Design and Application of Decision Support System for Power Loss Reduction of Rural Power Network

2012 ◽  
Vol 241-244 ◽  
pp. 682-686 ◽  
Author(s):  
Bing Xia Chen ◽  
Jian Cheng Tan
Keyword(s):  
Power Loss ◽  
High Efficiency ◽  
Reactive Power ◽  
Distribution Networks ◽  
Loss Reduction ◽  
Power Network ◽  
Loss Calculation ◽  
Power Loss Reduction ◽  
Theoretical Line ◽  
Line Loss

Theoretical line loss calculation is the basis of line loss analysis and decision-making in loss reduction. Due to technical constraints in the past, traditional theoretical line loss calculation method uses a large number of simplified calculations. This paper proposes a new online method of line loss calculation in distribution networks which is based on PQU (active power, reactive power and voltage) load curves. The proposed method has a large amount of data operation and thus can accurately reflect the load variation with time. Based on a new kind of metrical equipment and the proposed method, the assistant decision system for comprehensive power loss reduction of rural power network has been developed and has been tested on several rural power networks. The results show its convenience, accurate calculation and high efficiency.

scholarly journals Optimal Placement and Sizing of Capacitors in Radial Distribution Systems: A Two-Stage Method

2020 ◽  
pp. 31-43
Author(s):  
Sunday Adeleke Salimon ◽  
Abiodun Aderemi Baruwa ◽  
Saheed Oluwasina Amuda ◽  
Hafiz Adesupo Adeleke
Keyword(s):  
Radial Distribution ◽  
Power Loss ◽  
Reactive Power ◽  
Distribution Networks ◽  
Loss Reduction ◽  
Two Stage ◽  
Second Stage ◽  
Power Loss Reduction ◽  
Radial Distribution Networks ◽  
Shunt Capacitors

Optimal allocation of shunt capacitors in the radial distribution networks results in both technical and economic benefits. This paper presents a two-stage method of Loss Sensitivity Factor (LSF) and Cuckoo Search Algorithm (CSA) to find the optimal size and location of shunt capacitors with the objective of minimizing cost due to power loss and reactive power compensation of the distribution networks. The first stage utilizes the LSF to predict the potential candidate buses for shunt capacitor placement thereby reducing the search space of the second stage and avoiding unnecessary repetitive load flow while the second stage uses the CSA to find the size and actual placement of the shunt capacitors satisfying the operating constraints. The applicability of the proposed two stage method is tested on the standard IEEE 33-bus and Ayepe 34-bus Nigerian radial distribution networks of the Ibadan Electricity Distribution Company. After running the algorithm, the simulation results gave percentage real and reactive power loss reduction of 34.28% and 28.94% as compared to the base case for the IEEE 33-bus system while the percentage real and reactive power loss reduction of 22.89% and 21.40% was recorded for the Ayepe 34-bus system. Comparison of the obtained results with other techniques in literatures for the standardized IEEE 33-bus reveals the efficiency of the proposed method as it achieved technical benefits of reduced total power loss, improved voltage profile and bus voltage stability, and the economic benefit of reduced total cost due to electrical power loss and compensation.

Theoretical Line Loss Calculation in 10kV Power Distribution Network Based on an Improved Alternative Method

2012 ◽  
Vol 241-244 ◽  
pp. 687-692
Author(s):  
Bing Xia Chen ◽  
Jian Cheng Tan
Keyword(s):  
Power Distribution ◽  
Reactive Power ◽  
Distribution Networks ◽  
Power Networks ◽  
Power Distribution Network ◽  
Loss Analysis ◽  
Loss Calculation ◽  
Theoretical Line ◽  
Line Loss ◽  
Calculation System

Theoretical line loss calculation is the basis of making line loss analysis and loss-reduction decision. Due to technical constraints in the past, traditional algorithms on theoretical line loss calculation used a large number of simplified calculations. This paper proposes a new online method of line loss calculation in distribution networks which is based on PQU (active power, reactive power and voltage) load curves. Based on a new kind of metrical equipment and the proposed method, the power loss calculation system has been developed and has been tested on several rural power networks. The results prove that the method improves the assumptions of traditional algorithms and calculation precision.

scholarly journals ACTIVE POWER LOSS REDUCTION BY SYNTHESIZED ALGORITHM

2018 ◽  
Vol 6 (5) ◽  
pp. 149-156
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Harmony Search ◽  
Pso Algorithm ◽  
Loss Reduction ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
De Algorithm ◽  
Power Loss Reduction ◽  
Power Problem

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 and compared to other standard reported algorithms. Simulation results show’s that Synthesized Algorithm (SA) successfully reduces the real power loss and voltage profiles are within the limits.

scholarly journals Real power loss reduction by hyena optimizer algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 186
Author(s):  
Lenin Kanagasabai
Keyword(s):  
Mathematical Modelling ◽  
Convergent Evolution ◽  
Power Loss ◽  
Social Relationship ◽  
Reactive Power ◽  
Scent Marking ◽  
Loss Reduction ◽  
Real Power ◽  
Power Loss Reduction ◽  
Power Problem

