This document discusses using multi-agent based particle swarm optimization (MAPSO) and genetic algorithms (MAGA) to solve the load shedding problem in power systems. MAPSO integrates a multi-agent system with PSO, allowing agents to cooperate and compete with neighbors to find optimal load shedding solutions quickly. MAGA applies genetic algorithm concepts like reproduction, crossover and mutation to agents. The document outlines the load shedding problem formulation and constraints. It also describes PSO, genetic algorithms, multi-agent systems and how MAPSO and MAGA combine these approaches to determine the most appropriate loads to shed during under frequency or voltage conditions.
Comparison of cascade P-PI controller tuning methods for PMDC motor based on ...IJECEIAES
In this paper, there are two contributions: The first contribution is to design a robust cascade P-PI controller to control the speed and position of the permanent magnet DC motor (PMDC). The second contribution is to use three methods to tuning the parameter values for this cascade controller by making a comparison between them to obtain the best results to ensure accurate tracking trajectory on the axis to reach the desired position. These methods are the classical method (CM) and it requires some assumptions, the genetic algorithm (GA), and the particle swarm optimization algorithm (PSO). The simulation results show the system becomes unstable after applying the load when using the classical method because it assumes cancellation of the load effect. Also, an overshoot of about 3.763% is observed, and a deviation from the desired position of about 12.03 degrees is observed when using the GA algorithm, while no deviation or overshoot is observed when using the PSO algorithm. Therefore, the PSO algorithm has superiority as compared to the other two methods in improving the performance of the PMDC motor by extracting the best parameters for the cascade P-PI controller to reach the desired position at a regular speed.
NOVEL PSO STRATEGY FOR TRANSMISSION CONGESTION MANAGEMENTelelijjournal
In post deregulated era of power system load characteristics become more erratic. Unplanned transactions
of electrical power through transmission lines of particular path may occur due to low cost offered by
generating companies. As a consequence those lines driven close to their operating limits and becomes
congested as the lines are originally designed for traditional vertically integrated structure of power
system. This congestion in transmission lines is unpredictable with deterministic load flow strategy.
Rescheduling active and reactive power output of generators is the promising way to manage congestion.
In this paper Particle Swarm Optimization (PSO) with varying inertia weight strategy, with two variants
e1-PSO and e-2 PSO is applied for optimal solution of active and reactive power rescheduling for
managing congestion. The generators sensitivity technique is opted for identifying participating generators
for managing congestion. Proposed algorithm is tested on IEEE 30 bus system. Comparison is made
between results obtained from proposed techniques to that of results reported in previous literature.
Multi Area Economic Dispatch Using Secant Method and Tie Line MatrixIJAPEJOURNAL
In this paper, Secant method and tie line matrix are proposed to solve multi area economic dispatch (MAED) problem with tie line loss. Generator limits of all generators in each area are calculated at given area power demands plus export (or import) using secant method and the generator limits of all generators are modified as modified generator limits. Central economic dispatch (CED) problem is used to determine the output powers of all generators and finally power flows in all tie lines are determined from tie line matrix. Here, Secant method is applied to solve the CED problem. A modified tie line matrix is used to find power flow in each tie line and then tie line loss is calculated from the power flow in each tie line. The proposed approach has been tested on two-area (two generators in each area) system and four-area (four generators in each area) system. It is observed from various cases that the proposed approach provides optimally best solution in terms of cost with tie line loss with less computational burden.
International Journal of Engineering Research and DevelopmentIJERD Editor
• Electrical, Electronics and Computer Engineering,
• Information Engineering and Technology,
• Mechanical, Industrial and Manufacturing Engineering,
• Automation and Mechatronics Engineering,
• Material and Chemical Engineering,
• Civil and Architecture Engineering,
• Biotechnology and Bio Engineering,
• Environmental Engineering,
• Petroleum and Mining Engineering,
• Marine and Agriculture engineering,
• Aerospace Engineering
Comparison of cascade P-PI controller tuning methods for PMDC motor based on ...IJECEIAES
In this paper, there are two contributions: The first contribution is to design a robust cascade P-PI controller to control the speed and position of the permanent magnet DC motor (PMDC). The second contribution is to use three methods to tuning the parameter values for this cascade controller by making a comparison between them to obtain the best results to ensure accurate tracking trajectory on the axis to reach the desired position. These methods are the classical method (CM) and it requires some assumptions, the genetic algorithm (GA), and the particle swarm optimization algorithm (PSO). The simulation results show the system becomes unstable after applying the load when using the classical method because it assumes cancellation of the load effect. Also, an overshoot of about 3.763% is observed, and a deviation from the desired position of about 12.03 degrees is observed when using the GA algorithm, while no deviation or overshoot is observed when using the PSO algorithm. Therefore, the PSO algorithm has superiority as compared to the other two methods in improving the performance of the PMDC motor by extracting the best parameters for the cascade P-PI controller to reach the desired position at a regular speed.
NOVEL PSO STRATEGY FOR TRANSMISSION CONGESTION MANAGEMENTelelijjournal
In post deregulated era of power system load characteristics become more erratic. Unplanned transactions
of electrical power through transmission lines of particular path may occur due to low cost offered by
generating companies. As a consequence those lines driven close to their operating limits and becomes
congested as the lines are originally designed for traditional vertically integrated structure of power
system. This congestion in transmission lines is unpredictable with deterministic load flow strategy.
Rescheduling active and reactive power output of generators is the promising way to manage congestion.
In this paper Particle Swarm Optimization (PSO) with varying inertia weight strategy, with two variants
e1-PSO and e-2 PSO is applied for optimal solution of active and reactive power rescheduling for
managing congestion. The generators sensitivity technique is opted for identifying participating generators
for managing congestion. Proposed algorithm is tested on IEEE 30 bus system. Comparison is made
between results obtained from proposed techniques to that of results reported in previous literature.
Multi Area Economic Dispatch Using Secant Method and Tie Line MatrixIJAPEJOURNAL
In this paper, Secant method and tie line matrix are proposed to solve multi area economic dispatch (MAED) problem with tie line loss. Generator limits of all generators in each area are calculated at given area power demands plus export (or import) using secant method and the generator limits of all generators are modified as modified generator limits. Central economic dispatch (CED) problem is used to determine the output powers of all generators and finally power flows in all tie lines are determined from tie line matrix. Here, Secant method is applied to solve the CED problem. A modified tie line matrix is used to find power flow in each tie line and then tie line loss is calculated from the power flow in each tie line. The proposed approach has been tested on two-area (two generators in each area) system and four-area (four generators in each area) system. It is observed from various cases that the proposed approach provides optimally best solution in terms of cost with tie line loss with less computational burden.
