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Journal articles on the topic 'Active power loss'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Cheng, Ying Ying, Feng Zhou, and Ji Xiao. "Analysis of Active Power Loss for Reactive Power Compensation Devices." Advanced Materials Research 986-987 (July 2014): 1638–42. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.1638.

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Analyzing the active power loss of reactive power compensation device under normal reactive power output status and its influence factors and metering the active power loss of the reactor in the 500kV substation and the capacitor in the 220kV substation, concluding that the active power meter had the influencing factors of forward and reverse active and putting forward the assessment requirements for the running reactive power compensation device by analyzing the site calibration data of metering device.

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2

Lenin, K. "ACTIVE POWER LOSS REDUCTION BY FIREFLY ALGORITHM." International Journal of Research -GRANTHAALAYAH 6, no. 3 (2018): 155–65. http://dx.doi.org/10.29121/granthaalayah.v6.i3.2018.1509.

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This paper proposes a swarm intelligence algorithm, called Firefly Mating Algorithm (FMA) for solving optimal reactive power problem. FMA uses genetic algorithm as the core of the algorithm. The main feature of the algorithm is a novel mating pair selection method which is inspired by the following two mating behaviours of fireflies in nature: (i) the mutual attraction between males and females causes them to mate and (ii) fireflies of both sexes are of the multiple-mating type, mating with multiple opposite sex partners. A female continues mating until her spermatheca becomes full, and, in th

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3

Lenin, K. "ACTIVE POWER LOSS REDUCTION BY SYNTHESIZED ALGORITHM." International Journal of Research -GRANTHAALAYAH 6, no. 5 (2018): 149–56. http://dx.doi.org/10.29121/granthaalayah.v6.i5.2018.1436.

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In this paper, Synthesized Algorithm (SA) proposed to solve the optimal reactive power problem. Proposed Synthesized Algorithm (SA) is a combination of three well known evolutionary algorithms, namely Differential Evolution (DE) algorithm, Particle Swarm Optimization (PSO) algorithm, and Harmony Search (HS) algorithm. It merges the general operators of each algorithm recursively. This achieves both good exploration and exploitation in SA without altering their individual properties. In order to evaluate the performance of the proposed SA, it has been tested in Standard IEEE 57,118 bus systems

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4

Lenin, K. "ACTIVE POWER LOSS REDUCTION BY ASSORTED ALGORITHMS." International Journal of Research -GRANTHAALAYAH 6, no. 5 (2018): 263–75. http://dx.doi.org/10.29121/granthaalayah.v6.i5.2018.1448.

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This paper presents assorted algorithms for solving optimal reactive power problem. Symbiosis modeling (SM), which extends the dynamics of the canonical PSO algorithm by adding a significant ingredient that takes into account the symbiotic co evolution between species, Hybridization of Evolutionary algorithm with Conventional Algorithm (HCA) that uses the abilities of evolutionary and conventional algorithm and Genetical Swarm Optimization (GS), which combines Genetic Algorithms (GA) and Particle Swarm Optimization (PSO).All the above said SM, HCA,GS algorithms are used to augment the converge

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5

Kanagasabai, Lenin. "Integrated Algorithm for Decreasing Active Power Loss." IAES International Journal of Artificial Intelligence (IJ-AI) 7, no. 1 (2018): 33. http://dx.doi.org/10.11591/ijai.v7.i1.pp33-41.

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This paper projects an Integrated Algorithm (IA) for solving optimal reactive power problem. Quick convergence of the Cuckoo Search (CS), the vibrant root change of the Firefly Algorithm (FA), and the incessant position modernization of the Particle Swarm Optimization (PSO) has been combined to form the Integrated Algorithm (IA). In order to evaluate the efficiency of the proposed Integrated Algorithm (IA), it has been tested in standard IEEE 57,118 bus systems and compared to other standard reported algorithms. Simulation results show that Integrated Algorithm (IA) is considerably reduced the

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6

Dr.K.Lenin. "ACTIVE POWER LOSS REDUCTION BY FIREFLY ALGORITHM." International Journal of Research - Granthaalayah 6, no. 3 (2018): 155–65. https://doi.org/10.5281/zenodo.1213570.

