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

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Published: 3 June 2025

Last updated: 22 June 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Juliartawan, Putu, I. Nyoman Setiawan, and I. Wayan Arta Wijaya. "REKONFIGURASI PENYULANG KINTAMANI UNTUK MENURUNKAN.RUGI-RUGI.DAYA.DAN JATUH TEGANGAN PADA WILAYAH KERJA ULP BANGLI." Jurnal SPEKTRUM 9, no. 1 (2022): 130. http://dx.doi.org/10.24843/spektrum.2022.v09.i01.p15.

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The Kintamani supplay is uone of the feedersl in the PT PLN (Persero) UP3 East Bali Work Areawhich is the responsibility of ULP Bangli. This feeder uses a loop type configuration that servesconsumers in the Penelokan, Songan, Kintamani, Writing, Catur and Sukawana areas. This feederhas a channel length of 237,886 kms with a total distribution substation of 183 transformer units andthe peak load of this feeder reaches 3,160 kW. This condition causes a voltage drop of 7.68% andpower losses of 7.5%. The reconfiguration of this feeder is done by cutting the network which istransferred to Nandini's feeder. Reconfiguration by cutting the network shows an improvement in thevoltage of the Kintamani feeder which initially dropped from 7.68% to 3.27% and decreased powerlosses from 7.5% to 4.81%. Improvements in voltage and power losses that occur in this feederchange the value of the voltage drop and power losses on the Nandini feeder, which has an increasein the percentage of voltage drop from (-)1.44% to 1.27% and losses the initial power of 0.5% to2.10%. With this the Kintamani feeder is right for reconfiguration with the Nandini feeder. Thus, for theKintamani feeder and the Nandini feeder, it is in accordance with the 2017-2022 Distribution SystemMaster Plan Manual, where the maximum allowable voltage drop is a maximum of 19kV and amaximum power loss of 5%.

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12

A., V. Sudhakara Reddy, Damodar Reddy M., and Satish Kumar Reddy M. "Network Reconfiguration of Distribution System for Loss Reduction Using GWO Algorithm." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (2017): 3226–34. https://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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13

Wijaya, I. K. A., R. S. Hartati, and I. W. Sukerayasa. "OPTIMASI PENEMPATAN KAPASITOR BANK UNTUK PERBAIKAN RUGI DAYA PADA PENYULANG SABA MENGGUNAKAN ALGORITMA GENETIKA." Jurnal SPEKTRUM 6, no. 2 (2019): 7. http://dx.doi.org/10.24843/spektrum.2019.v06.i02.p02.

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Saba feeder is a feeder who supplies 78 distribution transformers with feeder length 38,959 kms, through this Saba feeder electrical energy is channeled radially to each distribution substation. In 2017 the voltage shrinkage at Saba feeder was 9.88% (18,024 kV) while the total power loss was 445.5 kW. In this study an attempt was made to overcome the voltage losses and power losses using the method of optimizing bank capacitors with genetic algorithms and network reconfiguration. The best solution obtained from this study will be selected for repair of voltage losses and power losses in Saba feeders. The results showed that by optimizing bank capacitors using genetic algorithms, the placement of capacitor banks was placed on bus 23 (the channel leading to the BB0024 transformer) and successfully reduced the power loss to 331.7 kW. The network reconfiguration succeeded in fixing the voltage on the Saba feeder with a voltage drop of 4.75% and a total power loss of 182.7 kW. With the combined method, reconfiguration and optimization of bank capacitors with genetic algorithms were obtained on bus 27 (channel to transformer BB0047) and managed to reduce power losses to 143 kW.

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14

Fathur Nureza Aksan and Samsurizal. "Studi Rekonfigurasi Sistem Distribusi Pada Jaringan 20 kV Dengan Metode Simple Branch Exchange Pada Penyulang Cempaka." EPSILON: Journal of Electrical Engineering and Information Technology 19, no. 2 (2021): 45–52. http://dx.doi.org/10.55893/epsilon.v19i2.64.

