scholarly journals A Comprehensive Review on Optimal Location and Sizing of Reactive Power Compensation Using Hybrid-Based Approaches for Power Loss Reduction, Voltage Stability Improvement, Voltage Profile Enhancement and Loadability Enhancement

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 222733-222765
Author(s):  
Bazilah Ismail ◽  
Noor Izzri Abdul Wahab ◽  
Mohammad Lutfi Othman ◽  
Mohd Amran Mohd Radzi ◽  
Kanendra Naidu Vijyakumar ◽  
...  
Keyword(s):  
Power Loss ◽  
Voltage Stability ◽  
Reactive Power ◽  
Reactive Power Compensation ◽  
Optimal Location ◽  
Loss Reduction ◽  
Voltage Profile ◽  
Comprehensive Review ◽  
Power Loss Reduction

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 Voltage profile improvement and losses minimization for Hayin Rigasa radial network Kaduna using distributed generation

2021 ◽  
Vol 5 (1) ◽  
pp. 20-36
Author(s):  
Idris A. Araga ◽  
Abel E. Airoboman ◽  
Simon A. Auta
Keyword(s):  
Distributed Generation ◽  
Power Loss ◽  
Reactive Power ◽  
Circuit Breaker ◽  
Research Work ◽  
Sensitivity Index ◽  
Loss Reduction ◽  
Voltage Profile ◽  
Active And Reactive Power ◽  
Power Loss Reduction

This research work has presented the application of distributed generation (DG) units in a simultaneous placement approach on IEEE 33 radial test systems for validation of the technique with further implementation on 56-Bus Hayin Rigasa feeder. The genetic algorithm (GA) is employed in obtaining the optimal sizes and load loss sensitivity index for locations of the DGs for entire active and reactive power loss reduction. The voltage profile index is computed for each bus of the networks to ascertain the weakest voltage bus of the network before and after DG and circuit breaker allocation. The simultaneous placement approach of the DGs is tested with the IEEE 33-bus test networks and Hayin Rigasa feeder network and the results obtained are confirmed by comparing with the results gotten from separate DGs allocation on the networks. For IEEE 33-bus system, the simultaneous allocation of DGs and of optimal sizes 750 kW, 800 kW and at locations of buses 2 and 6 respectively, lead to a 66.49 % and 68.64 % drop in active and reactive power loss and 3.02 % improvement in voltage profile. For the 56-bus Hayin Rigasa network in Kaduna distribution network, the simultaneous placement of DGs of sizes 1,470 kW and 1490 kW at locations of bus 16 and 23 respectively, lead to a 79.54 % and 73.98 % drop in active and reactive power loss and 15.94 % improvement in voltage profile. From results comparison, it is evident that the allocation of DGs using the combination GA and load loss sensitivity index, gives an improved performance in relations to power loss reduction and voltage profile improvements of networks when compared to without DGs.

scholarly journals TECHNO-ECONOMIC ASSESMENT FOR DISTRIBUTED GENERATION PLACEMENT IN DISTRIBUTION SYSTEM

KURVATEK ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 1-6
Author(s):  
Sugiarto Kadiman
Keyword(s):  
Distributed Generation ◽  
Power Loss ◽  
Distribution Systems ◽  
Reactive Power ◽  
Test System ◽  
Optimal Placement ◽  
Loss Reduction ◽  
Voltage Profile ◽  
Power Loss Reduction ◽  
Reduction Index

This paper presents a proposed function which is known as techno-economic model for optimal placement of distributed generation (DG) resources in distribution systems in order to minimize the power losses and improve voltage profile. Combined sensitivity factors (CSF), such real power loss reduction index, reactive power loss reduction index, voltage profile improvement index, and life cycle cost, and particle swarm optimization (PSO) are applied to the proposed technique to obtain the best compromise between these costs. Simulation results on IEEE 14-bus test system are presented to demonstrate the usefulness of the proposed procedure.

Reactive Power Compensation of Oilfield Distribution Systems Based on Tabu Search Algorithm

2013 ◽  
Vol 397-400 ◽  
pp. 1113-1116
Author(s):  
Xiao Meng Wu ◽  
Wang Hao Fei ◽  
Xiao Mei Xiang ◽  
Wen Juan Wang
Keyword(s):  
Power Loss ◽  
Distribution Systems ◽  
Reactive Power ◽  
Search Algorithm ◽  
Global Optimum ◽  
Reactive Power Compensation ◽  
Active Power ◽  
Voltage Profile ◽  
Voltage Range ◽  
Shunt Capacitors

In order to solve the problem in reactive power compensation of oilfield distribution systems at present, a Taboo search algorithm is proposed in this paper, by which the optimal location and size of shunt capacitors on distribution systems are determined. Then the voltage profile is improved and the active power loss is reduced. In this paper, Voltage qualified is used as objective function to search an initial solution that meets the voltage constraints so that it is feasible in practicable voltage range; then the global optimum solution can be got when taking the reduced maximum of active power loss as objective unction. The examples show that the improved algorithm is feasible and effective.

scholarly journals Self-Adaptive Firefly-Algorithm-Based Unified Power Flow Controller Placement with Single Objectives

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Selvarasu Ranganathan ◽  
S. Rajkumar
Keyword(s):  
Power Loss ◽  
Power Flow ◽  
Unified Power Flow Controller ◽  
Loss Reduction ◽  
Voltage Profile ◽  
Power Loss Reduction ◽  
Voltage Stability Enhancement ◽  
Stability Enhancement ◽  
Power Flow Controller ◽  
Voltage Profile Improvement

The selection of positions for unified power flow controller (UPFC) placement in transmission network is an essential factor, which aids in operating the system in a more reliable and secured manner. This paper focuses on strengthening the power system performance through UPFC placement employing self-adaptive firefly algorithm (SAFA), which selects the best positions along with parameters for UPFC placement. Three single objectives of real power loss reduction, voltage profile improvement, and voltage stability enhancement are considered in this work. IEEE 14, 30, and 57 test systems are selected to accomplish the simulations and to reveal the efficacy of the proposed SAFA approach; besides, solutions are compared with two other algorithms solutions of honey bee algorithm (HBA) and bacterial foraging algorithm (BFA). The proposed SAFA contributes real power loss reduction, voltage profile improvement, and voltage stability enhancement by optimally choosing the placement for UPFC.

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>

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