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Abstract:

This paper proposes a swarm intelligence algorithm, called Firefly Mating Algorithm (FMA) for solving optimal reactive power problem. FMA uses genetic algorithm as the core of the algorithm. The main feature of the algorithm is a novel mating pair selection method which is inspired by the following two mating behaviours of fireflies in nature: (i) the mutual attraction between males and females causes them to mate and (ii) fireflies of both sexes are of the multiple-mating type, mating with multiple opposite sex partners. A female continues mating until her spermatheca becomes full, and, in th

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7

Dr.K.Lenin, *1. "ACTIVE POWER LOSS REDUCTION BY ASSORTED ALGORITHMS." International Journal of Research - Granthaalayah 6, no. 5 (2018): 263–75. https://doi.org/10.5281/zenodo.1270459.

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Abstract:

This paper presents assorted algorithms for solving optimal reactive power problem. Symbiosis modeling (SM), which extends the dynamics of the canonical PSO algorithm by adding a significant ingredient that takes into account the symbiotic co evolution between species, Hybridization of Evolutionary algorithm with Conventional Algorithm (HCA) that uses the abilities of evolutionary and conventional algorithm and Genetical Swarm Optimization (GS), which combines Genetic Algorithms (GA) and Particle Swarm Optimization (PSO).All the above said SM, HCA,GS algorithms are used to augment the converge

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8

Dr.K.Lenin, *1. "ACTIVE POWER LOSS REDUCTION BY SYNTHESIZED ALGORITHM." International Journal of Research - Granthaalayah 6, no. 5 (2018): 149–56. https://doi.org/10.5281/zenodo.1255264.

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Abstract:

In this paper, Synthesized Algorithm (SA) proposed to solve the optimal reactive power problem. Proposed Synthesized Algorithm (SA) is a combination of three well known evolutionary algorithms, namely Differential Evolution (DE) algorithm, Particle Swarm Optimization (PSO) algorithm, and Harmony Search (HS) algorithm. It merges the general operators of each algorithm recursively. This achieves both good exploration and exploitation in SA without altering their individual properties. In order to evaluate the performance of the proposed SA, it has been tested in Standard IEEE 57,118 bus systems

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9

Dr.K.Lenin. "ACTIVE POWER LOSS REDUCTION BY INNOVATIVE ALGORITHMS." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 5, no. 8 (2018): 131–38. https://doi.org/10.5281/zenodo.1343476.

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This paper presents Attraction & Repulsion Particle Swarm Optimization (APSO), Random Virus Algorithm (RVA) for solving optimal Reactive Power Optimization problem. The modified model uses a diversity measure to have the algorithm alternate between exploring and exploiting behavior. In this work two phases are introduced i.e., attraction and <em>repulsion</em>. By measuring the diversity we let the swarm alternate between these phases. As long as the diversity is above a certain threshold <em>d<sub>low </sub></em>the particles attract each other. When the diversity declines below <em>d<sub

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10

K., Lenin. "Integrated Algorithm for Decreasing Active Power Loss." International Journal of Artificial Intelligence (IJ-AI) 7, no. 1 (2017): 33–41. https://doi.org/10.11591/ijai.v7.i1.pp33-41.

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Abstract:

This paper projects an Integrated Algorithm (IA) for solving optimal reactive power problem. Quick convergence of the Cuckoo Search (CS), the vibrant root change of the Firefly Algorithm (FA), and the incessant position modernization of the Particle Swarm Optimization (PSO) has been combined to form the Integrated Algorithm (IA). In order to evaluate the efficiency of the proposed Integrated Algorithm (IA), it has been tested in standard IEEE 57,118 bus systems and compared to other standard reported algorithms. Simulation results show that Integrated Algorithm (IA) is considerably reduced the

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11

Prakash, Kumar Hota, and Prasad Naik Atulya. "A New Methodology for Active Power Transmission Loss Allocation in Deregulated Power System." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 4 (2017): 1725–27. https://doi.org/10.11591/ijece.v7i4.pp1725-1737.

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This paper presents a new method for transmission loss allocation in a deregulated power system. As the power loss is a nonlinear quantity, so to allocate the loss in a common transmission corrider is a difficult task. It allocates transmission losses to loads based on the actual power flow in the lossy lines due to the concerned load. Each lossy line is subdivided into as many sub-lines as corresponding to the numbers of load attached to it. The tracing of power flow through each sub-line is worked out by using proportional sharing method. The power loss in each lossy line is equal with the t

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12

Dasman, Dasman. "Analysis of Power Loss of 20 kV Power Distribution." MATEC Web of Conferences 215 (2018): 01040. http://dx.doi.org/10.1051/matecconf/201821501040.