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The increasing electric energy consumption must be balanced with good electricity network quality. To overcome this, we need a distribution network reconfiguration. Reconfiguration in power distribution network is done to improve power distribution network quality. Problem that exist in the radial distribution network is power losses. To minimize the power losses can be overcome by compensating capacitor. Done by determining the location of capacitors in distribution networks. With that the capacitor compensates the distribution network is more effective and efficient in distributing power. In this research discusses the network reconfiguration using simple branch exchange method to reduce power losses in radial distribution networks. The reconfiguration method was carried out with the help of the Electrical Transient Analysis Program (ETAP) 12.6.0 software and has been tested by PT REKAYASA INDUSTRI and PLN Area South Kalimantan on the 20 kV distribution system of Cempaka feeder, South Kalimantan. With this reconfiguration, the voltage drop that occurs on several buses, especially bus 37 and 44, has a voltage drop of up to 95.03% to 95.31% with an average drop voltage of 95.1%, which means the average voltage drop. each feeder is ableto send electricity to consumers properly.

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15

Duran-Quintero, Michel, John E. Candelo, and Jose Soto-Ortiz. "A modified backward/forward sweep-based method for reconfiguration of unbalanced distribution networks." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (2019): 85–101. https://doi.org/10.11591/ijece.v9i1.pp85-101.

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A three-phase unbalanced power flow method can provide a more realistic scenario of how distribution networks operate. The backward/forward sweepbased power flow method (BF-PF) has been used for many years as an important computational tool to solve the power flow for unbalanced and radial power systems. However, some of the few available research tools produce many errors when they are used for network reconfiguration because the topology changes after multiple switch actions and the nodes are disorganized continually. This paper presents a modified BF-PF for threephase unbalanced radial distribution networks that is capable of arranging the system topology when reconfiguration changes the branch connections. A binary search is used to determine the connections between nodes, allowing the algorithm to avoid those problems when reconfiguration is carried out, regardless of node numbers. Tests are made to verify the usefulness of the proposed algorithm in both the IEEE 13-node test feeder and the 123-node test feeder, converging in every run where constraints are accomplished. This approach can be used easily for a large-scale feeder network reconfiguration. The full version of this modified backward/forward sweep algorithm is available for research at MathWorks.

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16

Wang, Xu Qiang, Yuan Zeng, Hao Ran Zhang, Guang Yi Liu, and Zhan Yong Yang. "An Interactive Distribution Network Reconfiguration Method in GridLAB-D/MATLAB Environment." Advanced Materials Research 756-759 (September 2013): 2260–64. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2260.

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Optimal operation of distribution network is widely concerned in recent years. As an important part of optimal operation, the reconfiguration of distribution network is a focus of research. This paper proposes an interactive reconfiguration algorithm of distribution network based on GridLAB-D and uses a typical feeder sample to illustrate the effectiveness of this algorithm.

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17

Tabatabaei, Sajad, and Taher Niknam. "Impact of wind generators on distribution feeder reconfiguration." Journal of Renewable and Sustainable Energy 4, no. 5 (2012): 053101. http://dx.doi.org/10.1063/1.4748282.

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18

Niknam, Taher, and Ehsan Azad Farsani. "A hybrid evolutionary algorithm for distribution feeder reconfiguration." Science China Technological Sciences 53, no. 4 (2010): 950–59. http://dx.doi.org/10.1007/s11431-010-0116-2.

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19

Niknam, Taher, Reza Khorshidi, and Bahman Bahmani Firouzi. "A hybrid evolutionary algorithm for distribution feeder reconfiguration." Sadhana 35, no. 2 (2010): 139–62. http://dx.doi.org/10.1007/s12046-010-0023-z.

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20

Roytelman, I., V. Melnik, S. S. H. Lee, and R. L. Lugtu. "Multi-objective feeder reconfiguration by distribution management system." IEEE Transactions on Power Systems 11, no. 2 (1996): 661–67. http://dx.doi.org/10.1109/59.496136.

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21

Su, C. T., C. S. Lee, and L. L. Huang. "Feeder reconfiguration employing simulated annealing for distribution systems." European Transactions on Electrical Power 11, no. 5 (2001): 341–47. http://dx.doi.org/10.1002/etep.4450110510.

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22

Ali, Machrus, Hidayatul Nurohmah, and Dwi Ajiatmo. "Rekonfigurasi Jaringan Distribusi Radial 65 Bus Berbasis Binary Particle Swarm Optimization (BPSO)." Jurnal JEETech 3, no. 1 (2022): 57–61. http://dx.doi.org/10.32492/jeetech.v3i1.3108.