International Journal of Engineering Research and DevelopmentIJERD Editor
• Electrical, Electronics and Computer Engineering,
• Information Engineering and Technology,
• Mechanical, Industrial and Manufacturing Engineering,
• Automation and Mechatronics Engineering,
• Material and Chemical Engineering,
• Civil and Architecture Engineering,
• Biotechnology and Bio Engineering,
• Environmental Engineering,
• Petroleum and Mining Engineering,
• Marine and Agriculture engineering,
• Aerospace Engineering
Security constrained optimal load dispatch using hpso technique for thermal s...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Locational marginal pricing framework in secured dispatch scheduling under co...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Optimal Unit Commitment Based on Economic Dispatch Using Improved Particle Sw...paperpublications3
Abstract: In this paper, an algorithm to solve the optimal unit commitment problem under deregulated environment has been proposed using Particle Swarm Optimization (PSO) intelligent technique accounting economic dispatch constraints. In the present electric power market, where renewable energy power plants have been included in the system, there is a lot of unpredictability in the demand and generation. This paper presents an improved particle swarm optimization algorithm (IPSO) for power system unit commitment with the consideration of various constraints. IPSO is an extension of the standard particle swarm optimization algorithm (PSO) which uses more particles information to control the mutation operation, and is similar to the social society in that a group of leaders could make better decisions. The program was developed in MATLAB and the proposed method implemented on IEEE 14 bus test system.
Security Constrained UCP with Operational and Power Flow ConstraintsIDES Editor
An algorithm to solve security constrained unit
commitment problem (UCP) with both operational and power
flow constraints (PFC) have been proposed to plan a secure
and economical hourly generation schedule. This proposed
algorithm introduces an efficient unit commitment (UC)
approach with PFC that obtains the minimum system
operating cost satisfying both unit and network constraints
when contingencies are included. In the proposed model
repeated optimal power flow for the satisfactory unit
combinations for every line removal under given study period
has been carried out to obtain UC solutions with both unit and
network constraints. The system load demand patterns have
been obtained for the test case systems taking into account of
the hourly load variations at the load buses by adding
Gaussian random noises. In this paper, the proposed
algorithm has been applied to obtain UC solutions for IEEE
30, 118 buses and Indian utility practical systems scheduled
for 24 hours. The algorithm and simulation are carried
through Matlab software and the results obtained are quite
encouraging.
The optimal solution for unit commitment problem using binary hybrid grey wol...IJECEIAES
The aim of this work is to solve the unit commitment (UC) problem in power systems by calculating minimum production cost for the power generation and finding the best distribution of the generation among the units (units scheduling) using binary grey wolf optimizer based on particle swarm optimization (BGWOPSO) algorithm. The minimum production cost calculating is based on using the quadratic programming method and represents the global solution that must be arriving by the BGWOPSO algorithm then appearing units status (on or off). The suggested method was applied on “39 bus IEEE test systems”, the simulation results show the effectiveness of the suggested method over other algorithms in terms of minimizing of production cost and suggesting excellent scheduling of units.
Operation cost reduction in unit commitment problem using improved quantum bi...IJECEIAES
Unit Commitment (UC) is a nonlinear mixed integer-programming problem. UC used to minimize the operational cost of the generation units in a power system by scheduling some of generators in ON state and the other generators in OFF state according to the total power outputs of generation units, load demand and the constraints of power system. This paper proposes an Improved Quantum Binary Particle Swarm Optimization (IQBPSO) algorithm. The tests have been made on a 10-units simulation system and the results show the improvement in an operation cost reduction after using the proposed algorithm compared with the ordinary Quantum Binary Particle Swarm Optimization (QBPSO) algorithm
This paper proposed the integration of solar energy resources into the conventional unit commitment. The growing concern about the depletion of fossil fuels increased the awareness on the importance of renewable energy resources, as an alternative energy resources in unit commitment operation. However, the present renewable energy resources are intermitted due to unpredicted photovoltaic output. Therefore, Ant Lion Optimizer (ALO) is proposed to solve unit commitment problem in smart grid system with consideration of uncertainties .ALO is inspired by the hunting appliance of ant lions in natural surroundings. A 10-unit system with the constraints, such as power balance, spinning reserve, generation limit, minimum up and down time constraints are considered to prove the effectiveness of the proposed method. The performance of proposed algorithm are compared with the performance of Dynamic Programming (DP). The results show that the integration of solar energy resources in unit commitment scheduling can improve the total operating cost significantly.
Effects of Different Parameters on Power System Transient Stability StudiesPower System Operation
The transient stability studies plays a vital role in
providing secured operating configurations in power systems.
This paper shows an analysis of the effects of various parameters
on the transient stability studies of power system. The various
parameters for which the analysis is presented include the Fault
Clearing Time (FCT), Fault location, load increasing, machine
damping coefficient D, and Generator Armature Resistance
GAR. Under the condition that the power system is subjected to a
three-phase short-circuit fault, the Critical Clearing Time (CCT)
is calculated using numerical integration method. The analysis
has been carried out on the IEEE 30-bus test system. From this
analysis, we can conclude the importance of these different
parameters on power system transient stability studies.
Economic Dispatch using Quantum Evolutionary Algorithm in Electrical Power S...IJECEIAES
Unpredictable increase in power demands will overload the supply subsystems and insufficiently powered systems will suffer from instabilities, in which voltages drop below acceptable levels. Additional power sources are needed to satisfy the demand. Small capacity distributed generators (DGs) serve for this purpose well. One advantage of DGs is that they can be installed close to loads, so as to minimise loses. Optimum placements and sizing of DGs are critical to increase system voltages and to reduce loses. This will finally increase the overall system efficiency. This work exploits Quantum Evolutionary Algorithm (QEA) for the placements and sizing. This optimisation targets the cheapest generation cost. Quantum Evolutionary Algorithm is an Evolutionary Algorithm running on quantum computing, which works based on qubits and states superposition of quantum mechanics. Evolutionary algorithm with qubit representation has a better characteristic of diversity than classical approaches, since it can represent superposition of states.
Cost Aware Expansion Planning with Renewable DGs using Particle Swarm Optimiz...IJERA Editor
This Paper is an attempt to develop the expansion-planning algorithm using meta heuristics algorithms. Expansion Planning is always needed as the power demand is increasing every now and then. Thus for a better expansion planning the meta heuristic methods are needed. The cost efficient Expansion planning is desired in the proposed work. Recently distributed generation is widely researched to implement in future energy needs as it is pollution free and capability of installing it in rural places. In this paper, optimal distributed generation expansion planning with Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) for identifying the location, size and type of distributed generator for future demand is predicted with lowest cost as the constraints. Here the objective function is to minimize the total cost including installation and operating cost of the renewable DGs. MATLAB based `simulation using M-file program is used for the implementation and Indian distribution system is used for testing the results.