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In the distribution of electrical energy from the plant to the consumer, there is a decrease in quality due to the loss of power (losses). These power losses are caused by a voltage drop across the line and subsequently producing a power loss on the line. This power loss can be classified into two types based on its line parameters, i.e., active power loss and reactive power loss. The line’s active power loss generates losses of power/losses so that the active power reaches the load on the receiving end is always less than the productive power of the sender side. Power losses in the electrical

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13

Ambika, Prasad Hota, Mishra Sivkumar, Prasad Mishra Debani, and Reddy Salkuti Surender. "Allocating active power loss with network reconfiguration in electrical power distribution systems." International Journal of Power Electronics and Drive System (IJPEDS) 12, no. 1 (2021): 130–38. https://doi.org/10.11591/ijpeds.v12.i1.pp130-138.

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This paper presents a branch exchange (BE) based heuristic network reconfiguration technique where, the proposed bus classification strategy remodels dynamically as per the modified topology in order to provide a reconfigured network with minimum loss. Further, for fair allocation of the active power losses, it develops a new active power loss allocation (APLA) technique which eradicates the influence of cross-term analytically from loss formulation without any assumptions and approximations. The effectiveness of the proposed procedure has been investigated against other established methods us

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14

Dr.K.Lenin. "ACTIVE POWER LOSS REDUCTION BY FLOWER POLLINATION ALGORITHM." International Journal of Research - Granthaalayah 5, no. 12 (2017): 223–31. https://doi.org/10.5281/zenodo.1134563.

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This paper presents Flower Pollination (FP) algorithm for solving the optimal reactive power problem. Minimization of real power loss is taken as key intent. Flower pollination algorithm is a new nature-inspired algorithm, based on the characteristics of flowering plants. The biological evolution point of view, the objective of the flower pollination is the survival of the fittest and the optimal reproduction of plants in terms of numbers as well as the largely fittest. In order to evaluate the performance of the proposed Flower Pollination (FP) algorithm, it has been tested on IEEE 57 bus sys

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15

Lenin, K. "SUPERLATIVE ALGORITHM FOR REDUCTION OF ACTIVE POWER LOSS." International Journal of Research -GRANTHAALAYAH 5, no. 10 (2017): 101–11. http://dx.doi.org/10.29121/granthaalayah.v5.i10.2017.2287.

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This paper proposes Spinner Dolphin Algorithm (SDA) for solving optimal reactive power problem. Echolocation is the genetic sonar used by Spinner dolphin & it used by few kinds of other animals for direction-finding, hunting in diverse environments. This ability of Spinner dolphin is imitated in this paper to develop a new-fangled procedure for solving optimal reactive power problem. Spinner Dolphin Algorithm (SDA) takes reward of the overriding rules and outperforms many vigorous optimization methods. The new-fangled approach SDA leads to exceptional results with small computational effor

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16

Lenin, K. "ENRICHED CUCKOO ALGORITHM FOR ACTIVE POWER LOSS REDUCTION." International Journal of Research -GRANTHAALAYAH 5, no. 11 (2017): 307–15. http://dx.doi.org/10.29121/granthaalayah.v5.i11.2017.2357.

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This paper proposes Group Competition (GC) algorithm for solving optimal reactive power problem. Group Competition (GC) algorithm stimulated from the contest of sport teams in a sport group. A number of individuals as sport teams contend in a simulated group for numerous weeks. Based on the group schedule in every week, teams play in pairs and their game result is determined in terms of win or loss, given known the playing strength along with the teams’ planned formations. Modeling an artificial match analysis, each team devises a new playing strategy for the subsequent week competition and th

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17

Lenin, K. "ACTIVE POWER LOSS REDUCTION BY FLOWER POLLINATION ALGORITHM." International Journal of Research -GRANTHAALAYAH 5, no. 12 (2020): 223–31. http://dx.doi.org/10.29121/granthaalayah.v5.i12.2017.497.