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The configuration of a radial distribution network is difficult to simplify because it is very complex. This network reconfiguration is used to redesign the configuration of the radial distribution network by opening and closing switches on the distribution network. The feeder of Purwoasri, The feeder of Purwoasri, Rayon Kertosono has 65 buses which cause the Mojokerto area to have a very large loss so it needs to be reconfigured.. The resulting power flow will result in network power losses due to configuration. The reconfiguration process will be repeated until a configuration form that produces the smallest power losses is obtained. The number of feeders and buses on the network will be difficult if done manually and takes a very long time, so solving the problem must use a computer program. Network reconfiguration using the Matlab 2013a program will analyze its power flow using the Newton Raphson method and using the Binary Particle Swarm Optimization (BPSO) artificial intelligence method. Before reconfiguration, the network experienced losses of 1169,1374 kWatt after reconfiguration experienced losses of 635,7444 kWatt. The results of the reconfiguration can reduce losses of 635,74440 kWatt or 45,6228 % from the previous loss.

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23

Azizivahed, Ali, Hossein Narimani, Mehdi Fathi, Ehsan Naderi, Hamid Reza Safarpour, and Mohammad Rasoul Narimani. "Multi-objective dynamic distribution feeder reconfiguration in automated distribution systems." Energy 147 (March 2018): 896–914. http://dx.doi.org/10.1016/j.energy.2018.01.111.

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24

Arya Suardika, I. Putu, I. Gede Dyana Arjana, and Anak Agung Gede Maharta Pemayun. "Rekonfigurasi Saluran Distribusi 20 kV Untuk Mengurangi Rugi-Rugi Daya dan Jatuh Tegangan Pada Penyulang Abang." Jurnal SPEKTRUM 5, no. 2 (2018): 231. http://dx.doi.org/10.24843/spektrum.2018.v05.i02.p29.

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The feeder of Abang is one of forty-one feeders in State Electrical Company (PT PLN) of east Bali Area. This feeder uses a configuration of radial type that serves consumers in the area of Padangkerta, Bias, Batannyuh, Kikian, Ababi, Datah, Kubu, Nusu, Tianyar and Ban. It has a line transect that is 212,8 kms in leght with a total of distribution subtatations of 167 transformers and its peaks load reaches 5669 kW. This conditions results in drop voltage of 16,255% and and power losses of 10,12%. Feeder reconfiguration is accomplished by two ways: the cutting of the network that is diverted to Subagan feeder and the planning of Kubu feeder. Reconfiguration by using a method of cutting the network shows a voltage correction of Abang feeder with the initial drop voltage of 16,255% to 3.05% and the initial power losses of 10,12% to 3,49%. The improvement of voltage and power losses occurring in this feeder is not followed by Subagan feeder, which experienced an initial rise in the percentage of drops voltage of 6,78% to 27,615% and its initial power losses of 3,89% to 13,28%. While reconfiguration with Kubu's feeder planning is able to repair the drop voltage of Abang feeder to 2.94% and its power losses to 3,41%. Meanwhile, Kubu’s feeder in accordance with the results of running the program ETAP that the value of drop voltage is 2,95% and power losses is 2,73%.

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25

Gwachha, Rakesh, Tanus Bikram Malla, Yogesh Bhattarai, and Rupesh Gautam. "Distribution Network Reconfiguration Using Genetic Algorithm for Loss Reduction: A Case Study of Katunje Feeder, Bhaktapur." Journal of Science and Engineering 10 (December 31, 2023): 39–47. http://dx.doi.org/10.3126/jsce.v10i1.61018.

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The power distribution system has difficulties with regard to power loss and unacceptable voltage drops as a result of the rapidly expanding power system network, rising electrical energy consumption, and longer distances of power distribution. A typical strategy to address the issues with the distribution system is to perform distribution system reconfiguration. The study focuses on distribution feeder reconfiguration of the Katunje Feeder of Bhaktapur, Nepal where optimization problem is formulated to minimize the system active power loss and investment cost of the system. Genetic algorithm is employed in a co-simulation framework to solve the optimization problem where states of 26 different tie switches are to be altered to achieve the desired optimum results. Two cases are formulated: in case I active power loss is assigned more weight than investment cost whereas, in case II equal weights are assigned for active power loss and investment cost. The results showed the reduction in active power loss and investment cost for both the cases. Case I resulted in more active power loss reduction compared to case II, and case II resulted in more investment cost reduction compared to case I. From this, decision makers can obtain insights in adopting one of the cases for distribution feeder reconfiguration based on technical consideration (active power loss reduction) or economic consideration (investment cost reduction).

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26

Milani, Armin Ebrahimi, and Mahmood Reza Haghifam. "A Heuristic Approach for Multi Objective Distribution Feeder Reconfiguration." International Journal of Applied Evolutionary Computation 1, no. 2 (2010): 60–73. http://dx.doi.org/10.4018/jaec.2010040103.

