Relevant bibliographies by topics / Distribution Feeder Reconfiguration (DFR)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Distribution Feeder Reconfiguration (DFR)'

Author: Grafiati
Published: 3 June 2025
Last updated: 22 June 2025

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Journal articles on the topic "Distribution Feeder Reconfiguration (DFR)"

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Civanlar, S., J. J. Grainger, H. Yin, and S. S. H. Lee. "Distribution feeder reconfiguration for loss reduction." IEEE Transactions on Power Delivery 3, no. 3 (1988): 1217–23. http://dx.doi.org/10.1109/61.193906.

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Noudjiep Djiepkop, Giresse Franck, and Senthil Krishnamurthy. "Multi-Objective Feeder Reconfiguration Using Discrete Particle Swarm Optimization." Mathematics 10, no. 3 (2022): 531. http://dx.doi.org/10.3390/math10030531.

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Electric power distribution systems have been heavily engaged in evolutionary changes toward effective usage of distribution networks for dependability, quality, and improvement of services delivered to customers throughout the years. This was accomplished via a procedure known as reconfiguration. Several strategies have been offered by various authors for successful distribution feeder reconfiguration with a novel optimization method. As a result, this work developed a Discrete Particle Swarm Optimization (DPSO) method to address the issue of distribution system feeder reconfiguration during both steady-state and dynamic power system operations. In a dynamic state, the power demand and generation required are continually changing over time, and the DPSO algorithm finds a new set of solutions to fulfill the power demand. Many network topologies are investigated for the dynamic operation. The feeder reconfiguration single-objective optimization problem was transformed into a multi-objective optimization problem by taking into account both real power loss reduction and distribution system load balancing. The suggested technique was verified using various IEEE 16, 33, and 69 bus standard test distribution systems to determine the efficiency of the developed DPSO algorithm. The simulation findings reveal that DPSO outperforms other optimization algorithms in terms of actual power loss reduction and load balancing, while solving multi-objective distribution system feeder reconfiguration.
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Rajaram, R., K. Sathish Kumar, S. Prabhakar Karthikeyan, and J. Edward Belwin. "Distribution System Reconfiguration for Loss Minimization Using Modified Artificial Neural Network Approach of 16 Bus and 33 Bus Standard Test Systems with an Compensator." Applied Mechanics and Materials 573 (June 2014): 767–76. http://dx.doi.org/10.4028/www.scientific.net/amm.573.767.

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– This paper presents a new method for identifying the best switching option for the reconfiguration of Radial Distribution Systems (RDS). Feeder reconfiguration is the technique to alter the topological structure of the distribution feeder by changing the open/close status of sectionalizing and tie switches. The reconfiguration involves in selection of the set of sectionalizing switches to be opened and tie switch to be closed such that the resulting RDS has the desirable performance. Amongst the several criteria considered for optimal network configuration, loss minimization criterion is very widely used. In this project a novel method is presented which utilizes feeder reconfiguration as a planning and real time control tool in order to restructure the primary feeders for the loss minimization. The mathematical formulation of the proposed method is given; the solution procedure is illustrated with an example. Owing to the discrete nature of the solution space, a neural network approach for optimal reconfiguration of distribution network is proposed.
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Huang, Yen-Chih, Wen-Ching Chang, Hsuan Hsu, and Cheng-Chien Kuo. "Planning and Research of Distribution Feeder Automation with Decentralized Power Supply." Electronics 10, no. 3 (2021): 362. http://dx.doi.org/10.3390/electronics10030362.

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The high penetration of distributed generation in distributed energy systems causes the variation of power loss and makes the power grid become more complicated, so this paper takes various types of optimal algorithms into account and simulates the feeder reconfiguration on the IEEE-33 system as well as the Taiwan power system. The simulation verifies linear population size reduction of successful history-based adaptive differential evolution (L-SHADE) and particle swarm optimization (PSO) fitness in different systems and provides the recommended location of distributed energy. The proposed method keeps the voltage bound of 0.95 to 1.03 p.u. of Taiwan regulation. In the IEEE-33 system, we achieved a 52.57% power loss reduction after feeder reconfiguration, and a 70.55% power loss reduction after the distributed generator was implemented and feeder reconfiguration. Under the variation of load demand and power generation of the Taiwan power system, we establish the system models by forecasting one-day load demand. Then, we propose a one-day feeder switch operation strategy by considering the switches’ operation frequency with the reduction of 83.3% manual operation and recommend feeder automation to achieve feeder power loss reduction, voltage profile improvement and get regional power grid resilient configuration.
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Poornachandra Rao, G., and P. Ravi Babu. "Load Balancing and Restoring Service by Using Hybrid Ant Lion and Improved Mayfly Optimization Technique." Journal of Asian Energy Studies 7 (April 1, 2023): 62–76. http://dx.doi.org/10.24112/jaes.070005.

