scholarly journals Voltage Collapse Enhancement and Loss Reduction by Reactive Power Reserve

2011 ◽  
Vol 12 (12) ◽  
pp. 32-42
Author(s):  
A. Subramanian ◽  
Dr. G. Ravi
Keyword(s):  
Reactive Power ◽  
Voltage Collapse ◽  
Loss Reduction

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.

Loss Minimization in Active Distribution Network

2020 ◽  
pp. 119-135 ◽  
Author(s):  
Bawoke Simachew
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Electrical Power ◽  
Distribution Network ◽  
Optimization Method ◽  
Energy Utilization ◽  
Optimal Size ◽  
Loss Reduction ◽  
Voltage Profile ◽  
Power Sources

Power loss reduction is an important problem that needs to be addressed with respect to generating electrical power. It is important to reduce power loss using locally generated power sources and/or compensations. This chapter brings a method of presents a method of maximizing energy utilization, feeder loss reduction, and voltage profile improvement for radial distribution network using the active and reactive power sources. Distributed Generation (DG) (wind and solar with backup by biomass generation) and shunt capacitor (QG) for reactive power demand are used. Integrating DG and QG at each bus might reduce the loss but it is economically unaffordable, especially for developing countries. Therefore, the utilization optimization method is required for finding an optimal size and location to feeders for placing QG and DG to minimize feeder loss.

An ant colony optimization algorithm-based emergency control strategy for voltage collapse mitigation

2012 ◽  
Vol 3 (2) ◽  
pp. 147-156 ◽  
Author(s):  
R. A. El-Sehiemy ◽  
A. A. A. El Ela ◽  
A. M. M. Kinawy ◽  
M. T. Mouwafia
Keyword(s):  
Power Systems ◽  
Ant Colony Optimization ◽  
Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Ant Colony ◽  
Voltage Collapse ◽  
Ant Colony Optimization Algorithm ◽  
Preventive Control ◽  
Control Actions

Abstract This paper presents optimal preventive control actions using ant colony optimization (ACO) algorithm to mitigate the occurrence of voltage collapse in stressed power systems. The proposed objective functions are: minimizing the transmission line losses as optimal reactive power dispatch (ORPD) problem, maximizing the preventive control actions by minimizing the voltage deviation of load buses with respect to the specified bus voltages and minimizing the reactive power generation at generation buses based on control variables under voltage collapse, control and dependent variable constraints using proposed sensitivity parameters of reactive power that dependent on a modification of Fast Decoupled Power Flow (FDPF) model. The proposed preventive actions are checked for different emergency conditions while all system constraints are kept within their permissible limits. The ACO algorithm has been applied to IEEE standard 30-bus test system. The results show the capability of the proposed ACO algorithm for preparing the maximal preventive control actions to remove different emergency effects.

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