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The reconfiguration is an operation process used for optimization with specific objectives by means of changing the status of switches in a distribution network. This paper presents an algorithm for network recon-figuration based on the heuristic rules and fuzzy multi objective approach where each objective is normalized with inspiration from fuzzy set to cause optimization more flexible and formulized as a unique multi objective function. Also, the genetic algorithm is used for solving the suggested model, in which there is no risk of non-linear objective functions and constraints. The effectiveness of the proposed method is demonstrated through several examples in this paper.

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27

Kavousi-Fard, Abdollah, and Taher Niknam. "Optimal Distribution Feeder Reconfiguration for Reliability Improvement Considering Uncertainty." IEEE Transactions on Power Delivery 29, no. 3 (2014): 1344–53. http://dx.doi.org/10.1109/tpwrd.2013.2292951.

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28

A.V. SUDHAKARA, REDDY, and REDDY M. DAMODAR. "APPLICATION OF WHALE OPTIMIZATION ALGORITHM FOR DISTRIBUTION FEEDER RECONFIGURATION." i-manager’s Journal on Electrical Engineering 11, no. 3 (2018): 17. http://dx.doi.org/10.26634/jee.11.3.14119.

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29

Taylor, T., and D. Lubkeman. "Implementation of heuristic search strategies for distribution feeder reconfiguration." IEEE Transactions on Power Delivery 5, no. 1 (1990): 239–46. http://dx.doi.org/10.1109/61.107279.

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30

Hsu, Y. Y., and J. H. Yi. "Planning of distribution feeder reconfiguration with protective device coordination." IEEE Transactions on Power Delivery 8, no. 3 (1993): 1340–47. http://dx.doi.org/10.1109/61.252660.

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31

Qin Zhou, D. Shirmohammadi, and W. H. E. Liu. "Distribution feeder reconfiguration for service restoration and load balancing." IEEE Transactions on Power Systems 12, no. 2 (1997): 724–29. http://dx.doi.org/10.1109/59.589664.

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32

Parlier, Guillaume, Hervé Guéguen, and Feihu Hu. "Smart brute-force approach for distribution feeder reconfiguration problem." Electric Power Systems Research 174 (September 2019): 105837. http://dx.doi.org/10.1016/j.epsr.2019.04.015.

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33

Nadi Arta, I. Gede, I. Nyoman Setiawan, and I. Wayan Arta Wijaya. "REKONFIGURASI JARINGAN DISTRIBUSI PADA PENYULANG RUMAH SAKIT BALI MED (RSBM)." Jurnal SPEKTRUM 6, no. 4 (2019): 43. http://dx.doi.org/10.24843/spektrum.2019.v06.i04.p7.

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RSBM feeder is one of the feeders sourced from transformer 2 of the Amlapura Substation. This feeder is a radial type 20 kV distribution network configuration with 133 distribution transformers with a total power of 5.428 kVA and a load of 144 amperes. The lowest voltage condition in RSBM feeders has reached 18,608 kV or a voltage drop of 6.96%. To overcome the voltage drop and power loss in RSBM feeders, it is necessary to reconfigure the network. Network reconfiguration is done by planning a new feeder (Rendang feeder). Analysis of power loss and voltage drop using the help of ETAP software. Based on studies that have been done, before the network reconfiguration is obtained the percentage of voltage drop is 6,98% and the power loss is 5,68%. After being reconfigured with a new feeder plan (Rendang feeder), it showed good results, ie the percentage of voltage drop dropped to 2.09% and the power loss dropped to 3.20%. Whereas the Rendang feeder showed good results with the percentage of voltage drop of 2.78% and the power loss of 3.47%. Based on load forecasting for the next 5 years on RSBM feeders the voltage drop and power loss are still according to the standard. Whereas the Rendang feeder occurs a voltage drop and a power loss that is not up to standard in 2023, namely a voltage drop of 5.20% and a power loss of 5.07%. So that for the Rendang feeder in 2023 it is necessary to break the load or reconfigure the feeder again.

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34

Liu, Long Hui, Yan Wang, Shu Jun Yao, Lu Yao Ma, and Jing Yang. "Distribution Network Reconfiguration with Distributed Generation Based on Cloud Genetic Algorithm." Advanced Materials Research 529 (June 2012): 306–10. http://dx.doi.org/10.4028/www.scientific.net/amr.529.306.

