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Relevant bibliographies by topics / Power Loss Minimization and GTO Algorithm

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Academic literature on the topic 'Power Loss Minimization and GTO Algorithm'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Power Loss Minimization and GTO Algorithm"

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Ram, Prasad Kannemadugu, Adhimoorthy V, and Lakshmi Devi A. "Optimal Allocation of Combined DG and DSTATCOM for Enhancement of Voltage Stability in Radial Distribution Networks." Indian Journal of Science and Technology 16, no. 35 (2023): 2845–55. https://doi.org/10.17485/IJST/v16i35.1042.

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Abstract <strong>Objectives:</strong> To improve the voltage profile, minimize the network power loss and enhance the stability of the Redial Distribution Systems (RDS). <strong>Methods:</strong> A novel and successful meta-heuristic method of Group Teaching Optimization (GTO) algorithm is proposed to evolve the solution of voltage stability in RDS. The effective compensating devices of DG and DSTATCOM are used for increasing the voltage profile and reducing the system losses. The VSI is also calculated to predetermine the best position of the two compensating devices <stro

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Reddy, N. Madhusudhan, Dr T. Vamsee Kiran, I. Kranthi Kumar, Karri Ravikumar Reddy, Chodagam Srinivas, and K. Divya. "Optimizing Capacitor Placement in Distribution Systems Under Variable Loading Conditions with Golden Jack Optimization (GJO)." International Journal of Electrical and Electronics Research 12, no. 2 (2024): 338–44. http://dx.doi.org/10.37391/ijeer.120202.

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In modern society, the demand for electricity is ever-growing, making the minimization of power losses in distribution systems paramount. One significant aspect contributing to these losses is the strategic placement of capacitors within the distribution network. Efficient capacitor placement not only reduces power losses but also enhances the overall performance and reliability of the system. In today's world, where electricity is indispensable, minimizing power losses in the distribution system holds significant importance. This research introduces the Golden Jack Optimization (GJO) algorith

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NAWAS, AHAMED HUSSAIN ASIF NOORUL, and SELVAPERUMAL SUNDARAMOORTHY. "GIANT TREVALLY OPTIMIZED CONGESTION MANAGEMENT USING FACTS CONTROLLER ALLOCATION IN DEREGULATED ELECTRICITY MARKETS." REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE 69, no. 4 (2024): 377–82. http://dx.doi.org/10.59277/rrst-ee.2024.69.4.2.

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One of the technical issues arising from deregulation is transmission line congestion. The size and location of FACTS controllers like thyristor-controlled series compensators (TCSCs) and static VAR compensator (SVC) devices significantly affect their efficiency in congestion management problems. As a nonlinear problem, locating and sizing these devices in a power network is difficult. To solve this issue, this paper presents the technique for optimal FACTS placement using the giant trevally optimizer (GTO) algorithm for congestion management (CM). Three objective functions are considered to r

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In, Sokvan, Sovann Ang, Chivon Choeung, Sokun Ieng, Horchhong Cheng, and Vichet Huy. "Optimal Reactive Power Dispatch Using Artificial Gorilla Troops Optimizer Considering Voltage Stability." International Journal of Electrical and Electronics Research 12, no. 3 (2024): 1000–1008. http://dx.doi.org/10.37391/ijeer.120334.

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The power system has been expanded to supply and fulfil the consumers’ requirements for reliability, affordability, and power quality. Power loss reduction and voltage stability enhancement are important points and have been considered interesting subjects for researchers and utilities. Furthermore, reactive power plays an important role in power system stability, security, and voltage improvement, and it is known as reactive power dispatch (RPD). In this paper, a newly developed meta-heuristic optimization technique that inspired the gorilla troop’s social intelligence in nature is applied. I

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Lenin, K. "ENHANCED SPIDER ALGORITHM FOR MINIMIZATION OF REAL POWER LOSS." International Journal of Research -GRANTHAALAYAH 6, no. 4 (2018): 301–11. http://dx.doi.org/10.29121/granthaalayah.v6.i4.2018.1665.