Comparison of backstepping, sliding mode and PID regulators for a voltage inv...IJECEIAES
In the present paper, an efficient and performant nonlinear regulator is designed for the control of the pulse width modulation (PWM) voltage inverter that can be used in a standalone photovoltaic microgrid. The main objective of our control is to produce a sinusoidal voltage output signal with amplitude and frequency that are fixed by the reference signal for different loads including linear or nonlinear types. A comparative performance study of controllers based on linear and non-linear techniques such as backstepping, sliding mode, and proportional integral derivative (PID) is developed to ensure the best choice among these three types of controllers. The performance of the system is investigated and compared under various operating conditions by simulations in the MATLAB/Simulink environment to demonstrate the effectiveness of the control methods. Our investigation shows that the backstepping controller can give better performance than the sliding mode and PID controllers. The accuracy and efficiency of the proposed backstepping controller are verified experimentally in terms of tracking objectives.
Solar PV parameter estimation using multi-objective optimisationjournalBEEI
The estimation of the electrical model parameters of solar PV, such as light-induced current, diode dark saturation current, thermal voltage, series resistance, and shunt resistance, is indispensable to predict the actual electrical performance of solar photovoltaic (PV) under changing environmental conditions. Therefore, this paper first considers the various methods of parameter estimation of solar PV to highlight their shortfalls. Thereafter, a new parameter estimation method, based on multi-objective optimisation, namely, Non-dominated Sorting Genetic Algorithm-II (NSGA-II), is proposed. Furthermore, to check the effectiveness and accuracy of the proposed method, conventional methods, such as, ‘Newton-Raphson’, ‘Particle Swarm Optimisation, Search Algorithm, was tested on four solar PV modules of polycrystalline and monocrystalline materials. Finally, a solar PV module photowatt PWP201 has been considered and compared with six different state of art methods. The estimated performance indices such as current absolute error matrics, absolute relative power error, mean absolute error, and P-V characteristics curve were compared. The results depict the close proximity of the characteristic curve obtained with the proposed NSGA-II method to the curve obtained by the manufacturer’s datasheet.
Security constrained optimal load dispatch using hpso technique for thermal s...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Locational marginal pricing framework in secured dispatch scheduling under co...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Optimal Unit Commitment Based on Economic Dispatch Using Improved Particle Sw...paperpublications3
Abstract: In this paper, an algorithm to solve the optimal unit commitment problem under deregulated environment has been proposed using Particle Swarm Optimization (PSO) intelligent technique accounting economic dispatch constraints. In the present electric power market, where renewable energy power plants have been included in the system, there is a lot of unpredictability in the demand and generation. This paper presents an improved particle swarm optimization algorithm (IPSO) for power system unit commitment with the consideration of various constraints. IPSO is an extension of the standard particle swarm optimization algorithm (PSO) which uses more particles information to control the mutation operation, and is similar to the social society in that a group of leaders could make better decisions. The program was developed in MATLAB and the proposed method implemented on IEEE 14 bus test system.
Security Constrained UCP with Operational and Power Flow ConstraintsIDES Editor
An algorithm to solve security constrained unit
commitment problem (UCP) with both operational and power
flow constraints (PFC) have been proposed to plan a secure
and economical hourly generation schedule. This proposed
algorithm introduces an efficient unit commitment (UC)
approach with PFC that obtains the minimum system
operating cost satisfying both unit and network constraints
when contingencies are included. In the proposed model
repeated optimal power flow for the satisfactory unit
combinations for every line removal under given study period
has been carried out to obtain UC solutions with both unit and
network constraints. The system load demand patterns have
been obtained for the test case systems taking into account of
the hourly load variations at the load buses by adding
Gaussian random noises. In this paper, the proposed
algorithm has been applied to obtain UC solutions for IEEE
30, 118 buses and Indian utility practical systems scheduled
for 24 hours. The algorithm and simulation are carried
through Matlab software and the results obtained are quite
encouraging.
The optimal solution for unit commitment problem using binary hybrid grey wol...IJECEIAES
The aim of this work is to solve the unit commitment (UC) problem in power systems by calculating minimum production cost for the power generation and finding the best distribution of the generation among the units (units scheduling) using binary grey wolf optimizer based on particle swarm optimization (BGWOPSO) algorithm. The minimum production cost calculating is based on using the quadratic programming method and represents the global solution that must be arriving by the BGWOPSO algorithm then appearing units status (on or off). The suggested method was applied on “39 bus IEEE test systems”, the simulation results show the effectiveness of the suggested method over other algorithms in terms of minimizing of production cost and suggesting excellent scheduling of units.
Operation cost reduction in unit commitment problem using improved quantum bi...IJECEIAES
Unit Commitment (UC) is a nonlinear mixed integer-programming problem. UC used to minimize the operational cost of the generation units in a power system by scheduling some of generators in ON state and the other generators in OFF state according to the total power outputs of generation units, load demand and the constraints of power system. This paper proposes an Improved Quantum Binary Particle Swarm Optimization (IQBPSO) algorithm. The tests have been made on a 10-units simulation system and the results show the improvement in an operation cost reduction after using the proposed algorithm compared with the ordinary Quantum Binary Particle Swarm Optimization (QBPSO) algorithm
This paper proposed the integration of solar energy resources into the conventional unit commitment. The growing concern about the depletion of fossil fuels increased the awareness on the importance of renewable energy resources, as an alternative energy resources in unit commitment operation. However, the present renewable energy resources are intermitted due to unpredicted photovoltaic output. Therefore, Ant Lion Optimizer (ALO) is proposed to solve unit commitment problem in smart grid system with consideration of uncertainties .ALO is inspired by the hunting appliance of ant lions in natural surroundings. A 10-unit system with the constraints, such as power balance, spinning reserve, generation limit, minimum up and down time constraints are considered to prove the effectiveness of the proposed method. The performance of proposed algorithm are compared with the performance of Dynamic Programming (DP). The results show that the integration of solar energy resources in unit commitment scheduling can improve the total operating cost significantly.
Effects of Different Parameters on Power System Transient Stability StudiesPower System Operation
The transient stability studies plays a vital role in
providing secured operating configurations in power systems.
This paper shows an analysis of the effects of various parameters
on the transient stability studies of power system. The various
parameters for which the analysis is presented include the Fault
Clearing Time (FCT), Fault location, load increasing, machine
damping coefficient D, and Generator Armature Resistance
GAR. Under the condition that the power system is subjected to a
three-phase short-circuit fault, the Critical Clearing Time (CCT)
is calculated using numerical integration method. The analysis
has been carried out on the IEEE 30-bus test system. From this
analysis, we can conclude the importance of these different
parameters on power system transient stability studies.
