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.
Dynamic Economic Dispatch Assessment Using Particle Swarm Optimization TechniquejournalBEEI
This paper presents the application of particle swarm optimization (PSO) technique for solving the dynamic economic dispatch (DED) problem. The DED is one of the main functions in power system planning in order to obtain optimum power system operation and control. It determines the optimal operation of generating units at every predicted load demands over a certain period of time. The optimum operation of generating units is obtained by referring to the minimum total generation cost while the system is operating within its limits. The DED based PSO technique is tested on a 9-bus system containing of three generator bus, six load bus and twelve transmission lines.
HYBRID PARTICLE SWARM OPTIMIZATION FOR SOLVING MULTI-AREA ECONOMIC DISPATCH P...ijsc
We consider the Multi-Area Economic Dispatch problem (MAEDP) in deregulated power system
environment for practical multi-area cases with tie line constraints. Our objective is to generate allocation
to the power generators in such a manner that the total fuel cost is minimized while all operating
constraints are satisfied. This problem is NP-hard. In this paper, we propose Hybrid Particle Swarm
Optimization (HGAPSO) to solve MAEDP. The experimental results are reported to show the efficiency of
proposed algorithms compared to Particle Swarm Optimization with Time-Varying Acceleration
Coefficients (PSO-TVAC) and RCGA.
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.
GWO-based estimation of input-output parameters of thermal power plantsTELKOMNIKA JOURNAL
The fuel cost curve of thermal generators was very important in the calculation of economic dispatch and optimal power flow. Temperature and aging could make changes to fuel cost curve so curve estimation need to be done periodically. The accuracy of the curve parameters estimation strongly affected the calculation of the dispatch. This paper aims to estimate the fuel cost curve parameters by using the grey wolf optimizer method. The problem of curve parameter estimation was made as an optimization problem. The objective function to be minimized was the total number of absolute error or the difference between the actual value and the estimated value of the fuel cost function. The estimated values of parameter that produce the smallest total absolute error were the values of final solution. The simulation results showed that parameter estimation using gray wolf optimizer method further minimized the value of objective function. By using three models of fuel cost curve and given test data, parameter estimation using grey wolf optimizer method produced the better estimation results than those estimation results obtained using least square error, particle swarm optimization, genetic algorithm, artificial bee colony and cuckoo search methods.
Reduction of Active Power Loss byUsing Adaptive Cat Swarm Optimizationijeei-iaes
This paper presents, an Adaptive Cat Swarm Optimization (ACSO) for solving reactive power dispatch problem. Cat Swarm Optimization (CSO) is one of the new-fangled swarm intelligence algorithms for finding the most excellent global solution. Because of complication, sometimes conventional CSO takes a lengthy time to converge and cannot attain the precise solution. For solving reactive power dispatch problem and to improve the convergence accuracy level, we propose a new adaptive CSO namely ‘Adaptive Cat Swarm Optimization’ (ACSO). First, we take account of a new-fangled adaptive inertia weight to velocity equation and then employ an adaptive acceleration coefficient. Second, by utilizing the information of two previous or next dimensions and applying a new-fangled factor, we attain to a new position update equation composing the average of position and velocity information. The projected ACSO has been tested on standard IEEE 57 bus test system and simulation results shows clearly about the high-quality performance of the planned algorithm in tumbling the real power loss.
This paper analyses the optimal power system planning with DGs used as real and reactive power compensator. Recently planning of DG placement reactive power compensation are the major problems in distribution system. As the requirement in the power is more the DG placement becomes important. When planned to make the DG placement, cost analysis becomes as a major concern. And if the DGs operate as reactive power compensator it is most helpful in power quality maintenance. So, this paper deals with the optimal power system planning with renewable DGs which can be used as a reactive power compensators. The problem is formulated and solved using popular meta-heuristic techniques called cuckoo search algorithm (CSA) and particle swarm optimization (PSO). the comparative results are presented.
Dynamic Economic Dispatch Assessment Using Particle Swarm Optimization TechniquejournalBEEI
This paper presents the application of particle swarm optimization (PSO) technique for solving the dynamic economic dispatch (DED) problem. The DED is one of the main functions in power system planning in order to obtain optimum power system operation and control. It determines the optimal operation of generating units at every predicted load demands over a certain period of time. The optimum operation of generating units is obtained by referring to the minimum total generation cost while the system is operating within its limits. The DED based PSO technique is tested on a 9-bus system containing of three generator bus, six load bus and twelve transmission lines.