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In this paper Enhanced Spider (ES) algorithm is proposed to solve reactive power Problem. Enthused by the spiders, a new Enhanced Spider (ES) algorithm is utilized to solve reactive power problem. The composition is primarily based on the foraging approach of social spiders, which make use of of the vibrations spread over the spider web to choose the position of prey. The simulation results demonstrate high-quality performance of Enhanced Spider (ES) algorithm in solving reactive power problem. The projected Enhanced Spider (ES) algorithm has been tested in standard IEEE 57,118 bus systems and

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Dr., K. Lenin *1. "ENHANCED SPIDER ALGORITHM FOR MINIMIZATION OF REAL POWER LOSS." International Journal of Research - Granthaalayah 6, no. 4 (2018): 301–11. https://doi.org/10.5281/zenodo.1248184.

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In this paper Enhanced Spider (ES) algorithm is proposed to solve reactive power Problem. Enthused by the spiders, a new Enhanced Spider (ES) algorithm is utilized to solve reactive power problem. The composition is primarily based on the foraging approach of social spiders, which make use of of the vibrations spread over the spider web to choose the position of prey. The simulation results demonstrate high-quality performance of Enhanced Spider (ES) algorithm in solving reactive power problem. The projected Enhanced Spider (ES) algorithm has been tested in standard IEEE 57,118 bus systems and

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T., Nireekshana, Upadhyay Poonam, Bhavani J., and Krishna Kumari N. "Comparative Analysis of Multi Objective Optimal Power Flow in Power Systems." Journal of Recent Trends in Electrical Power System 3, no. 2 (2020): 1–12. https://doi.org/10.5281/zenodo.3988980.

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<em>This paper consists of the comparative analysis of different methods applied to loss minimization in power system. The objective is to minimize the total power loss and keep the power outputs of generators; bus voltages, shunt capacitors/reactors and transformer tap setting in their specified limits. By maintaining the whole system power loss as minimum there by minimum cost allocation can be achieved. This project explains a comparative analysis between Gradient methods, Search methods and Genetic Algorithm Approach (GA). The Gradient and Search methods are the iterative local optimizatio

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Lenin, K., B. Ravindhranath Reddy, and M. Surya Kalavathi. "IMPROVED BABOON ALGORITHM FOR MINIMIZATION OF REAL POWER LOSS." International Journal of Research -GRANTHAALAYAH 5, no. 1 (2017): 33–42. http://dx.doi.org/10.29121/granthaalayah.v5.i1.2017.1690.

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This paper projects Improved Baboon Algorithm (IBA) for solving the Reactive Power dispatch problem. The key feature in this problem is reduction of real power loss and to keep voltage profiles within limits. This algorithm is inspired from the tree climbing procedures of Baboons, where the Baboons look for the highest tree by climbing up from their positions. The simulation results expose amended performance of the IBA in solving an optimal reactive power dispatch problem. In order to evaluate up the performance of the proposed algorithm, it has been tested on Standard IEEE 30 bus system and

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K.Lenin, Reddy B.Ravindhranath, and Kalavathi M.Surya. "IMPROVED BABOON ALGORITHM FOR MINIMIZATION OF REAL POWER LOSS." International Journal of Research -GRANTHAALAYAH 5, no. 1 (2017): 33–42. https://doi.org/10.5281/zenodo.259549.

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This paper projects Improved Baboon Algorithm (IBA) for solving the Reactive Power dispatch problem. The key feature in this problem is reduction of real power loss and to keep voltage profiles within limits. This algorithm is inspired from the tree climbing procedures of Baboons, where the Baboons look for the highest tree by climbing up from their positions. The simulation results expose amended performance of the IBA in solving an optimal reactive power dispatch problem. In order to evaluate up the performance of the proposed algorithm, it has been tested on Standard IEEE 30 bus system and

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Lenin, K. "REAL POWER LOSS MINIMIZATION BY MUTUAL MAMMAL BEHAVIOR ALGORITHM." International Journal of Research -GRANTHAALAYAH 5, no. 5 (2017): 88–98. http://dx.doi.org/10.29121/granthaalayah.v5.i5.2017.1840.

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This paper presents a Mutual Mammal Behavior (MM) algorithm for solving Reactive power problem in power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization is taken is taken as main objective. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. A Meta heuristic algorithm for global optimization called the Mutual Mammal Behavior (MM) is introduced. Mammal groups like Carnivores, African lion, Cheetah, Dingo Fennec Fox, Moose, Polar Bear, Sea

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Dissertations / Theses on the topic "Power Loss Minimization and GTO Algorithm"

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Bouchard, Derrick Earl. "Towards loss minimization in power distribution systems using AI, the WatDist algorithm." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq21333.pdf.

