Application of Moth-Flame Optimization Algorithm for the Determination of Maximum Loading Limit of Power System

2020 ◽  
pp. 78-93
Author(s):  
Suvabrata Mukherjee ◽  
Provas Kumar Roy
Keyword(s):  
Power System ◽  
Optimization Algorithm ◽  
Optimization Technique ◽  
Spiral Path ◽  
Maximum Loading ◽  
Navigation Strategy ◽  
Multi Agent ◽  
Moth Flame Optimization Algorithm ◽  
Reduced Time

Moth-flame optimization algorithm (MFOA) based on the navigation strategy of moths in universe is a novel bio-inspired optimization technique and has been exerted for determining the maximum loading limit of power system. This process is highly effective for traversing long distances following a straight path. As a matter of fact, moths follow a deadly spiral path as artificial lights tend to confuse them. Exploration and exploitation are two vital aspects of the algorithm, used in tuning of the parameters. The algorithm is verified on MATPOWER case30 and case118 systems. Comparison of the performance of MFOA has been done with other evolutionary algorithms such as multi-agent hybrid PSO (MAHPSO), differential evolution (DA), hybridized DE, and PSO (DEPSO). The performance of MFOA in determining maximum loading limit is verified from the results. In much reduced time, MFO algorithm also gives high maximum loading point (MLP).

scholarly journals Combined load frequency and terminal voltage control of power systems using moth flame optimization algorithm

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Deepak Kumar Lal ◽  
Ajit Kumar Barisal
Keyword(s):  
Power Systems ◽  
Power System ◽  
Optimization Algorithm ◽  
Optimization Technique ◽  
Voltage Control ◽  
Electric Power System ◽  
Primary Control ◽  
Load Frequency ◽  
Terminal Voltage ◽  
Moth Flame Optimization Algorithm

AbstractStability of nominal frequency and voltage level in an electric power system is the primary control issue of practicing engineers. Any deterioration in these two parameters will affect the performance and life expectancy of the associated machinery to the power system. Hence, controllers are installed and set for a specific working situation and deal with small variations in load demand to keep the frequency and terminal voltage magnitude within the permissible limits. As the system performance can be improved with selecting suitable controller, an attempt has been made to design fractional-order PID (FOPID) controller for combined frequency and voltage control problems. This paper presents plan and execution examination of FOPID controller for simultaneous load frequency and voltage control of power system using recently developed nature-motivated powerful optimization technique, i.e., moth flame optimization algorithm. The first part of the present work demonstrates the implementation of the proposed technique on frequency stabilization of isolated power system with AVR for excitation voltage control. The superiority and effectiveness of the proposed approach are tested by comparing the dynamic response of the system with PID controllers optimized by other intelligent techniques. Then the present work is extended to multi-unit two-area power system. The tuning ability of the algorithm is extensively and comparatively investigated.

Moth-Flame Optimization Algorithm Based Load Flow Analysis of Ill-Conditioned Power Systems

2020 ◽  
Vol 11 (1) ◽  
pp. 1-27
Author(s):  
Suvabrata Mukherjee ◽  
Provas Kumar Roy
Keyword(s):  
Power Systems ◽  
Optimization Algorithm ◽  
Flow Analysis ◽  
Load Flow ◽  
Critical Conditions ◽  
Spiral Path ◽  
Load Flow Analysis ◽  
Navigation Strategy ◽  
Whale Optimization ◽  
Moth Flame Optimization Algorithm

Using a novel bio-inspired optimization algorithm based on the navigation strategy of moths in a universe called transverse orientation, called the Moth-Flame Optimization Algorithm (MFOA), has been applied to solve the load flow problem for power systems under critical conditions. This mechanism is highly effective for traversing covering expanded radius in straight direction. As a matter of fact, moths follow a deadly spiral path as they get confused by artificial lights. For the tuning of parameters, both exploration and exploitation processes play an important role. MFOA is exercised for load flow analysis of small, medium, and large ill-conditioned power systems. The three different standard ill-conditioned cases considered in order to verify the robustness of the algorithm are IEEE 14-bus, IEEE 30-bus and IEEE 57-bus test systems. The results obtained by the application of MFOA shows that the algorithm is able to provide better results than the results obtained by the application of a biogeography inspired optimization algorithm, namely biogeography-based optimization (BBO) and a nature-inspired optimization algorithm, namely the whale optimization algorithm (WOA). This approves the superiority of the proposed algorithm. Simulation and numerical results demonstrate that the MFO is a potent alternative approach for load flow analysis under both normal and critical conditions in practical power systems especially in case of failure of conventional methods, thereby proving the robustness of the method. To the best of the authors' awareness, it is the first report on application of MFOA load flow analysis.