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According to the traditional distribution network reconfiguration, the fault feeder with distributed generation (DG) will separate from the distribution network immediately while the network goes wrong. In order to improve the system power supply reliability and the utilization rate of DG, the new standard allows the distribution network change into island operation. This paper establishes the mathematics model of the distribution network reconfiguration with DG, the objective function and constraint conditions. The traditional genetic algorithm (GA) has the shortcomings of premature convergence and slow convergence speed to solve this nonlinear optimization problem. This paper applies the cloud genetic algorithm (CGA) to solve the network reconfiguration problem. The Case study on IEEE33 test system shows that the algorithm is reasonable and effective.

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35

Ale, T. O., and I. O. Abumere. "Power Loss Minimisation on 11kV Distribution Network using Feeder Reconfiguration." European Journal of Engineering Research and Science 5, no. 1 (2020): 12–19. http://dx.doi.org/10.24018/ejers.2020.5.1.1333.

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The Distribution System can be seen as the networking line that interfaces between the large power generators and transmission lines on one hand and the electricity consumers on the other. The system can exist in different topologies among which, the radial form is popular because of its simplicity and comparative low cost of design. The problem of poor power quality and instability has been a major power systems challenge especially in Nigeria. More reliable and stable power systems among other benefits can be enjoyed when system are optimized. Hence, this paper implements the feeder reconfiguration technique as an optimization procedure to minimize the technical losses on the Ondo Road 11kV Feeder Segment. MATLAB is employed to simulate the optimization process and the results showed that an average of 85.47% active power loss reduction on all phases is obtainable with the proposed technique leading to an estimated financial saving of ₦639,999.75 per hour.

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36

Ale, T. O., and I. O. Abumere. "Power Loss Minimisation on 11kV Distribution Network using Feeder Reconfiguration." European Journal of Engineering and Technology Research 5, no. 1 (2020): 12–19. http://dx.doi.org/10.24018/ejeng.2020.5.1.1333.

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The Distribution System can be seen as the networking line that interfaces between the large power generators and transmission lines on one hand and the electricity consumers on the other. The system can exist in different topologies among which, the radial form is popular because of its simplicity and comparative low cost of design. The problem of poor power quality and instability has been a major power systems challenge especially in Nigeria. More reliable and stable power systems among other benefits can be enjoyed when system are optimized. Hence, this paper implements the feeder reconfiguration technique as an optimization procedure to minimize the technical losses on the Ondo Road 11kV Feeder Segment. MATLAB is employed to simulate the optimization process and the results showed that an average of 85.47% active power loss reduction on all phases is obtainable with the proposed technique leading to an estimated financial saving of ?639,999.75 per hour.

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Pondri, Ferdian, and Sukardi Sukardi. "Analysis Repair End Voltage and shrink Technical with Reconfiguration Network 20 KV distribution on Feeder PT PLN (Persero) Service Unit Silago Customer (ULP) Sitiung." Dinasti Information and Technology 1, no. 1 (2023): 10–16. http://dx.doi.org/10.38035/dit.v1i1.252.

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There is voltage drop at the tip feeder silago because length network feeder the resulted distribution Genre Power electricity to customer suboptimal . By Because That done analysis transfer burden network with method reconfigure network distribution voltage intermediate feeder the original syllabus is output from substation Connect (GH) Sungai Dareh Then moved to feeder river dareh that direct output from substation Main (GI) Lansek River with use ETAP application 19.01, so expected can repair voltage drop across the feeder sylago so that distribution Genre Power electricity to customer can be optimal.

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Boskabadi, Mahmoud, and Mahmoud Sadegh. "Reliable Distribution Feeder Reconfiguration Containing Distributed Generation Using Evolutionary Algorithm." Current Journal of Applied Science and Technology 25, no. 1 (2017): 1–8. http://dx.doi.org/10.9734/cjast/2017/38162.

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Terno, O. "ON THE LOCAL AND GLOBAL OPTIMUMS IN DISTRIBUTION FEEDER RECONFIGURATION." Oil Shale 22, no. 2S (2005): 171. http://dx.doi.org/10.3176/oil.2005.2s.10.

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Niknam, Taher, Mohsen Zare, Jamshid Aghaei, and Ehsan Azad Farsani. "A NEW HYBRID EVOLUTIONARY OPTIMIZATION ALGORITHM FOR DISTRIBUTION FEEDER RECONFIGURATION." Applied Artificial Intelligence 25, no. 10 (2011): 951–71. http://dx.doi.org/10.1080/08839514.2011.621288.