Economic Dispatch using Quantum Evolutionary Algorithm in Electrical Power S...IJECEIAES
Unpredictable increase in power demands will overload the supply subsystems and insufficiently powered systems will suffer from instabilities, in which voltages drop below acceptable levels. Additional power sources are needed to satisfy the demand. Small capacity distributed generators (DGs) serve for this purpose well. One advantage of DGs is that they can be installed close to loads, so as to minimise loses. Optimum placements and sizing of DGs are critical to increase system voltages and to reduce loses. This will finally increase the overall system efficiency. This work exploits Quantum Evolutionary Algorithm (QEA) for the placements and sizing. This optimisation targets the cheapest generation cost. Quantum Evolutionary Algorithm is an Evolutionary Algorithm running on quantum computing, which works based on qubits and states superposition of quantum mechanics. Evolutionary algorithm with qubit representation has a better characteristic of diversity than classical approaches, since it can represent superposition of states.
Cost Aware Expansion Planning with Renewable DGs using Particle Swarm Optimiz...IJERA Editor
This Paper is an attempt to develop the expansion-planning algorithm using meta heuristics algorithms. Expansion Planning is always needed as the power demand is increasing every now and then. Thus for a better expansion planning the meta heuristic methods are needed. The cost efficient Expansion planning is desired in the proposed work. Recently distributed generation is widely researched to implement in future energy needs as it is pollution free and capability of installing it in rural places. In this paper, optimal distributed generation expansion planning with Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) for identifying the location, size and type of distributed generator for future demand is predicted with lowest cost as the constraints. Here the objective function is to minimize the total cost including installation and operating cost of the renewable DGs. MATLAB based `simulation using M-file program is used for the implementation and Indian distribution system is used for testing the results.
Comparison of backstepping, sliding mode and PID regulators for a voltage inv...IJECEIAES
In the present paper, an efficient and performant nonlinear regulator is designed for the control of the pulse width modulation (PWM) voltage inverter that can be used in a standalone photovoltaic microgrid. The main objective of our control is to produce a sinusoidal voltage output signal with amplitude and frequency that are fixed by the reference signal for different loads including linear or nonlinear types. A comparative performance study of controllers based on linear and non-linear techniques such as backstepping, sliding mode, and proportional integral derivative (PID) is developed to ensure the best choice among these three types of controllers. The performance of the system is investigated and compared under various operating conditions by simulations in the MATLAB/Simulink environment to demonstrate the effectiveness of the control methods. Our investigation shows that the backstepping controller can give better performance than the sliding mode and PID controllers. The accuracy and efficiency of the proposed backstepping controller are verified experimentally in terms of tracking objectives.
Solar PV parameter estimation using multi-objective optimisationjournalBEEI
The estimation of the electrical model parameters of solar PV, such as light-induced current, diode dark saturation current, thermal voltage, series resistance, and shunt resistance, is indispensable to predict the actual electrical performance of solar photovoltaic (PV) under changing environmental conditions. Therefore, this paper first considers the various methods of parameter estimation of solar PV to highlight their shortfalls. Thereafter, a new parameter estimation method, based on multi-objective optimisation, namely, Non-dominated Sorting Genetic Algorithm-II (NSGA-II), is proposed. Furthermore, to check the effectiveness and accuracy of the proposed method, conventional methods, such as, ‘Newton-Raphson’, ‘Particle Swarm Optimisation, Search Algorithm, was tested on four solar PV modules of polycrystalline and monocrystalline materials. Finally, a solar PV module photowatt PWP201 has been considered and compared with six different state of art methods. The estimated performance indices such as current absolute error matrics, absolute relative power error, mean absolute error, and P-V characteristics curve were compared. The results depict the close proximity of the characteristic curve obtained with the proposed NSGA-II method to the curve obtained by the manufacturer’s datasheet.
IOSR Journal of Humanities and Social Science is an International Journal edited by International Organization of Scientific Research (IOSR).The Journal provides a common forum where all aspects of humanities and social sciences are presented. IOSR-JHSS publishes original papers, review papers, conceptual framework, analytical and simulation models, case studies, empirical research, technical notes etc.
Dosimetric evaluation of the MLCs for irregular shaped radiation fieldsIOSR Journals
The three-dimensional conformal radiotherapy (3D-CRT), intensity-modulated radiotherapy (IMRT), and image-guided radiotherapy (IGRT) are the most advanced techniques in radiotherapy, which use irregular fields–using multileaf collimators in a linear accelerator. The accuracy of these techniques depends on dosimetric characteristics of the multileaf collimators. There is an option for optimizing the jaws to the irregular MLC field to reduce the scattered radiation and intra- and inter-leaf radiation leakage beyond the field. In this study, ,80 leaf MLC system has been taken to compare and differentiate their characteristics with 6-MV, and 10-MV photon beams.
The MLC system in Elekta linear accelerator is used as a separate unit, that is, The dosimetric characteristics include dose rates, percentage depth doses, surface dose, dose in the build-up region, penumbra, and width of 50% dose levels
Bayesian Estimation of Above-Average Performance in Tertiary Institutions: A ...IOSR Journals
Bayesian approach for parameter estimation has the capacity to yield more precise estimates than methods based on sampling theory. There are several common Bayesian models; in this study we applied Empirical Bayes (EB) model called Beta-binomial model. The study is motivated by the need to beam searchlight on universities, faculties or fields of study with graduates who may not be eligible for further educational pursuits. This study provides means of assessment or a basis of evaluation of students’ performances among faculties or fields of study and overall performance of a university. This study uses Bayesian methods of inference to estimate the proportion of above-average performance of graduates from the various faculties in University of Lagos. The model adopted generated results which are of smaller variances compared with variances of sample Proportions, showing that the posterior proportions generated are more efficient estimators. This is further evidenced in narrow widths of the computed confidence intervals. The overall result shows that the proportion of above-average performance of graduates of University of Lagos, who are eligible for further educational pursuits (i.e. higher degrees), is approximately 72% of the university graduates
IOSR Journal of Humanities and Social Science is an International Journal edited by International Organization of Scientific Research (IOSR).The Journal provides a common forum where all aspects of humanities and social sciences are presented. IOSR-JHSS publishes original papers, review papers, conceptual framework, analytical and simulation models, case studies, empirical research, technical notes etc.