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Abstract:

This paper presents Flower Pollination (FP) algorithm for solving the optimal reactive power problem. Minimization of real power loss is taken as key intent. Flower pollination algorithm is a new nature-inspired algorithm, based on the characteristics of flowering plants. The biological evolution point of view, the objective of the flower pollination is the survival of the fittest and the optimal reproduction of plants in terms of numbers as well as the largely fittest. In order to evaluate the performance of the proposed Flower Pollination (FP) algorithm, it has been tested on IEEE 57 bus sys

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18

Lenin, K. "ENHANCED ALGORITHM FOR REDUCTION OF ACTIVE POWER LOSS." International Journal of Research -GRANTHAALAYAH 6, no. 7 (2018): 45–51. http://dx.doi.org/10.29121/granthaalayah.v6.i7.2018.1282.

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In this paper, Enhanced Aggressive Weed Optimization (EWO) algorithm is applied to solve the optimal reactive power Problem. Aggressive Weed Optimization is a stochastic search algorithm that imitate natural deeds of weeds in colonize and detection of appropriate place for growth and reproduction. Enhanced Aggressive Weed Optimization (EWO) algorithm is based on hybridization of genetic algorithm with weed optimization algorithm which refers combination of crossover and mutation of genetic algorithm, and by the use of the cross factor new species are arisen. Proposed Enhanced Aggressive Weed O

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19

Dr.K.Lenin. "SUPERLATIVE ALGORITHM FOR REDUCTION OF ACTIVE POWER LOSS." International Journal of Research - Granthaalayah 5, no. 10 (2017): 101–11. https://doi.org/10.5281/zenodo.1039633.

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Abstract:

This paper proposes Spinner Dolphin Algorithm (SDA) for solving optimal reactive power problem. Echolocation is the genetic sonar used by Spinner dolphin & it used by few kinds of other animals for direction-finding, hunting in diverse environments. This ability of Spinner dolphin is imitated in this paper to develop a new-fangled procedure for solving optimal reactive power problem. Spinner Dolphin Algorithm (SDA) takes reward of the overriding rules and outperforms many vigorous optimization methods. The new-fangled approach SDA leads to exceptional results with small computational effor

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20

Dr.K.Lenin. "ENRICHED CUCKOO ALGORITHM FOR ACTIVE POWER LOSS REDUCTION." International Journal of Research - Granthaalayah 5, no. 11 (2017): 295–303. https://doi.org/10.5281/zenodo.1098397.

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Abstract:

This paper proposes Group Competition (GC) algorithm for solving optimal reactive power problem. Group Competition (GC) algorithm stimulated from the contest of sport teams in a sport group. A number of individuals as sport teams contend in a simulated group for numerous weeks. Based on the group schedule in every week, teams play in pairs and their game result is determined in terms of win or loss, given known the playing strength along with the teams’ planned formations. Modeling an artificial match analysis, each team devises a new playing strategy for the subsequent week competition

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21

Dr., K. Lenin. "ENHANCED ALGORITHM FOR REDUCTION OF ACTIVE POWER LOSS." International Journal of Research - Granthaalayah 6, no. 7 (2018): 45–51. https://doi.org/10.5281/zenodo.1322997.

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Abstract:

In this paper, Enhanced Aggressive Weed Optimization (EWO) algorithm is applied to solve the optimal reactive power Problem. Aggressive Weed Optimization is a stochastic search algorithm that imitate natural deeds of weeds in colonize and detection of appropriate place for growth and reproduction. Enhanced Aggressive Weed Optimization (EWO) algorithm is based on hybridization of genetic algorithm with weed optimization algorithm which refers combination of crossover and mutation of genetic algorithm, and by the use of the cross factor new species are arisen. Proposed Enhanced Aggressive Weed O

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22

Salgado, R. S., C. F. Moyano, and A. D. R. Medeiros. "Reviewing strategies for active power transmission loss allocation in power pools." International Journal of Electrical Power & Energy Systems 26, no. 2 (2004): 81–90. http://dx.doi.org/10.1016/j.ijepes.2003.07.001.

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23

Du, Xiao, Yigong Zhang, Zhiqiang Dai, Huan Liu, Siming Wei, and Jin Liu. "Power Distribution Strategy Considering Active Power Loss for DFIGs Wind Farm." Journal of Power and Energy Engineering 02, no. 04 (2014): 213–19. http://dx.doi.org/10.4236/jpee.2014.24030.