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The most common problems encountered in any electrical distribution system are load unbalance and restoring service to healthy zones in the case of a fault. The load on the distribution network is not constant and varies from feeder to feeder throughout the day. In the event of a fault, network reconfiguration is performed to balance the loads and restore service to healthy zones. Network reconfiguration involves modifying the structure of a network by sectionalizing and tie switches. Through reconfiguration, loads can be transferred from a feeder that is relatively heavily loaded to one that is relatively lightly loaded. A hybrid Ant Lion Optimization and Improved Mayfly Optimization (ALO-IMO) technique for load balancing and restoring service is implemented in this paper. The proposed technique is employed for load balancing and restoring service on the IEEE 3 feeder system, as well as restoring service on the IEEE 4 feeder system.
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Kavousi-Fard, Abdollah, and Mohammad-Reza Akbari-Zadeh. "Reliability enhancement using optimal distribution feeder reconfiguration." Neurocomputing 106 (April 2013): 1–11. http://dx.doi.org/10.1016/j.neucom.2012.08.033.

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Qin Zhou, D. Shirmohammadi, and W. H. E. Liu. "Distribution feeder reconfiguration for operation cost reduction." IEEE Transactions on Power Systems 12, no. 2 (1997): 730–35. http://dx.doi.org/10.1109/59.589665.

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Lin, W. M., F. S. Cheng, and M. T. Tsay. "Distribution feeder reconfiguration with refined genetic algorithm." IEE Proceedings - Generation, Transmission and Distribution 147, no. 6 (2000): 349. http://dx.doi.org/10.1049/ip-gtd:20000715.

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Barbosa, Carlos Henrique Nogueira de Resende, Marcus Henrique Soares Mendes, and João Antônio de Vasconcelos. "Robust feeder reconfiguration in radial distribution networks." International Journal of Electrical Power & Energy Systems 54 (January 2014): 619–30. http://dx.doi.org/10.1016/j.ijepes.2013.08.015.

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Reddy, A. V. Sudhakara, M. Damodar Reddy, and M. Satish Kumar Reddy. "Network Reconfiguration of Primary Distribution System Using GWO Algorithm." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (2017): 3226. http://dx.doi.org/10.11591/ijece.v7i6.pp3226-3234.

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This manuscript presents a feeder reconfiguration in primary distribution networks with an objective of minimizing the real power loss or maximization of power loss reduction. An optimal switching for the network reconfiguration problem is introduced in this article based on step by step switching and simultaneous switching. This paper proposes a Grey Wolf Optimization (GWO) algorithm to solve the feeder reconfiguration problem through fitness function corresponding to optimum combination of switches in power distribution systems. The objective function is formulated to solve the reconfiguration problem which includes minimization of real power loss. A nature inspired Grey Wolf Optimization Algorithm is utilized to restructure the power distribution system and identify the optimal switches corresponding minimum power loss in the distribution network. The GWO technique has tested on standard IEEE 33-bus and 69-bus systems and the results are presented.
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Dissertations / Theses on the topic "Distribution Feeder Reconfiguration (DFR)"

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Peng, Zhengrui. "Feeder reconfiguration on distribution network considering harmonics." Thesis, University of Strathclyde, 2017. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=28416.