Evaluation Of Analgesic And Anti Inflammatory Activity Of Siddha Drug Karuvil...IOSR Journals
The present study was carried out to validate the anti-inflammatory and analgesic activities of Karuvilanchi ver chooranam (KVC) (Root powder of Smilax zeylanica) in rodents. Analgesic study was carried out by using Eddy’s Hotplate method and acetic acid-induced writhing test and Anti inflammatory study was evaluated by Cotton pellet granuloma method and by plethysmometer method. The result of the analgesic activity evaluated using hot plate method revealed that the reaction time for mice was significantly increased in a dose dependent manner after one hour of oral administration. It was found that both KVC and Aspirin caused an inhibition on the writhing response induced by acetic acid. Doses of 250 and 500 mg/kg of the KVC and aspirin respectively, could completely block the writhing response exhibited about 61.51 and 72.51% inhibition. In acute inflammation model, the formalin induced paw oedema was significantly reduced by all the doses of KVC used when compared to control (P<0.05). The results of cotton pellet granuloma method indicated that KVC in both doses significantly reduced the weight of the cotton pellet granuloma with a dose dependent effect. From the result it can be concluded that the trial drug Karuvilanchi Ver Chooranam has potent analgesic and anti inflammatory properties which confirmed the traditional use
IOSR Journal of Applied Chemistry (IOSR-JAC) is an open access international journal that provides rapid publication (within a month) of articles in all areas of applied chemistry and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Chemical Science. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Load shedding in power system using the AHP algorithm and Artificial Neural N...IJAEMSJORNAL
This paper proposes the load shedding method based on considering the load importance factor, primary frequency adjustment, secondary frequency adjustment and neuron network. Consideration the process of primary frequency control, secondary frequency control helps to reduce the amount of load shedding power and restore the system’s frequency to the permissible range. The amount of shedding power of each load bus is distributed based on the load importance factor. Neuron network is applied to distribute load shedding strategies in the power system at different load levels. The experimental and simulated results on the IEEE 37- bus system present the frequency can restore to allowed range and reduce the damage compared to the traditional load shedding method using under frequency relay- UFLS.
Security constrained optimal load dispatch using hpso technique for thermal s...eSAT Journals
Abstract This paper presents Hybrid Particle Swarm Optimization (HPSO) technique to solve the Optimal Load Dispatch (OLD) problems with line flow constrain, bus voltage limits and generator operating constraints. In the proposed HPSO method both features of EP and PSO are incorporated, so the combined HPSO algorithm may become more effective to find the optimal solutions. In this paper, the proposed Hybrid PSO, PSO and EP techniques have been tested on IEEE14, 30 bus systems. Numerical simulation results show that the Hybrid PSO algorithm outperformed standard PSO algorithm and Evolution Programming method on the same problem and can save considerable cost of Optimal Load Dispatch.
This paper discusses the possible applications of particle swarm optimization (PSO) in the Power system. One of the problems in Power System is Economic Load dispatch (ED). The discussion is carried out in view of the saving money, computational speed – up and expandability that can be achieved by using PSO method. The general approach of the method of this paper is that of Dynamic Programming Method coupled with PSO method. The feasibility of the proposed method is demonstrated, and it is compared with the lambda iterative method in terms of the solution quality and computation efficiency. The experimental results show that the proposed PSO method was indeed capable of obtaining higher quality solutions efficiently in ED problems.
This paper presents the implementation of multiple distributed generations planning in distribution system using computational intelligence technique. A pre-developed computational intelligence optimization technique named as Embedded Meta EP-Firefly Algorithm (EMEFA) was utilized to determine distribution loss and penetration level for the purpose of distributed generation (DG) installation. In this study, the Artificial Neural Network (ANN) was used in order to solve the complexity of the multiple DG concepts. EMEFA-ANN was developed to optimize the weight of the ANN to minimize the mean squared error. The proposed method was validated on IEEE 69 Bus distribution system with several load variations scenario. The case study was conducted based on the multiple unit of DG in distribution system by considering the DGs are modeled as type I which is capable of injecting real power. Results obtained from the study could be utilized by the utility and energy commission for loss reduction scheme in distribution system.
Passerine swarm optimization algorithm for solving optimal reactive power dis...IJAAS Team
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Stable Multi Optimized Algorithm Used For Controlling The Load Shedding Problems In Power Systems
1. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE)
e-ISSN: 2278-1676,p-ISSN: 2320-3331, Volume 5, Issue 3 (Mar. - Apr. 2013), PP 81-90
www.iosrjournals.org
www.iosrjournals.org 81 | Page
Stable Multi Optimized Algorithm Used For Controlling The
Load Shedding Problems In Power Systems
Nila P Divakaran, A Dyaneswaran,M.E.,
PG Scholar in Power Systems Engineering Vivekanandha College of Engineering for Women Elayampalayam,
Tamilnadu
Assistant Professor in EEE Dept ,Vivekanandha College of Engineering for Women Elayampalayam, Tamilnadu
Abstract: This paper presents new approaches to update our existing load shedding scheme, planned for the
coming years. If generation in power system is insufficient to power all loads, efficient load shedding operations
may need to be deployed to maintain the supply demand balance. This paper proposes a distributed multi agent
based PSO and GA, which can make the efficient load shedding decision based on the global information. This
method integrates the multi agent system and PSO,GA algorithm. In order to obtain optimal solution quickly,
each agent competes and corporate with its neighbours, and it can also learn by using its knowledge. According
to this algorithm, total net active power and operating status of loads can be discovered accurately even with
faults. For further improvement, can apply genetic algorithm for controlling purposes, compare both the
methods.
Keywords: Multi Agent System, Particle swarm optimization, Genetic Algorithm, Load shedding, Voltage
Stability
I. INTRODUCTION
The Objective of Power Systems designs are high power, electrical utility applications, industrial and
commercial applications. Power Management is the aspect of managing the electrical loads such as available
power loads are not overloaded The important objective of power systems design for high power, electrical
utility applications, industrial and commercial applications. Power management is the aspect of (1) managing
the electrical loads such as available power loads are not overloaded. (2) Power is allocated to the different loads
such that the loads are receiving a proper allocation of power. Power management gives the good idea about
Electrical Demand and Supply.
Power Management Systems (PMS) are essential for safe, efficient and reliable operation of industrial
power systems. The functionality of the PMS suite includes load-shedding, power sharing, network
synchronization and power restoration. PMS solutions protect and optimize the stability of industrial power
systems in disturbance situations by ensuring power sharing between generators when the industrial power
system is islanded from the grid. These solutions also ensure that the generators meet the required power
demand when the network is connected to the grid. The load shedding functionality ensures power availability
to critical process loads by dropping less critical loads.
The load shedding problem has a significant influence on secure and economic operation of power
systems. Suppose any faults, sudden load change, and insufficient generation can create power mismatch
between generation and loads. Load shedding is the process of tripping certain amount of load with lower
priority to maintain the stability of the remaining portion of system.
Different methods have been proposed for load shedding. Multi Agent System is one of the most
popular distributed control solutions. This control scheme requires the collection and transmission of global
information. Advantages of MAS include the ability to survive single point failures and decentralized data
processing, which leads to efficient task distribution. It causing faster operation and decision making process
The Multi Agent system and Particle Swarm Optimization are integrated to form the proposed MAPSO
method for solving load shedding problem. In MAPSO not only a solution to the optimization problem but also
a particle to PSO. According to this method, two agents will communicate with each other only if their
corresponding buses are connected. Through information exchanges, each agent can discover necessary global
information for load shedding decision making. PSO is used to optimize the information exchange. Then the
load shedding activities of all agents can be coordinated. Find out the optimum value of the load to be shed.