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24

Deshmukh, Nachiketa, Siva Prabhakar, and Sandeep Anand. "Power Loss Reduction in Buck Converter Based Active Power Decoupling Circuit." IEEE Transactions on Power Electronics 36, no. 4 (2021): 4316–25. http://dx.doi.org/10.1109/tpel.2020.3024721.

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25

Lenin, Kanagasabai. "Air cloud algorithm for diminution of active power loss." International Journal of Robotics and Automation (IJRA) 9, no. 3 (2020): 190–95. https://doi.org/10.11591/ijra.v9i3.pp190-195.

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In this work, air cloud (AC) algorithm is used to solve the optimal reactive power problem. Clouds shape in numerous ways. Convective clouds are created when moist air is warmed and expand into floating. Air raises haulage water vapour and within it expands and gets cooled as it goes. As the temperature and pressure of the air diminish, its saturation point – the equilibrium level of evaporation and condensation – is reduced. Every x is one cloud droplet, and qualitative characteristic of one cloud is explained by the three digital character (Ex,

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26

L.I., Kovernikova, and Bui Ngoc Hung. "Estimation of Active Power Loss Due to Supraharmonics in a 22 kV Transmission Line." Energy Systems Research 7, no. 3(27) (2024): 14–21. http://dx.doi.org/10.25729/esr.2024.03.0002.

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Power systems in Vietnam's coal mining industrial areas supply power of inferior quality. Supraharmonic voltages and currents in power grids serving electric energy to open-cut coal mines were detected by measuring power quality indicators and operating parameters in the power grid serving electric energy to the open-pit coal mine and coal screening plant operated by "Cua Ong-Vinacomin" company. The active powers of harmonics and supraharmonics do not produce any useful work. They induce extra loss of active power. Coal mining and coal processing companies pay for the loss, which results in di

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27

Hota, Ambika Prasad, Sivkumar Mishra, Debani Prasad Mishra, and Surender Reddy Salkuti. "Allocating active power loss with network reconfiguration in electrical power distribution systems." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 1 (2021): 130. http://dx.doi.org/10.11591/ijpeds.v12.i1.pp130-138.

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This paper presents a branch exchange (BE) based heuristic network reconfiguration technique where, the proposed bus classification strategy remodels dynamically as per the modified topology in order to provide a reconfigured network with minimum loss. Further, for fair allocation of the active power losses, it develops a new active power loss allocation (APLA) technique which eradicates the influence of cross-term analytically from loss formulation without any assumptions and approximations. The effectiveness of the proposed procedure has been investigated against other established methods us

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28

Dr.K.Lenin. "REDUCTION OF ACTIVE POWER LOSS BY GROUP COMPETITION ALGORITHM." International Journal of Research - Granthaalayah 5, no. 11 (2017): 260–70. https://doi.org/10.5281/zenodo.1098363.

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This paper proposes Group Competition (GC) algorithm for solving optimal reactive power problem. Group Competition (GC) algorithm stimulated from the contest of sport teams in a sport group. A number of individuals as sport teams contend in a simulated group for numerous weeks. Based on the group schedule in every week, teams play in pairs and their game result is determined in terms of win or loss, given known the playing strength along with the teams’ planned formations. Modeling an artificial match analysis, each team devises a new playing strategy for the subsequent week competition

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29

Dr., K. Lenin. "REDUCTION OF ACTIVE POWER LOSS BY COYOTE SEARCH ALGORITHM." International Journal of Research - Granthaalayah 6, no. 10 (2018): 130–38. https://doi.org/10.5281/zenodo.1476661.

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This paper presents Coyote Search Algorithm (CSA) for solving optimal reactive power problem. Coyote Search Algorithm is a new bio – inspired heuristic algorithm which based on coyote preying behaviour. The way coyote search for food and survive by avoiding their enemies has been imitated to formulate the algorithm for solving the reactive power problem. And the specialty of coyote is possessing both individual local searching ability & autonomous flocking movement and this special property has been utilized to formulate the search algorithm. The proposed Coyote Search Algorithm (CSA

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30

Hota, Prakash Kumar, and Atulya Prasad Naik. "A New Methodology for Active Power Transmission Loss Allocation in Deregulated Power System." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 4 (2017): 1725. http://dx.doi.org/10.11591/ijece.v7i4.pp1725-1737.