HYBRID PARTICLE SWARM OPTIMIZATION FOR SOLVING MULTI-AREA ECONOMIC DISPATCH P...ijsc
We consider the Multi-Area Economic Dispatch problem (MAEDP) in deregulated power system
environment for practical multi-area cases with tie line constraints. Our objective is to generate allocation
to the power generators in such a manner that the total fuel cost is minimized while all operating
constraints are satisfied. This problem is NP-hard. In this paper, we propose Hybrid Particle Swarm
Optimization (HGAPSO) to solve MAEDP. The experimental results are reported to show the efficiency of
proposed algorithms compared to Particle Swarm Optimization with Time-Varying Acceleration
Coefficients (PSO-TVAC) and RCGA.
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.
GWO-based estimation of input-output parameters of thermal power plantsTELKOMNIKA JOURNAL
The fuel cost curve of thermal generators was very important in the calculation of economic dispatch and optimal power flow. Temperature and aging could make changes to fuel cost curve so curve estimation need to be done periodically. The accuracy of the curve parameters estimation strongly affected the calculation of the dispatch. This paper aims to estimate the fuel cost curve parameters by using the grey wolf optimizer method. The problem of curve parameter estimation was made as an optimization problem. The objective function to be minimized was the total number of absolute error or the difference between the actual value and the estimated value of the fuel cost function. The estimated values of parameter that produce the smallest total absolute error were the values of final solution. The simulation results showed that parameter estimation using gray wolf optimizer method further minimized the value of objective function. By using three models of fuel cost curve and given test data, parameter estimation using grey wolf optimizer method produced the better estimation results than those estimation results obtained using least square error, particle swarm optimization, genetic algorithm, artificial bee colony and cuckoo search methods.
Reduction of Active Power Loss byUsing Adaptive Cat Swarm Optimizationijeei-iaes
This paper presents, an Adaptive Cat Swarm Optimization (ACSO) for solving reactive power dispatch problem. Cat Swarm Optimization (CSO) is one of the new-fangled swarm intelligence algorithms for finding the most excellent global solution. Because of complication, sometimes conventional CSO takes a lengthy time to converge and cannot attain the precise solution. For solving reactive power dispatch problem and to improve the convergence accuracy level, we propose a new adaptive CSO namely ‘Adaptive Cat Swarm Optimization’ (ACSO). First, we take account of a new-fangled adaptive inertia weight to velocity equation and then employ an adaptive acceleration coefficient. Second, by utilizing the information of two previous or next dimensions and applying a new-fangled factor, we attain to a new position update equation composing the average of position and velocity information. The projected ACSO has been tested on standard IEEE 57 bus test system and simulation results shows clearly about the high-quality performance of the planned algorithm in tumbling the real power loss.
This paper analyses the optimal power system planning with DGs used as real and reactive power compensator. Recently planning of DG placement reactive power compensation are the major problems in distribution system. As the requirement in the power is more the DG placement becomes important. When planned to make the DG placement, cost analysis becomes as a major concern. And if the DGs operate as reactive power compensator it is most helpful in power quality maintenance. So, this paper deals with the optimal power system planning with renewable DGs which can be used as a reactive power compensators. The problem is formulated and solved using popular meta-heuristic techniques called cuckoo search algorithm (CSA) and particle swarm optimization (PSO). the comparative results are presented.
