in the distribution system are dependent on transmission lines current and resistance [10][11]. The losses are function of the system topology, size and location of the DG installation in the system.…”

Section: A2 Losses Reductionmentioning

confidence: 99%

Distribution System Planning With Distributed Generations Considering Benefits and Costs

Soudi¹

2013

IJMECS

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One of the methods used in the design and utilization of distribution systems to improve power quality and reliability of load power supply of consumers, is the application of distributed generation (DG) sources. In this paper, a new method is proposed for the design and utilization of distribution networks with DG resources application by finding the optimal sitting and sizing of generated power of DG with the aim of maximization of its benefits to costs. The benefits for DG are considered as system losses reduction, system reliability improvement and benefits from the sale electricity or from lack of purchase of electricity from the main system. The costs of DG are considered as initial capital, maintenance and operation cost and investment cost. In this paper to solve the optimal sitting and sizing problem a Modified particle swarm optimization (PSO) is applied. Simulations are presented on a 69-bus test distribution system to verify the effectiveness of the proposed method. Results showed that the proposed high-power method to find the optimal points of problem is faster and application of DG resources reduced the losses, costs and improved the system voltage profile

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“…Penetration of have many technical advantages. Furthermore, DG is accessible in modular unit, considered by easy of identify location for small generators, little construction times, and minimum capital investment [4,5]. The technical advantages of DG include improvement of voltage, reduction in loss of power, relieved from transmission and distribution congestion, enhanced network reliability and quality of power.…”

Section: Introductionmentioning

confidence: 99%

Distributed Generation Effect on Distribution System

Kotapuri¹,

Samala²

2021

JESA

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The idea about this proposed work, to know the Distributed Generation (DG) impact on distribution scheme. This is to improve the performance of the system using power loss reduction and voltage development. In this proposed work Wind Turbine (WT) and Photo-Voltaic (PV) units were taken for DGs and various algorithms are tested to get the effect of DG on network. In this paper one new hybrid algorithm is proposed to have optimal size and location of various types of DGs. Initially, active and reactive power losses of the test system and voltage at every bus of the test system were examined using Back and Forward (B/FW) Sweep technique. Similarly, Gravitational Search Analysis (GSA), BAT Analysis (BA) and Ant Lion Optimization (ALO) techniques were utilized to examine the parameters of the same test system. Finally, all the constraints were compared with projected hybrid approach. All the algorithms have tested on IEEE-33 and IEEE-69 standard test systems. Furthermore, the MATLAB simulation is used to get the optimal allocation of DGs.

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Reliability improvement of distribution system by optimal placement of DGs using PSO and neural network

Cited by 12 publications

References 11 publications

Power Loss Minimization and Voltage Profile Improvement by System Reconfiguration, DG Sizing, and Placement

Power Loss Minimization and Voltage Profile Improvement by System Reconfiguration, DG Sizing, and Placement

Distribution System Planning With Distributed Generations Considering Benefits and Costs

Distributed Generation Effect on Distribution System

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