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Abstract:

This paper presents a new method for transmission loss allocation in a deregulated power system. As the power loss is a nonlinear quantity, so to allocate the loss in a common transmission corrider is a difficult task. It allocates transmission losses to loads based on the actual power flow in the lossy lines due to the concerned load. Each lossy line is subdivided into as many sub-lines as corresponding to the numbers of load attached to it. The tracing of power flow through each sub-line is worked out by using proportional sharing method. The power loss in each lossy line is equal with the t

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31

Deng, Guohao, Dang Wang, and Weixin Gao. "Active and Reactive Power Coordination Optimization of the Active Distribution Network." Journal of Physics: Conference Series 2450, no. 1 (2023): 012023. http://dx.doi.org/10.1088/1742-6596/2450/1/012023.

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Abstract The operation and control of the active distribution network are faced with great challenges due to a mass of tunable and controllable devices connected to the network, resulting in large active power loss and voltage deviation. In this paper, a method of active and reactive power coordination optimization for the active distribution network based on a one-dimensional convolutional neural network (1D-CNN) is proposed. This method can mine valuable information from the historical data of distribution networks, and use one-dimensional convolutional neural networks to map the complex non

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32

Lenin, K. "REDUCTION OF ACTIVE POWER LOSS BY PIONEERING POLL ALGORITHM." International Journal of Research -GRANTHAALAYAH 5, no. 11 (2017): 139–48. http://dx.doi.org/10.29121/granthaalayah.v5.i11.2017.2337.

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This paper projects Pioneering Poll (PP) algorithm, which inspired by poll around the world is used to solve optimal reactive power problem. In Pioneering Poll (PP) algorithm population is general people and each person may be a candidate or a voter. Definite number of people will form dissimilar groups to set up political parties in the solution space. Advertising movement is the fundamental of this Pioneering Poll (PP) algorithm and it contains three core phases: sanguine campaign, disparate campaign and union campaign. During sanguine campaign, the nominee proclaims themselves through accen

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33

Lenin, K. "REDUCTION OF ACTIVE POWER LOSS BY GROUP COMPETITION ALGORITHM." International Journal of Research -GRANTHAALAYAH 5, no. 11 (2017): 260–70. http://dx.doi.org/10.29121/granthaalayah.v5.i11.2017.2352.

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Abstract:

This paper proposes Group Competition (GC) algorithm for solving optimal reactive power problem. Group Competition (GC) algorithm stimulated from the contest of sport teams in a sport group. A number of individuals as sport teams contend in a simulated group for numerous weeks. Based on the group schedule in every week, teams play in pairs and their game result is determined in terms of win or loss, given known the playing strength along with the teams’ planned formations. Modeling an artificial match analysis, each team devises a new playing strategy for the subsequent week competition and th

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34

Lenin, K. "REDUCTION OF ACTIVE POWER LOSS BY COYOTE SEARCH ALGORITHM." International Journal of Research -GRANTHAALAYAH 6, no. 10 (2018): 130–38. http://dx.doi.org/10.29121/granthaalayah.v6.i10.2018.1170.

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Abstract:

This paper presents Coyote Search Algorithm (CSA) for solving optimal reactive power problem. Coyote Search Algorithm is a new bio – inspired heuristic algorithm which based on coyote preying behaviour. The way coyote search for food and survive by avoiding their enemies has been imitated to formulate the algorithm for solving the reactive power problem. And the specialty of coyote is possessing both individual local searching ability & autonomous flocking movement and this special property has been utilized to formulate the search algorithm. The proposed Coyote Search Algorithm (CSA) has

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35

Lenin, K. "ACTIVE POWER LOSS REDUCTION BY AMPLIFIED ANT COLONY ALGORITHM." International Journal of Research -GRANTHAALAYAH 6, no. 7 (2018): 132–41. http://dx.doi.org/10.29121/granthaalayah.v6.i7.2018.1291.

