Reconfiguration for loadability limit enhancement of distribution systems

Tyagi, Aditya; Verma, Ashu; Bijwe, P.R.

doi:10.1049/iet-gtd.2017.0090

Cited by 35 publications

(27 citation statements)

References 24 publications

(19 reference statements)

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“…Therefore, new trends seek to automate the existing distribution systems to maximize benefits of these devices. The targeted automation process includes controlling the operation of the connected DGs and CBs as well as reconfiguration of the distribution systems to reduce system losses, improve voltage profile, relive feeders and substation loading, improve power quality and enhances system reliability [6], [7]. Distribution network reconfiguration (DNR) is the process of optimal restructuring (tying and sectionalizing) the distribution networks by altering the normally open and normally closed switches within the distribution network.…”

Section: Introductionmentioning

confidence: 99%

“…A number of meta-heuristic techniques has been introduced in literatures such as; one rank cuckoo search algorithm [27], which is formulated with multi-objectives of power loss minimization, voltage deviation minimization and voltage stability improvement; grey wolf optimizer [28]; stochastic fractal search algorithm [29]; particle swarm optimization [7]; multi-objective chaotic differential evolution [30]. As well, numerous meta-heuristic techniques have been introduced for DNR to enhance the distribution system performance such as; fireworks algorithm [31]; cuckoo search algorithm [32]; genetic algorithm with varying population [33], [34]; harmony search algorithm [6]; selective firefly algorithm [4]. Combining CBs, DGs and DNR with each other greatly improves the performance of distribution systems, but in return the number of control variables is increased, which increases the complexity of this problem.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Control and Operation of Fully Automated Distribution Networks Using Improved Tunicate Swarm Intelligent Algorithm

Fetouh

Elsayed

2020

IEEE Access

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Simultaneous control and operation of Capacitor Banks (CBs) and Distributed Generators (DGs) with Distribution Network Reconfiguration (DNR) is the most important modern trend for distribution systems performance enhancement. The complexity and variety of the control variables of this problem represent a challenge for distribution systems planners and operators as well as interested researchers. Optimal solution can provides quantitative as well as qualitative power service to satisfy consumers' satisfaction and reduces dissipated energy. Furthermore, performance enhancement of distribution systems is very vital where improving their performance directly affects the performance of transmission and generation systems. For solving the considered problem, an improved Tunicate Swarm Algorithm (ITSA), which imitates the swarming behaviors of the marine tunicates and their jet propulsions during its navigation and foraging procedure, is proposed. In ITSA, Lévy flight distributions are emerged in the traditional Tunicate Swarm Algorithm (TSA), which improves the diversification searching abilities of the TSA and consequently avoids the stagnation possibilities. The proposed ITSA is applied and tested for optimal DNR simultaneously with optimal control of the switched CBs and dispatchable DGs taking into account daily load variations. The ITSA is compared with other techniques on the standard 33-bus, 69-bus and the large-scale 119-bus distribution systems considering different automation scenarios of the distribution systems. The simulation results reveal that significant enhancement in distribution system performance is obtained through the application of the proposed automation process using the ITSA. ITSA can efficiently search for the optimal solutions of the problem and outperforms the other existing algorithms in the literatures.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Control and Operation of Fully Automated Distribution Networks Using Improved Tunicate Swarm Intelligent Algorithm

Fetouh

Elsayed

2020

IEEE Access

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“…In [20], a two-stage heuristic NR method using optimal load flow is introduced. Tyagi et al [21] also proposed a two-stage heuristic NR algorithm for loadability enhancement. Jasthi and Das [22] proposed a loss formula based heuristic method for NR.…”

Section: Introductionmentioning

confidence: 99%

Loss allocation in distribution networks with distributed generators undergoing network reconfiguration

Hota¹,

Mishra²

2020

IJECE

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In this paper, a branch exchange based heuristic network reconfiguration method is proposed for obtaining an optimal network in a deregulated power system. A unique bus identification scheme is employed which makes the load flow and loss calculation faster due to its reduced search time under varying network topological environment. The proposed power loss allocation technique eliminates the effect of cross-term analytically from the loss formulation without any assumptions and approximations. The effectiveness of the proposed reconfiguration and loss allocation methods are investigated by comparing the results obtained by the present approach with that of the existing “Quadratic method” using a 33-bus radial distribution system with/without DGs.

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“…The energy diagnosis is the process of consulting the state of transit of energy in all infrastructures [1]. It is the heart of any electrical network management program.…”

Section: Introductionmentioning

confidence: 99%

A Novel Genetic Approach Applied for Power Loss Reduction and Improved Bus Voltage Profile in Distribution Network System

Barradi¹,

Khaldi²,

Zazi³

et al. 2019

IJIES

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This paper presents a network reconfiguration which is a vital analysis process for optimizing and controlling distribution systems. The method is based on genetic algorithm by changing the status of the switches to improve the operational performance. The main objective is to minimize the system power losses and to keep bus voltage profile into limits with radial distribution to provide the consumers with quality electrical energy while minimizing the cost. For this optimization problem, an objective function is developed from an electrical branch to sort the fittest solution. Selection, Crossover and mutation are the necessary three operators in which some improvements are made for the effectiveness of the genetic approach. The method can be successfully applied for loss minimum problem. Numerical example simulated with MATLAB/GUI is demonstrated by 33-bus distribution network and tested using a default mode network. As results, premature convergence is avoided, it shows the validity of the proposed methodology while respecting all the constraints.

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Reconfiguration for loadability limit enhancement of distribution systems

Cited by 35 publications

References 24 publications

Optimal Control and Operation of Fully Automated Distribution Networks Using Improved Tunicate Swarm Intelligent Algorithm

Optimal Control and Operation of Fully Automated Distribution Networks Using Improved Tunicate Swarm Intelligent Algorithm

Loss allocation in distribution networks with distributed generators undergoing network reconfiguration

A Novel Genetic Approach Applied for Power Loss Reduction and Improved Bus Voltage Profile in Distribution Network System

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