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Guan, Wanlin. "Distribution System Feeder Reconfiguration Considering Optimal Dispatching Of Distributed Generators." IOP Conference Series: Materials Science and Engineering 452 (December 13, 2018): 032061. http://dx.doi.org/10.1088/1757-899x/452/3/032061.

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Niknam, Taher, Abdollah Kavousi Fard, and Alireza Seifi. "Distribution feeder reconfiguration considering fuel cell/wind/photovoltaic power plants." Renewable Energy 37, no. 1 (2012): 213–25. http://dx.doi.org/10.1016/j.renene.2011.06.017.

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Niknam, Taher. "An efficient multi-objective HBMO algorithm for distribution feeder reconfiguration." Expert Systems with Applications 38, no. 3 (2011): 2878–87. http://dx.doi.org/10.1016/j.eswa.2010.08.081.

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Wagner, T. P., A. Y. Chikhani, and R. Hackam. "Feeder reconfiguration for loss reduction: an application of distribution automation." IEEE Transactions on Power Delivery 6, no. 4 (1991): 1922–33. http://dx.doi.org/10.1109/61.97741.

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Niknam, Taher. "A NEW HYBRID ALGORITHM FOR MULTI-OBJECTIVE DISTRIBUTION FEEDER RECONFIGURATION." Cybernetics and Systems 40, no. 6 (2009): 508–27. http://dx.doi.org/10.1080/01969720903068500.

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Niknam, Taher, and Mokhtar Sha Sadeghi. "An efficient evolutionary optimization algorithm for multiobjective distribution feeder reconfiguration." International Journal of Control, Automation and Systems 9, no. 1 (2011): 112–17. http://dx.doi.org/10.1007/s12555-011-0114-6.

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Kavousi-Fard, Abdollah, Taher Niknam, and Abbas khosravi. "Multi-objective probabilistic distribution feeder reconfiguration considering wind power plants." International Journal of Electrical Power & Energy Systems 55 (February 2014): 680–91. http://dx.doi.org/10.1016/j.ijepes.2013.10.028.

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Guan, Wanlin, Yanghong Tan, Haixia Zhang, and Jianli Song. "Distribution system feeder reconfiguration considering different model of DG sources." International Journal of Electrical Power & Energy Systems 68 (June 2015): 210–21. http://dx.doi.org/10.1016/j.ijepes.2014.12.023.

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Indra Wiguna, I. Gusti Nyoman, I. Gede Dyana Arjana, and Tjok Gede Indra P. "Analisa Rekonfigurasi Jaringan Distribusi 20 kV Pada Penyulang Berawa Untuk Menurunkan Losses dan Drop Tegangan Penyaluran Tenaga Listrik." Jurnal SPEKTRUM 6, no. 2 (2019): 67. http://dx.doi.org/10.24843/spektrum.2019.v06.i02.p10.

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Peak load on Berawa Feeder is very high exceeding the maximum limit set by PLN which causes Losses and high voltage drop values, so reconfiguration of Repeater via LBS Canggu Club and LBS Damai Residance is carried out, using 3 distribution points. The flow before being reconfigured in the Swamp Feeder was 242A to 180.2A, while the previous Bumbak Feeder was 82A to 144.7A. losses previous deliveries of Bumbak Feederwere 5.2kW to 22.15kW, while previous Berawa Berulang was 117.1kW to 55.11kW, drop the previous Bumbak Feeder voltagewas 102kVA to 24kVA, whereas the previous Swamp Feeder was 775.4kVA to 490.2kVA. Load surges and decreases are caused by changes in load and channel length after being reconfigured.

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Nuhanović, Amir, Jasna Hivziefendić, and Amir Hadžimehmedović. "Distribution Network Reconfiguration Considering Power Losses and Outages Costs Using Genetic Algorithm." Journal of Electrical Engineering 64, no. 5 (2013): 265–71. http://dx.doi.org/10.2478/jee-2013-0039.

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Abstract This paper discusses the problem of finding the optimal network topological configuration by changing the feeder status. The reconfiguration problem is considered as a multiobjective problem aiming to minimize power losses and total interruptions costs subject to the system constraints: the network radiality voltage limits and feeder capability limits. Due to its complexity, the metaheuristic methods can be applied to solve the problem and often the choice is genetic algorithm. NSGA II is used to solve the multiobjective optimization problem in order to get Pareto optimal set with possible solutions. The proposed method has been tested on real 35 kV distribution network. The numerical results are presented to illustrate the feasibility of the proposed genetic algorithm.

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