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In this paper, Amplified Ant Colony (AAC) algorithm has been proposed for solving optimal reactive power problem. Mutation of Genetic algorithm (GA) is used in Ant Colony Algorithm (ACA) and the output of the GA is given as an input to the ACA. The proposed Amplified Ant Colony (AAC) algorithm has been tested on standard IEEE 118 & practical 191 bus test systems and simulation results show clearly the superior performance of the proposed Amplified Ant Colony (AAC) algorithm in reducing the real power loss & voltage profiles are within the limits.

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36

Lenin, Kanagasabai. "Active Power Loss Reduction by Particle Swarm Optimization Algorithm." International Journal of Computer Sciences and Engineering 7, no. 1 (2019): 904–6. http://dx.doi.org/10.26438/ijcse/v7i1.904906.

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37

Lenin, Kanagasabai. "Active Power Loss Reduction by Improved Gravitational Search Algorithm." International Journal of Darshan Institute on Engineering Research & Emerging Technology 8, no. 2 (2020): 22. http://dx.doi.org/10.32692/ijdi-eret/8.2.2019.1904.

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38

Fetih, N. H., and H. M. El-Shewy. "Induction Motor Optimum Design, Including Active Power Loss Effect." IEEE Transactions on Energy Conversion EC-1, no. 3 (1986): 155–60. http://dx.doi.org/10.1109/tec.1986.4765749.

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39

Lenin, Kanagasabai. "Enhanced whale optimization algorithm for active power loss diminution." International Journal of Informatics and Communication Technology (IJ-ICT) 9, no. 1 (2020): 19. http://dx.doi.org/10.11591/ijict.v9i1.pp19-23.

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In this paper Enhanced whale Optimization Algorithm (EWO) proposed to solve the optimal reactive power problem. Whale optimization algorithm is modeled by Bubble-net hunting tactic. In the projected optimization algorithm an inertia weight ω ∈ [1, 0] has been introduced to perk up the search ability. Whales are commonly moving 10-16 meters down then through the bubbles which are created artificially then they encircle the prey and move upward towards the surface of sea. Proposed Enhanced whale optimization algorithm (EWO) is tested in standard IEEE 57 bus systems and power loss reduced conside

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Lenin, Kanagasabai. "Air cloud algorithm for diminution of active power loss." IAES International Journal of Robotics and Automation (IJRA) 9, no. 3 (2020): 190. http://dx.doi.org/10.11591/ijra.v9i3.pp190-195.

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<p>In this work, air cloud (AC) algorithm is used to solve the optimal reactive power problem. Clouds shape in numerous ways. Convective clouds are created when moist air is warmed and expand into floating. Air raises haulage water vapour and within it expands and gets cooled as it goes. As the temperature and pressure of the air diminish, its saturation point – the equilibrium level of evaporation and condensation – is reduced. Every x is one cloud droplet, and qualitative characteristic of one cloud is explained by the three digital character (Ex, En, He), droplets number n, where Ex (

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Fetih, N. H., and H. M. El-Shewy. "Induction Motor Optimum Design, Including Active Power Loss Effect." IEEE Power Engineering Review PER-6, no. 9 (1986): 40. http://dx.doi.org/10.1109/mper.1986.5527668.

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Dr.K.Lenin. "REDUCTION OF ACTIVE POWER LOSS BY PIONEERING POLL ALGORITHM." International Journal of Research - Granthaalayah 5, no. 11 (2017): 139–48. https://doi.org/10.5281/zenodo.1069406.

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This paper projects Pioneering Poll (PP) algorithm, which inspired by poll around the world is used to solve optimal reactive power problem. In Pioneering Poll (PP) algorithm population is general people and each person may be a candidate or a voter. Definite number of people will form dissimilar groups to set up political parties in the solution space. Advertising movement is the fundamental of this Pioneering Poll (PP) algorithm and it contains three core phases: sanguine campaign, disparate campaign and union campaign. During sanguine campaign, the nominee proclaims themselves through accen

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Dr.K., Lenin *. "ACTIVE POWER LOSS REDUCTION BY AMPLIFIED ANT COLONY ALGORITHM." International Journal of Research - Granthaalayah 6, no. 7 (2018): 132–41. https://doi.org/10.5281/zenodo.1323035.

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In this paper, Amplified Ant Colony (AAC) algorithm has been proposed for solving optimal reactive power problem. Mutation of Genetic algorithm (GA) is used in Ant Colony Algorithm (ACA) and the output of the GA is given as an input to the ACA. The proposed Amplified Ant Colony (AAC) algorithm has been tested on standard IEEE 118 & practical 191 bus test systems and simulation results show clearly the superior performance of the proposed Amplified Ant Colony (AAC) algorithm in reducing the real power loss & voltage profiles are within the limits.