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One of the most important measures that can be employed to enhance the supply reliability and quality of electrical energy in a distribution network is feeder reconfiguration. Many studies have been conducted in this area, but only a minor proportion have considered the concept of system harmonics in feeder reconfiguration. In recent years, an increasing number of invertor/converter-based renewable generators are being connected to the distribution network, and given that these generators are harmonic sources, it is important to consider the impacts of the system harmonics in the feeder reconfiguration. Load flow analysis is used to determine a suitable network structure for specific purposes in the feeder reconfiguration problem. In this thesis, a new load flow method is proposed based on the backward/forward sweep method. This method can analyse distribution network load flow under both fundamental and harmonic conditions with distributed generators. Following this, a hybrid optimization method is proposed based on the salient features of the ant colony system and particle swarm optimization. This hybrid method has a higher searching accuracy performance for feeder reconfiguration when compared, on test system, with the ant colony system and particle swarm optimization. Finally, a 118 mid-voltage level distribution system is used to investigate the impacts of renewable generators and system harmonics. The test results verify that system harmonics will have a significant influence on feeder reconfiguration and, consequently, cannot be ignored. Furthermore, other factors including the different capacities of renewable generators, fluctuations in load demands over 24 hours, and the variable output of the renewable generators in different seasons are also investigated in the feeder reconfiguration problem.
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Ghaweta, Ahmad. "OPTIMAL DISTRIBUTION FEEDER RECONFIGURATION WITH DISTRIBUTED GENERATION USING INTELLIGENT TECHNIQUES." UKnowledge, 2019. https://uknowledge.uky.edu/ece_etds/134.

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Feeder reconfiguration is performed by changing the open/close status of two types of switches: normally open tie switches and normally closed sectionalizing switches. A whole feeder or part of a feeder may be served from another feeder by closing a tie switch linking the two while an appropriate sectionalizing switch must be opened to maintain the radial structure of the system. Feeder reconfiguration is mainly aiming to reduce the system overall power losses and improve voltage profile. In this dissertation, several approaches have been proposed to reconfigure the radial distribution networks including the potential impact of integrating Distributed Energy Resources (DER) into the grid. These approaches provide a Fast-Genetic Algorithm “FGA” in which the size and convergence speed is improved compared to the conventional genetic algorithm. The size of the population matrix is also smaller because of the simple way of constructing the meshed network. Additionally, FGA deals with integer variable instead of a binary one, which makes FGA a unique method. The number of the mesh/loop is based on the number of tie switches in a particular network. The validity of the proposed FGA is investigated by comparing the obtained results with the one obtained from the most recent approaches. The second the approach is the implementation of the Differential Evolution (DE) algorithm. DE is a population-based method using three operators including crossover, mutation, and selection. It differs from GA in that genetic algorithms rely on crossover while DE relies on mutation. Mutation is based on the differences between randomly sampled pairs of solutions in the population. DE has three advantages: the ability to find the global optimal result regardless of the initial values, fast convergence, and requirement of a few control parameters. DE is a well-known and straightforward population-based probabilistic approach for comprehensive optimization. In distribution systems, if a utility company has the right to control the location and size of distributed generations, then the location and size of DGs may be determined based on some optimization methods. This research provides a promising approach to finding the optimal size and location of the planned DER units using the proposed DE algorithm. DGs location is obtained using the sensitivity of power losses with respect to real power injection at each bus. Then the most sensitive bus is selected for installing the DG unit. Because the integration of the DG adds positive real power injections, the optimal location is the one with the most negative sensitivity in order to get the largest power loss reduction. Finally, after the location is specified, the proposed Differential Evolution Algorithm (DEA) is used to obtain the optimal size of the DG unit. Only the feasible solutions that satisfy all the constraints are considered. The objective of installing DG units to the distribution network is to reduce the system losses and enhance the network voltage profile. Nowadays, these renewable DGs are required to equip with reactive power devices (such as static VAR compensators, capacitor banks, etc.), to provide reactive power as well as to control the voltage at their terminal bus. DGs have various technical benefits such as voltage profile improvement, relief in feeder loading, power loss minimization, stability improvement, and voltage deviation mitigation. The distributed generation may not achieve its full potential of benefits if placed at any random location in the system. It is necessary to investigate and determine the optimum location and size of the DG. Most distribution networks are radial in nature with limited short-circuit capacity. Therefore, there is a limit to which power can be injected into the distribution network without compromising the power quality and the system stability. This research is aiming to investigate this by applying DG technologies to the grid and keeping the system voltage within a defined boundary [0.95 - 1.05 p.u]. The requirements specified in IEEE Standard 1547 are considered. This research considers four objectives related to minimization of the system power loss, minimization of the deviations of the nodes voltage, minimization of branch current constraint violation, and minimization of feeder’s currents imbalance. The research formulates the problem as a multi-objective problem. The effectiveness of the proposed methods is demonstrated on different revised IEEE test systems including 16 and 33-bus radial distribution system.
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Galymov, Birzhan. "Distribution Network Reconfiguration For Loss Reduction By Multi-branch Exchange Method." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614663/index.pdf.