Genetic Algorithm (GA) is a global optimization algorithm derived from evolution and natural
selection. Although genetic algorithm cannot always provide optimal solution, it has its own advantages (Liu
yong, Kang lishan & Chen yuping. 1997) and is a powerful tool for solving complex problems (Xi yugeng, Chai
tianyou & Yun weimin. 1996).
2. Stable Multi Optimized Algorithm Used For Controlling The
www.iosrjournals.org 82 | Page
The basic thought of Genetic algorithm:
1) Randomly producing a original population whose number of individuals is a constant N.
2) Producing next generation by crossing over and mutation among individuals.
3) Forming the new population of N individuals from the generation
4) Producing the next population by repeating the step2
Multi Agent Based PSO is used for controlling the load shedding problem. Faults, sudden load change
and insufficient generation can create major problems between generation and loads. If generation is
insufficient, efficient load shedding operations may need to be maintaining the supply demand balance. It
focuses interactions between a utility company and its customers/users. This is the Diagram for the ILS sceme
Figure 1 complete load shedding scheme
This method proposed to determine the most appropriate loads to be shed during under frequency and
under voltage condition. Total net active power can be calculating and operating status of loads can be
estimating accurately even with faults. It based on the discovered information; coordinated Load shedding
decision can be made
II. PROBLEM FORMULATION
Load shedding problem can be formulated as an optimization problem with the following objective
function and constraints:
Objective function:
The objective function (OBF) of the load shedding problem is to minimize the sum of curtailed load
during generation outage conditions. It can be expressed mathematically as:
Nbus
min f (t) = ∑ (α i. Δpdi2
+ β i. Δqdi2
)…………(1)
i=1
Where αi and βi are the weight factors for curtailed active and reactive power load of the ith bus and Nbus is the
number of buses in the transmission system. ∆Pdi and ∆Qdi are the curtailed active and reactive power load of
the ith transmission system.
Constraints:
The constraints can be listed as follows:
• Power flow balance equations:
N
Pgi – Pdio – Δpdi- Vi ∑ Vj Yij cos (δi – δj –θij) =0…… (2)
J=1
N
Qgi – Qdio – ΔQdi- Vi ∑ Vj Yij cos (δi – δj –θij) =0….. (3)
J=1
Pgi and Qgi are active and reactive power generations at the ith bus. Pdi0 and Qdi0 are initial active and
reactive power load of the ith bus. V‟s and δ‟s, are system bus voltages magnitudes and phase angles. Yij and θij
are bus admittance matrix elements.
• Maintaining the original load power factor:
To maintain the original load power factor ΔQdi is selected as:
3. Stable Multi Optimized Algorithm Used For Controlling The
www.iosrjournals.org 83 | Page
ΔQdi = ΔPdi [ΔQdio/ ΔPdio]………………………. (4)
• Generators active and reactive power limits:
Pgimin
< Pgi<Pgimax
i=1…No..............(5)
Qgimin
< Qgi<Qgimax
i=1…No………. (6)
• Voltage range limits:
Vimin
≤Vi≤Vimax
………………………………….. (7)
• Line loading limits:
|δi- δj|≤εij ……………………………………….. (8)
Where δi and δj are the voltage angles at bus i and bus j, and εij is the maximum voltage phase angle difference
between buses i and j.
III. MULTI AGENT BASED PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM
A. Particle Swarm Optimization
PSO simulates the behaviors of bird flocking. Suppose the following scenario: a group of birds are
randomly searching food in an area. There is only one piece of food in the area being searched. All the birds do
not know where the food is. But they know how far the food is in each iteration. The effective one is to follow
the bird, which is nearest to the food. PSO learned from the scenario and used it to solve the optimization
problems.
PSO is initialized with a group of random particles and then searches for optima by updating
generations. In every iteration, each particle is updated by following two "best" values. The first one is the best
solution (fitness) it has achieved so far. (The fitness value is also stored.) This value is called pbest. Another
"best" value that is tracked by the particle swarm optimizer is the best value, obtained so far by any particle in
the population.
This best value is a global best and called g-best. When a particle takes part of the population as its
topological neighbors, the best value is a local best and is called p-best. After finding the two best values, the
particle updates its velocity and positions with following equation.
Vi (u+1) =w *Vi (u) +C1*rand ( )*(pbest i –Pi (u)) +C2*
rand ( )*(gbesti –Pi (u)) …………………………………. (9)
Pi (u+1) = Pi (u) + Vi (u+1)…………………………….. (10)
In the above equation,
The term rand ( )*(pbesti –Pi (u)) is called particle memory influence
The term rand ( )*(gbesti –Pi (u)) is called swarm influence
Vi (u) which is the velocity of ith particle at iteration „u‟ must lie in the range
Vmin ≤ Vi(u) ≤ Vmax
• The parameter Vmax determines the resolution, or fitness, with which regions are to be searched between the
present position and the target position
• .If Vmax is too high; particles may fly past good solutions. If Vmin is too small, particles may not explore
sufficiently beyond local solutions.
• In many experiences with PSO, Vmax was often set at 10-20% of the dynamic range on each dimension.
• The constants C1and C2 pull each particle towards pbest and gbest positions.
• Low values allow particles to roam far from the target regions before being tugged back. On the other hand,
high values result in abrupt movement towards, or past, target regions.
4. Stable Multi Optimized Algorithm Used For Controlling The
www.iosrjournals.org 84 | Page
• The acceleration constants C1 and C2 are often set to be 2.0 according to past experiences
.• Suitable selection of inertia weight ‘ω‟ provides a balance between global and local explorations, thus
requiring less iteration on average to find a sufficiently optimal solution.
• In general, the inertia weight w is set according to the following equation,
W = Wmax - [ Wmax – Wmin / ITERmax] *ITER…………. (11)
Where w -is the inertia weighting factor
Wmax - maximum value of weighting factor
Wmin - minimum value of weighting factor
ITERmax - maximum number of iterations
ITER - current number of iteration
The PSO algorithm can be best described in general as follows:
1) For each particle, the position and velocity vectors will be randomly initialized with the same size as the
problem dimension.
2) Measure the fitness of each particle (pbest) and store the particle with the best fitness (gbest) value.
3) Update velocity and position vectors for each particle.
4) Repeat steps until a termination criterion is satisfied.
On the other hand, some advantages of aforementioned algorithms over PSO are the following:
• The availability of commercial versions of some algorithms like Matlab (genetic algorithm) and Excel
premium solver (evolutionary programming).