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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

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Dr.K.Lenin. "ENHANCED SEEKER OPTIMIZATION ALGORITHM FOR REDUCTION OF ACTIVE POWER LOSS." International Journal of Research - Granthaalayah 5, no. 10 (2017): 336–47. https://doi.org/10.5281/zenodo.1045996.

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This paper projects Enhanced Seeker Optimization (ESO) algorithm for solving optimal reactive power problem. Seeker optimization algorithm (SOA) models the deeds of human search population based on their memory, experience, uncertainty reasoning and communication with each other. In Artificial Bee Colony (ABC) algorithm the colony consists of three groups of bees: employed bees, onlookers and scouts. All bees that are presently exploiting a food source are known as employed bees. The number of the employed bees is equal to the number of food sources and an employed bee is allocated to one of t

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Haider, Waseem, Seema Batool, Federica Milazzo, and Quang P. Ha. "Machine Learning-Based Forecasting Active Power Loss in Distribution Systems." E3S Web of Conferences 626 (2025): 04003. https://doi.org/10.1051/e3sconf/202562604003.

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This paper presents an ensemble learning approach to predict the active power losses during the allocation and sizing of distributed generation (DG) units in power distribution networks. The forecast model incorporates the Gradient Boosting Machine Regression (GBMR) to estimate DG location, bus voltages, DG size, and active losses without conventional power flow calculations. The results demonstrate that the suggested estimations of power losses and DG sizing are effective, practical, and adaptable for power system management. The accuracy of the proposed model has been validated using key per

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Chang, Xue Fei, Xiang Yu Lv, and De Xin Li. "Active Power Optimization Considering both Generation Cost and Network Loss." Applied Mechanics and Materials 556-562 (May 2014): 1643–46. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.1643.

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In order to improve the calculation efficiency, active power and reactive power are usually optimized separately in optimal power flow considering the decoupling characteristic. However, this would decrease the economy performance of power system. This paper proposed a weighting factor to formulate a multi-objective model, combining the generation cost and system network loss together. The optimization problem is performed using genetic algorithms and quadratic programming respectively. Finally, the feasibility and efficiency of the proposed model are verified with the IEEE 14 Bus test system.

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Dr.K., Lenin. "REDUCTION OF ACTIVE POWER LOSS BY IMPROVED TABU SEARCH ALGORITHM." International Journal of Research - Granthaalayah 6, no. 7 (2018): 1–9. https://doi.org/10.5281/zenodo.1321287.

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In this paper, an Improved Tabu Search (ITS) algorithm has been proposed to solve the optimal reactive power problem. In this work Tabu Search- has been hybridized with Simulated Annealing algorithm to solve the optimal reactive power problem. Hybridization of these two algorithms improves the exploration & exploitation capabilities during the search. Proposed Improved Tabu Search (ITS) algorithm has been tested in Standard IEEE 57,118 bus systems & real power loss has been comparatively reduced with voltage profiles are within the limits.

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Wei, Xin, Xiao Yang, Lianrong Pan, Hang Ruan, and Daiyu Xie. "Coordinated Optimal Active-Reactive Scheduling of Active Distribution Networks Considering Demand Response." Journal of Physics: Conference Series 2979, no. 1 (2025): 012017. https://doi.org/10.1088/1742-6596/2979/1/012017.

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Abstract The constraints in the active - reactive power coordinated optimal scheduling of the conventional active distribution network are single - faceted, the scheduling efficiency is low, resulting in an increase in the final network loss ratio. Therefore, a method for designing and analyzing the active - reactive power coordinated optimal scheduling in the active distribution network with consideration of demand response is proposed. In light of the existing scheduling requirements, an analysis of the active - reactive power scheduling requirements is initially conducted, and the adaptive

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Karymov, R. R., B. V. Oleksyuk, and D. Kh Safiullin. "Active power loss in reactive power sources based on magnetically controlled shunt reactors." Russian Electrical Engineering 83, no. 10 (2012): 536–38. http://dx.doi.org/10.3103/s1068371212100057.

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