Economic/Emission Load Dispatch Using Artificial Bee Colony AlgorithmIDES Editor
This paper presents an application of the
artificial bee colony (ABC) algorithm to multi-objective
optimization problems in power system. A new multiobjective
artificial bee colony (MOABC) algorithm to
solve the economic/ emission dispatch (EED) problem is
proposed in this paper. Non-dominated sorting is
employed to obtain a Pareto optimal set. Moreover, fuzzy
decision theory is employed to extract the best
compromise solution. A numerical result for IEEE 30-bus
test system is presented to demonstrate the capability of
the proposed approach to generate well-distributed
Pareto-optimal solutions of EED problem in one single
run. In addition, the EED problem is also solved using the
weighted sum method using ABC. Results obtained with
the proposed approach are compared with other
techniques available in the literature. Results obtained
show that the proposed MOABC has a great potential in
handling multi-objective optimization problem.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Optimum designing of a transformer considering lay out constraints by penalty...INFOGAIN PUBLICATION
Optimum designing of power electrical equipment and devices play a leading role in attaining optimal performance and price of equipments in electric power industry. Optimum transformer design considering multiple constraints is acquired using optimal determination of geometric parameters of transformer with respect to its magnetic and electric properties. As it is well known, every optimization problem requires an objective function to be minimized. In this paper optimum transformer design problem comprises minimization of transformers mean core mass and its windings by satisfying multiple constraints according to transformers ratings and international standards using a penalty-based method. Hybrid big bang-big crunch algorithm is applied to solve the optimization problem and results are compared to other methods. Proposed method has provided a reliable optimization solution and has guaranteed access to a global optimum. Simulation result indicates that using the proposed algorithm, transformer parameters such as core mass, efficiency and dimensions are remarkably improved. Moreover simulation time using this algorithm is quit less in comparison to other approaches.
Soft Computing Technique Based Enhancement of Transmission System Lodability ...IJERA Editor
Due to the growth of electricity demands and transactions in power markets, existing power networks need to be enhanced in order to increase their loadability. The problem of determining the best locations for network reinforcement can be formulated as a mixed discrete-continuous nonlinear optimization problem (MDCP). The complexity of the problem makes extensive simulations necessary and the computational requirement is high. This paper compares the effectiveness of Evolutionary Programming (EP) and an ordinal optimization (OO) technique is proposed in this paper to solve the MDCP involving two types of flexible ac transmission systems (FACTS) devices, namely static var compensator (SVC) and thyristor controlled series compensator (TCSC), for system loadability enhancement. In this approach, crude models are proposed to cope with the complexity of the problem and speed up the simulations with high alignment confidence. The test and Validation of the proposed algorithm are conducted on IEEE 14–bus system and 22-bus Indian system.Simulation results shows that the proposed models permit the use of OO-based approach for finding good enough solutions with less computational efforts.
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.
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 Load Dispatch Problem with Valve – Point Effect Using a Binary Bat A...IDES Editor
This paper proposes application of BAT algorithm
for solving economic load dispatch problem. BAT
algorithmic rule is predicated on the localization
characteristics of micro bats. The proposed approach has
been examined and tested with the numerical results of
economic load dispatch problems with three and five
generating units with valve - point loading without
considering prohibited operating zones and ramp rate limits.
The results of the projected BAT formula are compared with
that of other techniques such as lambda iteration, GA, PSO,
APSO, EP, ABC and basic principle. For each case, the
projected algorithmic program outperforms the answer
reported for the existing algorithms. Additionally, the
promising results show the hardness, quick convergence
and potency of the projected technique.
Multi-objective Optimization Scheme for PID-Controlled DC MotorIAES-IJPEDS
DC Motor is the most basic electro-mechanical equipment and well-known for its merit and simplicity. The performance of DC motor is assessed based on several qualities that are most-likely contradictory each other, i.e. settling time and overshoot percentage. Most of controller’s optimization problems are multi-objective in nature since they normally have several conflicting objectives that must be met simultaneously. In this study, the grey relational analysis (GRA) was combined with Taguchi method to search the optimum PID parameter for multi-objective problem. First, a L9 (33) orthogonal array was used to plan out the processing parameters that would affect the DC motor’s speed. Then GRA was applied to overcome the drawback of single quality characteristics in the Taguchi method, and then the optimized PID parameter combination was obtained for multiple quality characteristics from the response table and the response graph from GRA. Signal-to-noise ratio (S/N ratio) calculation and analysis of variance (ANOVA) would be performed to find out the significant factors. Lastly, the reliability and reproducibility of the experiment was verified by confirming a confidence interval (CI) of 95%.
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
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 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.