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As structure and size of electric power distribution systems are getting more complex, distribution automation schemes become more attractive. One of the features that is desirable in an automated system is feeder reconfiguration for loss reduction. Loss reduction can make considerable savings for a utility and results in released system capacity. There is also improved voltage regulation in the system as a result of reduced feeder voltage drop. In this thesis, multi branch exchange algorithm is introduced to solve the network reconfiguration for loss reduction problem. The proposed technique is based on heuristic techniques applied to constraint satisfaction optimization problems. A critical review of earlier methods related with feeder reconfiguration is presented. A computer program was developed using Matlab to simulate this algorithm and results of simulations demonstrate its advantages over single branch exchange method. Moreover, the results show that the final configuration is independent of the initial configuration and give assurance that any solution offered will have a radial configuration with all loads connected.
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Kuo, Jeng Chian, and 郭政謙. "Feeder Reconfiguration in Distribution Systems by Simulated Annealing." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/31272972046816222858.

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碩士<br>國立臺灣科技大學<br>工程技術研究所<br>81<br>A modified simulated annealing technique is applied to network reconfiguration in distribution systems for loss reduction. A set of simplified line flow equations are first presented for approximate loss calculation. The loss profile of the whole system can be computed in a straightforward single-pass manner. Then an efficient perturbation scheme as well as an initialization procedure determining a better beginning temperature of the simulated annealing are also proposed. It follows that the computation time of the simulated annealing can be greatly reduced without degrading the solution quality. Besides, the effects due to switching limitation involved in network reconfiguration can be considered. The first operation is found to be the most effective, and the subsequent switching operations result in diminishing effects for loss reduction. A salient feature of the method lies in that it can fast provide a global optimal or near optimal solution for network reconfiguration problem. To verify the effectiveness of the proposed method, comparative studies are conducted on three test systems with rather encouraging results.
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Lee, Chu-Sheng, and 李居昇. "Optimal Feeder Reconfiguration and Capacitor placement in Distribution Systems." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/50769298380031204307.

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博士<br>國立中正大學<br>電機工程研究所<br>91<br>ABSTRACT This dissertation aims to introduce and explore methods for feeder reconfiguration and capacitor placement of distribution systems. Hopefully, this study can benefit automation management control of distribution systems. Mathematically, feeder reconfiguration and capacitor placement are optimization problems, in which an objective function must be optimized subject to certain constraints. Techniques such as simulated annealing (SA), genetic algorithm (GA), and mixed-integer hybrid differential evolution (MIHDE) are employed to solve those optimization problems. Feeder reconfiguration is performed via changing the open/closed states of sectionalizing switches and tie switches to alter the topological configuration of the system. Two reliability indices are employed to evaluate the system reliability, namely system average interruption frequency index (SAIFI) and system average interruption duration index (SAIDI). Formulations are developed for computing these reliability indices and power loss. Feeder reconfiguration is an optimization problem aimed at achieving the objectives of reliability improvement and loss reduction. This problem is solved using the SA and MIHDE techniques. Capacitor placement is employed to reduce power loss and correct voltage deviation for given load patterns. The problem of capacitor placement includes determining the location, size and type of the capacitors compensated. Capacitor placement is a combinatorial optimization problem that contains an objective function comprising power losses and capacitor installation costs subject to voltage constraints, which is solved here by employing the SA, GA, and MIHDE techniques. The nonlinear part of electrical loads has enhanced significantly in recent years. This study examines optimal capacitor placement, taking into account distorted substation voltage and nonlinear loads. The objective is to correct voltage deviation and reduce power loss under total harmonic distortion (THD) limits. This study also explores the problem of simultaneously considering feeder reconfiguration and capacitor settings. The numerical results show that the objective can be optimized more effectively by considering the above two together than by considering them separately. The proposed approaches are demonstrated using practical secondary substations of the Taiwan Power Company and some IEEE example systems. The proposed methods are applicable to both planning new systems and operating existing systems.
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Li, Ju Sheng, and 李居昇. "A feeder reconfiguration approach to reliability improvement for distribution systems." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/48937268871403515598.