• The extensive collection of books and research literatures, especially in the case of genetic algorithm and
evolutionary programming, which cover these competing methods. Despite the simplicity of the PSO concept
and implementation, its superiority is proven when compared with other techniques in many different
application areas.
B. Genetic Algorithm
Genetic algorithm is a search method that employs processes found in natural biological evolution.
These algorithms search or operate on a given population of potential solutions to find those that approach some
specification or criteria. To do this, the genetic algorithm applies the principle of survival of the fittest to find
better and better approximations. At each generation, a new set of approximations is created by the process of
selecting individual potential solutions (individuals) according to their level of fitness in the problem domain
and breeding them together using operators borrowed from natural genetics. This process leads to the evolution
of population of individuals that are better suited to their environment than the individuals that they were created
from, just as in natural adaptation.
C. Multi agent System
Agents have a certain level of autonomy, which means that they can take decisions without a central
controller or commander. To achieve this, they are driven by a set of tendencies. For a battery system a tendency
could be: “charge the batteries when the price for the kWh is low and the state of charge is low, too”. Thus, the
MAS decide when to start charging based on its own rules and goals and not by an external command. In
addition, the autonomy of every agent is related to the resources that it possesses and uses. These resources
could be the available fuel for a diesel generator.
Another significant characteristic of the agents is that they have partial or none at all representation of
the environment. For example, in a power system the agent of a generator knows only the voltage level of its
own bus and, maybe, it can estimate what is happening in certain specific buses. However, the agent does not
know what is happening in the whole system. This is the core of the MAS technology, since the goal is to
control a very complicated system with minimum data exchange and minimum computational demands.
Finally, another significant characteristic is that an agent has a certain behavior and tends to satisfy
certain objectives using its resources, skills and services. An example of these skills could be the ability to
produce or store power and an example for the services could be the ability to sell power in a market. The way
that the agent uses the resources, skills and services characterizes its behavior. As a consequence, it is obvious
that the behavior of every agent is formed by its goals. An agent that controls a battery system and its goal is to
supply uninterruptible power to a load will have different behavior from a similar battery, whose primary goal is
to maximize profits by bidding in the energy market.
MAS technology provides an opportunity to compute and optimize many complicated problems.
Agents in MAS act collectively as a society and they collaborate (or compete) to achieve their own individual
5. Stable Multi Optimized Algorithm Used For Controlling The
www.iosrjournals.org 85 | Page
goals as well as the common goal. This cooperative and competitive feature matches the essential nature of a
particle in PSO. Hence, this paper combines PSO and MAS to form a new optimal algorithm.
In general, the following four elements should be defined when MAS is used to solve problems:
a) The meaning and the purpose of each agent in MAS;
b) An environment where all agents live;
c) The definition of a local environment;
d) A set of behavioral rules, governing the interaction between the agents and their environment. They are the
laws of the agent universe.
D. Multi Agent based PSO and Genetic Algorithm
Enlightened by multi agent system and PSO, this paper integrates multi agent system and PSO to form
a multi agent-based PSO approach (MAPSO), for solving the optimization problem. In MAPSO, an agent
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 obtain optimal solution quickly, they compete and cooperate with their neighbors, and they
can also use knowledge. Making use of these agent–agent interactions and evolution mechanism of PSO in a
lattice-like environment, the proposed method can find high-quality solutions reliably with the faster
convergence characteristics in a reasonably good computation time.
In this study, MAS and PSO are integrated to form the proposed MAPSO method for solving reactive
power optimization dispatch. In MAPSO, an agent represents not only a candidate solution to the optimization
problem but also a particle to PSO. Firstly, a lattice-like environment is constructed, with each agent fixed on a
lattice-point.
Multi Agent based Genetic Algorithms (MAGA) have recently received much attention as robust
stochastic search algorithms for optimization problems. GAs are blind search technique using stochastic
operations based on the mechanics of the survival of the fittest. It also works with a population of individuals
rather than single point. Operation, involve random number generation (mutation), string copying
(reproduction), and partial string exchange (crossover). Each string represents a possible solution. It starts with
the formulation of the “fitness function”, which represents the objective function for the problem. Based on the
fitness of the population strings, two parent strings are selected probabilistically in the reproduction process.
Two child strings are then generated from the parent strings in the process of crossover by complementing the
child strings at selected bit positions.
In order to obtain optimal solution quickly, each agent competes and cooperates with their neighbors,
and they can also use knowledge to obtain high-quality optimal solution by self-learning. Making use of
evolution mechanism of PSO, it can speed up the transfer of information among agents, and the proposed
MAPSO method can realize the purpose of optimizing the value of objective function.
: In MAPSO, an agent represents a candidate solution to the optimization problem in hand and is a
particle to PSO. Hence, agent has a fitness value to the optimization problem. For solving reactive power
problem, its fitness value is the value of the active power loss in the transmission network
f (α) = fQ = ∑ Pkloss = ∑ gk ( Vi2
+ Vj2
– 2 Vi Vj cos θij)
k εNR k εNR ……………..(12)
The purpose of is to minimize the real power transmission losses and keep all the voltages within the
limits as much as possible. In reactive power optimization problem, each agent carries all control variables to be
optimized.
In MAS, all agents live in an environment. An environment is organized as a lattice-like structure in
Fig. In the environment, each agent is fixed on a lattice-point and each circle represents an agent; the data in
circle represents its position in the environment. Due to representation of a particle in PSO, each agent in its
database contains two data, i.e., particle‟s current velocity and its coordinates in the search space.
Since each agent can only sense its local environment in MAS, the definition of the local environment
is very important in the proposed method.
The neighbors of αij and Nij is defined as
Ni,j = { αi1,j , αi,j 1 , αi2,j , αi,j 2 }…………(13)
Where
i1
= i - 1 i ≠ 1 , j – 1 j ≠ 1
j1
= ..........(14)
Lsize i = 1 Lsize j = 1
6. Stable Multi Optimized Algorithm Used For Controlling The
www.iosrjournals.org 86 | Page
i2
= i + 1 i ≠ Lsize , j + 1 i ≠ Lsize
j2
= …....(15)
1 i = Lsize 1 i = Lsize
To quickly and accurately achieve its purposes, each agent has some behaviors. In MAPSO, each agent
firstly competes and cooperates with its neighbors to diffuse its useful information to the whole environment,
and it can also use evolution mechanism of PSO and its knowledge. On the basis of such behaviors, three
operators are designed for the agents
II. MAPSO AND GA BASED LOAD SHEDDING STRATEGY
As mentioned in previous sections, some load could be shed in emergency condition to protect the
other load. But it is difficult for dispatcher to determine which load and how many could be shedding. Because
load shedding bring up large economic lost, dispatcher must try his best to protect majority load. Therefore, an
algorithm of load shedding could be a great help to the dispatcher. It is noticed that the penalty function in PSO
could be considered as the load shedding in a power system which is the penalty for maintaining the safe
operation of the power system, and the load shedding strategy can be optimized with the PSO.