<p><span>To solve optimal reactive power problem this paper projects Hyena Optimizer (HO) algorithm and it inspired from the behaviour of Hyena. Collaborative behaviour &amp; Social relationship between Hyenas is the key conception in this algorithm. Hyenas a form of carnivoran mammal &amp; deeds are analogous to canines in several elements of convergent evolution. Hyenas catch the prey with their teeth rather than claws – possess hardened skin feet with large, blunt, no retractable claws are adapted for running and make sharp turns. However, the hyenas' grooming, scent marking, defecating habits, mating and parental behaviour are constant with the deeds of other feliforms. Mathematical modelling is formulated for the basic attributes of Hyena. Standard IEEE 14,300 bus test systems used to analyze the performance of Hyena Optimizer (HO) algorithm. Loss has been reduced with control variables are within the limits.</span></p>

Real Power Loss Reduction by Billfish and Red Mullet Optimization Algorithms

2021 ◽  
Author(s):  
Lenin Kanagasabai
Keyword(s):  
Power Loss ◽  
Voltage Stability ◽  
Reactive Power ◽  
Smart Cities ◽  
Fitness Function ◽  
Loss Reduction ◽  
Real Power ◽  
Red Mullet ◽  
Power Loss Reduction ◽  
Continuous Power

In this paper Billfish Optimization Algorithm (BOA) and Red Mullet Optimization (RMO) Algorithm has been designed for voltage stability enhancement and power loss reduction. Electrical Power is one among vital need in the society and also it plays lead role in formation of smart cities. Continuous power supply is essential and mainly quality of the power should be maintained in good mode. In this work real power loss reduction is key objective. Natural hunting actions of Billfish over pilchards are utilized to model the algorithm. Candidate solutions in the projected algorithm are Billfish and population in the exploration space is arbitrarily engendered. Movement of Billfish is high, it will attack the pilchards vigorously and it can’t escape from the attack done by the group of Billfish. Then in this paper Red Mullet Optimization (RMO) Algorithm is proposed to solve optimal reactive power problem. Projected RMO algorithm modeled based on the behavior and characteristics of red mullet. As a group they hunt for the prey and in each group there will be chaser and blocker. When the prey approaches any one of the blocker red mullet then automatically it will turn as new chaser. So roles will interchangeable and very much flexible. At any time chaser will become blocker and any of the blocker will become a chaser with respect to prey position and conditions. Then in that particular area when all the preys are hunted completed then red mullet group will change the area. So there will be flexibility and changing the role quickly with respect to prey position. Alike to that with reference to the fitness function the particle will be chosen as chaser. By means of considering L (voltage stability) - index BOA, and RMO algorithms verified in IEEE 30- bus system. Then without L-index BOA and RMO algorithms is appraised in 30 bus test systems. Both BOA and RMO algorithms condensed the power loss proficiently with improvement in voltage stability and minimization of voltage deviation.

scholarly journals Real Power Loss Reduction by Rock Dove Optimization and Fuligo Septica Optimization Algorithms

2020 ◽  
Vol 7 (2) ◽  
pp. E1-E6
Author(s):  
L. Kanagasabai
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Power Transmission ◽  
Transmission Loss ◽  
Loss Reduction ◽  
Exploration And Exploitation ◽  
Real Power ◽  
Fitness Value ◽  
Power Loss Reduction

This paper aims to use the Rock Dove (RD) optimization algorithm and the Fuligo Septica optimization (FSO) algorithm for power loss reduction. Rock Dove towards a particular place is based on the familiar (sight) objects on the traveling directions. In the formulation of the RD algorithm, atlas and range operator, and familiar sight operators have been defined and modeled. Every generation number of Rock Dove is reduced to half in the familiar sight operator and Rock Dove segment, which hold the low fitness value that occupying the lower half of the generation will be discarded. Because it is implicit that the individual’s Rock Dove is unknown with familiar sights and very far from the destination place, a few Rock Doves will be at the center of the iteration. Each Rock Dove can fly towards the final target place. Then in this work, the FSO algorithm is designed for real power loss reduction. The natural vacillation mode of Fuligo Septica has been imitated to develop the algorithm. Fuligo Septica connects the food through swinging action and possesses exploration and exploitation capabilities. Fuligo Septica naturally lives in chilly and moist conditions. Mainly the organic matter in the Fuligo Septica will search for the food and enzymes formed will digest the food. In the movement of Fuligo Septica it will spread like a venous network, and cytoplasm will flow inside the Fuligo Septica in all ends. THE proposed RD optimization algorithm and FSO algorithm have been tested in IEEE 14, 30, 57, 118, and 300 bus test systems and simulation results show the projected RD and FSO algorithm reduced the real power loss. Keywords: optimal reactive power, transmission loss, Rock Dove, Fuligo Septica.

scholarly journals REAL POWER LOSS REDUCTION BY ENRICHED GENETIC ALGORITHM

2017 ◽  
Vol 5 (8) ◽  
pp. 18-25
Author(s):  
K. Lenin
Keyword(s):  
Genetic Algorithm ◽  
Power Loss ◽  
Optimization Problem ◽  
Reactive Power ◽  
Selection Procedure ◽  
Selection Method ◽  
Loss Reduction ◽  
Reactive Power Optimization ◽  
Real Power ◽  
Power Loss Reduction

In this paper, Enriched Genetic Algorithm (EGA) utilized to solve reactive power optimization problem. In the proposed algorithm Stochastic Universal Selection (SS) is utilized to improve the selection procedure. The selection method in Genetic algorithm (GA) plays a significant role in the runtime to get the optimized solution as well as in the superiority of the solution. In this work, an enriched selection technique is presented which uphold both fast runtime and elevated quality solution. Proposed EGA algorithm has been tested in standard IEEE 118 & practical 191 bus test systems and simulation results show clearly the advanced performance of the proposed algorithm in reducing the real power loss.

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