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ZHANG, ZHI-MING, and 張志銘. "A study on feeder reconfiguration for distribution system line loss reduction." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/09913725951615246060.

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He, Shuo-Yu, and 何碩俞. "Feeder Reconfiguration in Distribution Systems for Voltage Sag Reduction by Genetic Algorithms." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/57412391632046162040.

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碩士<br>中原大學<br>電機工程研究所<br>90<br>Voltage sags are usually caused by shorts circuits, overloads, and starting of large motors. The interest in voltage sags is mainly due to the problems they cause on several types of equipment: adjustable-speed drives, process control equipment, and computers. In this thesis, a method based on the Genetic algorithms and decision making for multi-objective programming problems to study the reconfiguration in distribution systems for loss and voltage sag reduction is proposed. A set of simplified power flow equations is used for approximate loss calculation. Then the Prufer number is used for encoding a tree structure on the Genetic algorithms. A 16-bus system and a 33-bus system are used as test systems for showing the applicability of the proposed method.
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Wu, Wu-Chang, and 吳武昌. "Applications of Particle Swarm Optimization and Pareto Optimality for the Studies of Distribution System Feeder Reconfiguration." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/rbrm2g.

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博士<br>國立臺北科技大學<br>機電科技研究所<br>99<br>Feeder reconfiguration is an important function in Distribution Management System. Feeder reconfiguration is performed by opening/closing of closed switches and opened switches. During normal distribution system operations, by changing the on/off status of these switches can reduce system loss or operate the system more reliable and economic. When the status of switch is changed, the resulting topology of the distribution system must be maintained in radial structure. The constraints such as feeder loading and voltage profile should not be violated after reconfiguration. Under abnormal condition, feeder reconfiguration can be used to restore services to de-energized zones after fault is isolated. Since the reconfiguration is done by changing the status of switches, it can be categorized as a non-linear multi-objective combinational optimization problem. It is a difficult task to obtain the best switch operation plan by applying regular mathematics optimization methods. Since there are a lot of switches on distribution system, the goal to develop an optimal reconfiguration algorithm is to eliminate unnecessary searching for switch pairs. By doing so, searching time of the algorithm can be reduced while the accuracy of results identified by the algorithm should be maintained. To achieve this goal, this dissertation adopts the particle swarm optimization for feeder reconfiguration problems. In order to develop a feasible multi-objective particle swarm optimization algorithm which embedded Pareto Optimality technique for solving the feeder reconfiguration problem, this dissertation not only discusses the feasibility of applying particle swarm optimization for feeder reconfiguration but also reviews the requirements of the multi-objective feeder reconfiguration problems.
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Chang, Che-Ming, and 張哲銘. "Application of real-coding on evolutionary computation for distribution system feeder reconfiguration problems under load variations." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/77c8x9.

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碩士<br>國立臺北科技大學<br>自動化科技研究所<br>100<br>As the concepts of environmental protection and energy saving bring more attention, how to reduce the energy loss during the normal operations of distribution system becomes an important issue. Feeder reconfiguration is a very important technique that can be used to deal with different types of distribution system problems. By changing the distribution system structure the distribution system can be operated in a more efficient way during normal and contingency operations. Feeder reconfiguration is a typical combinatorial optimization problem. Due to the large amount of switches on a distribution system, the possible solutions of the switching operation plans increase dramatically. Therefore, searching for the best switching operation plan to accomplish the feeder reconfiguration becomes an important issue. This paper applies real-coding of Genetic Algorithm for single- and multi-objectives feeder reconfiguration under fixed load and various load conditions. The searching efficiency and stability with other coding methods are compared. In the multi-objectives feeder reconfiguration problems, the improved TOPSIS is applied to calculate the fitness value of the Genetic Algorithm in order to effectively solve the feeder reconfiguration problems.
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Books on the topic "Distribution Feeder Reconfiguration (DFR)"

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Wong, A. M. C. H. Simulation of feeder switching reconfiguration in power distribution systems. UMIST, 1992.

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Book chapters on the topic "Distribution Feeder Reconfiguration (DFR)"

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Hooshmand, Ehsan, and Abbas Rabiee. "Distribution Feeder Reconfiguration Considering Price-Based Demand Response Program." In Demand Response Application in Smart Grids. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32104-8_5.