The mathematical model of this new strategy is:
min f (P,Q) = ∑ Pgi - ∑ Plj + ∑ wlj (Plj – Plj‟)…….. (16)
i ε 1 j ε L j ε L
n
. Pi = Vi ∑ Vj (Gij cos θij + Bij sin θi)…………… (17)
i=1
n
. Qi = Vi ∑ Vj (Gij sin θij - Bij cos θi)…………… (18)
i=1
Pgimin
< Pgi<Pgimax
……………………………. (19)
Qgimin
< Qgi<Qgimax
……………………... (20)
Vmin ≤ Vi≤ Vmax……………………………… (21)
-Fimin
< Fi<Fiimax
………………………… (22)
0≤ Plj‟≤ Plj ...............................................(23)
It must be pointed that the reactive load always variable with the active load. In this paper, the reactive
load will decrease with active load with the power factor remaining unchanged. While the equality constraints
could be eliminated as explained in previous section, some of the inequality constraints could be handled easier
than being added to the aim function as a penalty function. It is also noted that generator outputs and the loads
are control variables, and they could be bounded after updating the position of particles.
The control variable P and Q not only contains the output of generator but also the active load. Plj is
demand power at bus j, but only Plj ' is supplied, so the amount of load Plj-Plj‟ is shed, the factor wlj is the
economical lost coefficient for the load j. It must be pointed that the reactive load always variable with the
active load. In this paper, the reactive load will decrease with active load with the Power factor remaining
unchanged. Suppose x is the control variable vector consisting of P and Q, etc., and it‟s dimension is xn . For
particle i,
Let
xi = ( xi
1
, xi
2
,….xi
n
)…………………………. (24)
xi, t+1 = xi, t + vi, t+1 …………………………… (25)
Then it becomes
7. Stable Multi Optimized Algorithm Used For Controlling The
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xj
min if xi
j
< xj
min
xi
l
= xj
max if xi
j
> xj
max ……………. (26)
xj
i others
The power equation becomes
min F(x) = ∑ Pgi - ∑ Plj
iεG jεL
+ wh ∑ max ((0,hj(x)) …………………. (27)
J=1
+ ∑wlj (Plj – plj‟)
It can be seen that the aim function consists of three parts. The first part is the power loss; the second
part is the penalty of violation of constraints; and the third part is the economic cost of load shedding. The
inequality constraint now only contains voltage constraint and transmission capacity constraint.
The setting of penalty factor as above is to make a masking effect. The particle firstly flies to position
where no constraint is violated, because the penalty to constraint violation is highest. Secondly, the particle
searches the position where no load is shedding. And at last, the particle tries to decrease the power loss.
Figure 2 Flow chart
V. IMPLEMENTATION OF MAPSO AND GENETIC ALGORITHM FOR LOAD SHEDDING PROBLEM
In MAPSO and GA, many different operators are utilized to simulate the behaviors of agents, and
realize their purposes. In order to reduce the computational cost, the self-learning operator is only performed on
the agent with the minimum fitness value in each generation, but it has an important effect on the performance
8. Stable Multi Optimized Algorithm Used For Controlling The
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of MAPSO and GA. The proposed method can quickly and accurately converge to the optimal solutions for
reactive power optimal dispatch. The details of the overall algorithm are as follows.
Step 1) Input parameters of system and algorithm, and specify the lower and upper boundaries of each variable.
Step 2) Generate a lattice-like environment, and initialize randomly each agent.
Step 3) Evaluate the fitness value of each agent based on the Load Shedding analysis results and the proposed
mixed-variable handing methods.
Step 4) Update the time counter.
Step 5) Perform the neighborhood competition and cooperation operator on each agent.
Step 6) Execute the PSO operator and further adjust its position in the search space on each agent according to
the velocity equations.
Step 7) Evaluate the fitness value of each agent based on the Load Shedding analysis results and the proposed
mixed-variable handing methods.
Step 8) Find the best agent with the minimum fitness value,
and then perform the self-learning operator.
VI. SIMULATION STUDIES
In this paper Multi Agent Based PSO and GA are used for controlling the Load shedding problems.
The Load Shedding is assigned with the priority of customers and make the generation is 1000MW. Consider
each and every agent they have different priorities First assign the lower priority, obtained the corresponding
optimized Load value for the proper Load Shedding.
The consumers are represented as C1, C2, C3, C4, and C5, each consumer have different priority. It
may be lower, medium or high, consider these priorities and got the optimum value corresponding to their Load
Demand.
Total cost of Each Generation unit
total_cost = 2.6333e+003
total_cost = 4.3752e+003
total_cost = 1.3734e+004
total_cost = 2.2673e+004
total_cost = 3.7808e+004
The Load Schedule corresponding to the generation
load = 69.7300
load = 95.1410 50.8221
load = 263.4590 219.1400 336.6359
load = 414.0326 369.7136 487.2095 301.1472
load = 749.4485 705.1295 822.6254 636.5631 670.8318
The Load Shedding is assigned with the priority of customers and make the generation is 1000MW.
Consider each and every agent they have different priorities First assign the lower priority, obtained the
corresponding optimized Load value for the Load shedding.
Obtained the total cost of each generation unit of the entire system and Corresponding Load Schedule is
obtained. There are two cases, one is the system with losses and another one is system without losses. In both
cases should get the total cost and Load Schedule. In this paper result represents the values without losses, so the
error value is zero.
The consumers are represented as C1, C2, C3, C4, and C5, each consumer have different priority. It
may be lower, medium or high, consider their priorities and got the optimum value of their corresponding Load
Schedule.
9. Stable Multi Optimized Algorithm Used For Controlling The
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Figure 3 Cost Vs Iteration (30) without loss
This graph is plotted between scheduling cost and its irrespective iteration. In this method 3 generating
units are committed along with the system. This plot shows the optimum value of the fuel cost value. Number of
iteration is 30. If the number of iteration is 60, the graph shown below
Figure 4 Load Vs Iteration (without loss)
This graph shows the graph between load and respective iteration. The Load Shedding is assigned with
the priority. Make the generation is 1000MW. There are different priorities assigned with each agent. First
consider the lower priority, got the corresponding optimized Load value for the Load shedding. The graph
between net power and iteration shown below. Sometimes the load value is taken as corresponding net power of
each agent.
Figure 5 Total net Power Vs Iteration
5 10 15 20 25 30
0
2000
4000
6000
8000
10000
12000
14000
16000
iteration
cost
5 10 15 20 25 30
600
700
800
900
1000
1100
1200
iteration
load
5 10 15 20 25 30
0
50
100
150
200
250
300
350
iteration
totalpower
10. Stable Multi Optimized Algorithm Used For Controlling The
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