A Novel Hybrid Optimization Algorithm Based on Multi-Agent and Particle Swarm

2011 ◽  
Vol 460-461 ◽  
pp. 512-517
Author(s):  
De Jia Shi ◽  
Wei Jin Jiang ◽  
Xiao Ling Ding
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Agent Interactions ◽  
Fitness Value ◽  
Multi Agent

A novel multi-agent particle swarm optimization algorithm (MAI'SO) is proposed for optimal reactive power dispatch and voltage control of power system. The method integrates multi-agent system (MAS) and particle swarm optimization algorithm (PSO). An agent in MAI.SO represents a particle to PSO and a candidate solution to the optimization problem. All agents live in a lattice-like environment, with each agent fixed on a lattice-point. In order to decrease fitness value, quickly, agents compete and cooperate with their neighbors. and they can also use knowledge. Making use of these agent interactions and evolution mechanism of I.SO. MAPSO realizes the purpose of' minimizing the value of' objective function. MAPSO applied for optimal reactive power is evaluated on an IEEE 30-bus power system. It is shown that the proposed approach converges to better solutions much faster than the earlier reported approaches

scholarly journals Application of Whale Optimization Algorithm for Environmental Constrained Economic Dispatch of Fixed Head Hydro-Wind-Thermal Power System

2019 ◽  
Vol 9 (1) ◽  
pp. 5608-5616
Keyword(s):  
Power System ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Optimization Technique ◽  
Economic Dispatch ◽  
Electric Power System ◽  
Whale Optimization Algorithm ◽  
Multi Objective ◽  
Whale Optimization ◽  
The Cost

This work applies whale optimization algorithm for emission constrained economic dispatch of hydrothermal units including wind power. As the wind power has a characteristic of cleanliness and is renewable, this is convincing to include this for better operation of electric power system keeping in view both economic and environmental aspects. Hydrothermal scheduling integrated with wind power establishes a multi-objective problem that becomes economic emission hydro-thermal-wind scheduling problem while taking into consideration the cost due to wind uncertainty. Whale optimization algorithm is proposed to solve this emission constrained economic dispatch problem with competing objectives. This algorithm is recently developed and gives the best solution among other nature inspired algorithms. The objectives minimum generations as well as emission cost, both are optimized together including different constraints. A daily scheduling of all the three types of systems - hydro, thermal and wind is considered to evaluate the competency of this optimization technique to get a solution for this multi-objective problem. The experiments are carried out on two systems for determining the effectiveness of the suggested method. Besides, results found using the whale optimization technique have been compared with the results obtained from other evolutionary methods. From the comparison, it is experimentally justified that the whale optimization works faster and the cost of generation as well as cost of emission are lower than the other approaches.

scholarly journals Intelligent swarm-based optimization technique for oscillatory stability assessment in power system

2019 ◽  
Vol 8 (4) ◽  
pp. 342
Author(s):  
N. A. M. Kamari ◽  
I. Musirin ◽  
A. A. Ibrahim ◽  
S. A. Halim
Keyword(s):  
Power System ◽  
Stability Condition ◽  
Least Squares Method ◽  
Damping Ratio ◽  
Optimization Technique ◽  
Computation Time ◽  
Stability Assessment ◽  
Swarm Optimization ◽  
Angle Stability

<p>This paper discussed the prediction of oscillatory stability condition of the power system using a particle swarm optimization(PSO) technique. Indicators namely synchronizing(<em>K<sub>s</sub></em>)and damping(<em>K<sub>d</sub></em>) torque coefficients is appointed to justify the angle stability condition in a multi-machine system. PSO is proposed and implemented to accelerate the determination of angle stability. The proposed algorithm has been confirmed to be more accurate with lower computation time compared with evolutionary programming(EP) technique. This result also supported with other indicators such as eigenvalues determination, damping ratio and least squares method. As a result, proposed technique is achievable to determine the oscillatory stability problems.</p>

Sign in / Sign up

Export Citation Format

Share Document