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Shobha, M., and B. Datta. "Multi-objective Hybrid Optimal Algorithm for Distribution System Feeder Reconfiguration." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-7630-0_17.

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Sathish Kumar, K., K. Rajalakhsmi, S. Prabhakar Karthikeyan, and R. Rajaram. "An Efficient Invasive Weed Optimization Algorithm for Distribution Feeder Reconfiguration and Loss Minimization." In Advances in Intelligent Systems and Computing. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2126-5_5.

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"Distribution Feeder Reconfiguration for Service Restoration and Load Balancing." In Power System Restoration. IEEE, 2009. http://dx.doi.org/10.1109/9780470545607.ch86.

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Rugthaicharoencheep, Nattachote, and Somporn Sirisumranukul. "Optimal Feeder Reconfiguration with Distributed Generation in Three-Phase Distribution System by Fuzzy Multiobjective and Tabu Search." In Energy Technology and Management. InTech, 2011. http://dx.doi.org/10.5772/14233.

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Conference papers on the topic "Distribution Feeder Reconfiguration (DFR)"

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Saputra, Fajar Wahyu, Kevin Marojahan Banjarnahor, and Nanang Hariyanto. "Advanced Distribution Management System: Automation of Optimal Feeder Reconfiguration in Spindle Distribution Networks." In 2024 6th International Conference on Power Engineering and Renewable Energy (ICPERE). IEEE, 2024. https://doi.org/10.1109/icpere63447.2024.10845476.

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Ayikpa, Malinwo Estone. "Optimal Placement and Sizing of Distributed Generations and Capacitor Banks in Large-Scale Distribution Network Using Feeder Reconfiguration." In 2025 IEEE Texas Power and Energy Conference (TPEC). IEEE, 2025. https://doi.org/10.1109/tpec63981.2025.10906970.

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Siti, W. M., A. A. Jimoh, and D. V. Nicolae. "Load balancing in distribution feeder through reconfiguration." In 31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005. IEEE, 2005. http://dx.doi.org/10.1109/iecon.2005.1568914.

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Liyanarathne, B. L. N. S. S., A. K. V. Gunasekara, D. V. A. V. Y. Senarathna, W. C. M. Perera, P. S. N. de Silva, and H. M. A. I. Herath. "Distribution Feeder Reconfiguration Algorithm for Electrical Outage Situations." In 2021 6th International Conference for Convergence in Technology (I2CT). IEEE, 2021. http://dx.doi.org/10.1109/i2ct51068.2021.9418206.

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Venkataramu, P. S., T. Ananthapadmanabha, Prabhakar Karthikeyan, and Jacob Raglend. "Reconfiguration of Distribution System Feeder Using Slope Criterion." In Power Systems Conference. SAE International, 2008. http://dx.doi.org/10.4271/2008-01-2916.

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Olamaei, J., T. Niknam, and G. Gharehpetian. "Impact of Distributed Generators on Distribution Feeder Reconfiguration." In 2007 IEEE Power Tech. IEEE, 2007. http://dx.doi.org/10.1109/pct.2007.4538580.

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Elsaiah, Salem, Mohammed Benidris, and Joydeep Mitra. "Reliability improvement of power distribution system through feeder reconfiguration." In 2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, 2014. http://dx.doi.org/10.1109/pmaps.2014.6960676.

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Jose, Joel, and Anupama Kowli. "Reliability constrained distribution feeder reconfiguration for power loss minimization." In 2016 19th National Power Systems Conference (NPSC). IEEE, 2016. http://dx.doi.org/10.1109/npsc.2016.7858938.

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Rasheed, Shaziya, Megha Gupta, and A. R. Abhyankar. "Feeder Voltage Dependent Distribution Network Reconfiguration for Loss Reduction." In 2018 20th National Power Systems Conference (NPSC). IEEE, 2018. http://dx.doi.org/10.1109/npsc.2018.8771851.

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Azadi, Hamidreza, Behnam Akbari, and Mohammad Sadegh Sepasian. "Power Quality Enhancement in Distribution Systems Using Feeder Reconfiguration." In 2018 Smart Grid Conference (SGC). IEEE, 2018. http://dx.doi.org/10.1109/sgc.2018.8777854.

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