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Lukman, Dedek Electrical Engineering &amp Telecommunications Faculty of Engineering UNSW. "Loss minimization in the modified algorithm of load flow analysis in industrial power system." Awarded by:University of New South Wales, 2002. http://handle.unsw.edu.au/1959.4/35453.

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The research looks at an alternative technique of loss minimization using B-loss formula in solving load flow analysis. A modeling of electrical system elements is first developed. A literature review on the history and development of load flow analysis and loss minimization especially on the use of B-loss formula are delivered. A report on load flow in practice and loss minimization measures based on a utility visit to EnergyAustralia is given. An analysis on three types of industrial power systems is also discussed. A substantial study on EDSA was made to adapt the commercial load flow progr

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Rosseti, Gustavo José Santiago. "Reconfiguração e alocação ótima de geração distribuída em sistemas de energia elétrica." Universidade Federal de Juiz de Fora (UFJF), 2011. https://repositorio.ufjf.br/jspui/handle/ufjf/2461.

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Samajpati, Dipanjan. "Distributed Generation Allocation For Power Loss Minimization And Voltage Improvement Of Radial Distribution Systems Using Genetic Algorithm." Thesis, 2014. http://ethesis.nitrkl.ac.in/6184/1/212EE4253-7.pdf.

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Numerous advantages attained by integrating Distributed Generation (DG) in distribution systems. These advantages include decreasing power losses and improving voltage profiles. Such benefits can be achieved and enhanced if DGs are optimally sized and located in the systems. This theses presents a distribution generation (DG) allocation strategy to improve node voltage and power loss of radial distribution systems using genetic algorithm (GA). The objective is to minimize active power losses while keep the voltage profiles in the network within specified limit. This approach finds optimal DG a

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Agbugba, Emmanuel Emenike. "Hybridization of particle Swarm Optimization with Bat Algorithm for optimal reactive power dispatch." Diss., 2017. http://hdl.handle.net/10500/23630.

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This research presents a Hybrid Particle Swarm Optimization with Bat Algorithm (HPSOBA) based approach to solve Optimal Reactive Power Dispatch (ORPD) problem. The primary objective of this project is minimization of the active power transmission losses by optimally setting the control variables within their limits and at the same time making sure that the equality and inequality constraints are not violated. Particle Swarm Optimization (PSO) and Bat Algorithm (BA) algorithms which are nature-inspired algorithms have become potential options to solving very difficult optimization problem

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Book chapters on the topic "Power Loss Minimization and GTO Algorithm"

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Mustaffa, Zuriani, and Mohd Herwan Sulaiman. "Loss Minimization of Optimal Power Flow with Stochastic Solar Power Generation Using Improved Salp Swarm Algorithm." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8690-0_13.

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Maity, Deblina, Sumit Banerjee, and Chandan Kumar Chanda. "Planning of Power Loss and Fuel Cost Minimization by Deployment of DERs Using Evolutionary Algorithm." In Advances in Sustainability Science and Technology. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9033-4_46.

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Kumar, Manish, Bhavnesh Kumar, and Asha Rani. "Loss Minimization Strategy for Induction Motor-Driven Electric Vehicles." In Futuristic Technology Perspectives on Entrepreneurship and Sustainable Innovation. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-5871-6.ch009.

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This chapter presents a loss model control (LMC) strategy for electric vehicles driven by poly-phase induction motors. The LMC is a widely used strategy for loss minimization of induction motor drives. The conventional LMC approach of reducing losses by minimizing the rotor flux/magnetizing current leads to surplus power loss caused by sudden increase in stator current during transient states. Therefore, to overcome the challenges, a new approach of LMC is presented for efficiency enhancement by equalizing the motor core losses and the copper losses. Furthermore, grasshopper optimization algor

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Mukherjee, V., Aparajita Mukherjee, and Dharmbir Prasad. "Whale Optimization Algorithm With Wavelet Mutation for the Solution of Optimal Power Flow Problem." In Advances in Computational Intelligence and Robotics. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-4766-2.ch023.

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This chapter proposes whale optimization algorithm (WOA) with wavelet mutation (WOA-WM) for solving optimal power flow (OPF) problem. The proposed WOA-WM algorithm of the present work utilizes wavelet theory to enhance the optimizing performance of basic WOA in exploring the solution space more effectively for getting better solution. Both WOA and the proposed WOA-WM algorithms are tested on four test power systems under different objective functions (that reflects either minimization of fuel cost or that of transmission line loss or improvement of voltage profile) for getting the optimal solu

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Kumar, G. V. Nagesh, B. Venkateswara Rao, D. Deepak Chowdary, and Polamraju V. S. Sobhan. "A Computational Comparison of Swarm Optimization Techniques for Optimal Load Shedding Under the Presence of FACTS Devices to Avoid Voltage Instability." In Critical Developments and Applications of Swarm Intelligence. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5134-8.ch008.

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Voltage instability has become a serious threat to the operation of modern power systems. Load shedding is one of the effective countermeasures for avoiding instability. Improper load shedding may result in huge technical and economic losses. So, an optimal load shedding is to be carried out for supplying more demand. This chapter implements bat and firefly algorithms for solving the optimal load shedding problem to identify the optimal amount of load to be shed. This is applied for a multi-objective function which contains minimization of amount of load to be shed, active power loss minimizat

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Conference papers on the topic "Power Loss Minimization and GTO Algorithm"

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Medina, Alexander González, Facundo Aguilera, and Manuel Javier Vidal Cué. "Loss Minimization Algorithm Based Strategy for EV Hybrid Energy Storage System Optimization." In 2024 IEEE Vehicle Power and Propulsion Conference (VPPC). IEEE, 2024. http://dx.doi.org/10.1109/vppc63154.2024.10755374.

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Datta, Suchana, Sadhan Gope, Diptanu Das, Raj Chakraborty, and Punam Das. "Active Power Loss Minimization in IEEE-33 Distribution Bus System by Symbiotic Organisms Search Algorithm." In 2024 IEEE Students Conference on Engineering and Systems (SCES). IEEE, 2024. http://dx.doi.org/10.1109/sces61914.2024.10652464.

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Bryan, Toalombo, and Tipán Luis. "Topological reconfiguration in Distribution Systems for loss minimization considering load-ability constraints through a group search algorithm." In 2024 11th International Conference on Power and Energy Systems Engineering (CPESE). IEEE, 2024. https://doi.org/10.1109/cpese62584.2024.10841089.

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Manjhi, Rampreet, Deepak Kumar Lal, and Sandeep Biswal. "Optimal Allocation of DGs in Radial Distribution Network for Power Loss Minimization based on LSF and GJO Algorithm." In 2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC). IEEE, 2022. http://dx.doi.org/10.1109/isssc56467.2022.10051616.

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Bhardwaj, A., P. Singh, and B. Bhattacharyya. "Active Power Loss Minimization By Harmony Search Algorithm." In Michael Faraday IET International Summit 2015. Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/cp.2015.1694.

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Balachennaiah, P., and M. Suryakalavathi. "Real Power Loss minimization using symbiotic organisms search algorithm." In 2015 Annual IEEE India Conference (INDICON). IEEE, 2015. http://dx.doi.org/10.1109/indicon.2015.7443589.

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Beti, Llorenc Taus, and Uwe Schafer. "Loss minimization algorithm of an IPMSM based on analytical expressions." In 2014 16th European Conference on Power Electronics and Applications (EPE'14-ECCE Europe). IEEE, 2014. http://dx.doi.org/10.1109/epe.2014.6910799.

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Hamour, Hanan, Salah Kamel, Hussein Abdel-mawgoud, Ahmed Korashy, and Francisco Jurado. "Distribution Network Reconfiguration Using Grasshopper optimization Algorithm for Power Loss Minimization." In 2018 International Conference on Smart Energy Systems and Technologies (SEST). IEEE, 2018. http://dx.doi.org/10.1109/sest.2018.8495659.

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Babu, P. Ravi, Sushma Pasunuru, and Gattu Vaishnavi. "Genetic algorithm and reconfiguration for loss minimization in radial distribution system." In 2017 International Conference on Smart Grids, Power and Advanced Control Engineering (ICSPACE). IEEE, 2017. http://dx.doi.org/10.1109/icspace.2017.8343446.

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Bhowmik, Arup Ratan, Ajoy Kumar Chakraborty, and Priyanath Das. "Optimal location of UPFC based on PSO algorithm considering active power loss minimization." In 2012 IEEE Fifth Power India Conference. IEEE, 2012. http://dx.doi.org/10.1109/poweri.2012.6479489.

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