Cuckoo Search Algorithm for Congestion Alleviation with Incorporation of Wind...IJECEIAES
The issue to alleviate congestion in the power system framework has emerged as an alluring field for the power system researchers. The research conducted in this article proposes a cuckoo search algorithm-based congestion alleviation strategy with the incorporation of wind farm. The bus sensitivity factor data are computed and utilized to sort out the sutiable position for the installation of the wind farm. The generators contributing in the real power rescheduleing process are selected as per the generator sensitivity values. The cuckoo search algorithm is implemented to minimize the congestion cost with the embodiment of the wind farm. The proposed method is tested on 39 bus New England framework and the results obtained with the cuckoo search-based congestion management approach outperforms the results opted with other heuristic optimization techniques in the past research literatures.
Bi-objective Optimization Apply to Environment a land Economic Dispatch Probl...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Advanced SOM & K Mean Method for Load Curve Clustering IJECEIAES
From the load curve classification for one customer, the main features such as the seasonal factors, the weekday factors influencing on the electricity consumption may be extracted. By this way some utilities can make decision on the tariff by seasons or by day in week. The popular clustering techniques are the SOM & K-mean or Fuzzy K-mean. SOM &Kmean is a prominent approach for clustering with a two-level approach: first, the data set will be clustered using the SOM and in the second level, the SOM will be clustered by K-mean. In the first level, two training algorithms were examined: sequential and batch training. For the second level, the K-mean has the results that are strongly depended on the initial values of the centers. To overcome this, this paper used the subtractive clustering approach proposed by Chiu in 1994 to determine the centers. Because the effective radius in Chiu’s method has some influence on the number of centers, the paper applied the PSO technique to find the optimum radius. To valid the proposed approach, the test on well-known data samples is carried out. The applications for daily load curves of one Southern utility are presented.
Firefly Algorithm to Opmimal Distribution of Reactive Power Compensation Units IJECEIAES
The issue of electric power grid mode of optimization is one of the basic directions in power engineering research. Currently, methods other than classical optimization methods based on various bio-heuristic algorithms are applied. The problems of reactive power optimization in a power grid using bio-heuristic algorithms are considered. These algorithms allow obtaining more efficient solutions as well as taking into account several criteria. The Firefly algorithm is adapted to optimize the placement of reactive power sources as well as to select their values. A key feature of the proposed modification of the Firefly algorithm is the solution for the multi-objective optimization problem. Algorithms based on a bio-heuristic process can find a neighborhood of global extreme, so a local gradient descent in the neighborhood is applied for a more accurate solution of the problem. Comparison of gradient descent, Firefly algorithm and Firefly algorithm with gradient descent is carried out.
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
Transfer of ut information from fpga through ethernet interfaceeSAT 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
Economic/Emission Load Dispatch Using Artificial Bee Colony AlgorithmIDES Editor
This paper presents an application of the
artificial bee colony (ABC) algorithm to multi-objective
optimization problems in power system. A new multiobjective
artificial bee colony (MOABC) algorithm to
solve the economic/ emission dispatch (EED) problem is
proposed in this paper. Non-dominated sorting is
employed to obtain a Pareto optimal set. Moreover, fuzzy
decision theory is employed to extract the best
compromise solution. A numerical result for IEEE 30-bus
test system is presented to demonstrate the capability of
the proposed approach to generate well-distributed
Pareto-optimal solutions of EED problem in one single
run. In addition, the EED problem is also solved using the
weighted sum method using ABC. Results obtained with
the proposed approach are compared with other
techniques available in the literature. Results obtained
show that the proposed MOABC has a great potential in
handling multi-objective optimization problem.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Optimum designing of a transformer considering lay out constraints by penalty...INFOGAIN PUBLICATION
Optimum designing of power electrical equipment and devices play a leading role in attaining optimal performance and price of equipments in electric power industry. Optimum transformer design considering multiple constraints is acquired using optimal determination of geometric parameters of transformer with respect to its magnetic and electric properties. As it is well known, every optimization problem requires an objective function to be minimized. In this paper optimum transformer design problem comprises minimization of transformers mean core mass and its windings by satisfying multiple constraints according to transformers ratings and international standards using a penalty-based method. Hybrid big bang-big crunch algorithm is applied to solve the optimization problem and results are compared to other methods. Proposed method has provided a reliable optimization solution and has guaranteed access to a global optimum. Simulation result indicates that using the proposed algorithm, transformer parameters such as core mass, efficiency and dimensions are remarkably improved. Moreover simulation time using this algorithm is quit less in comparison to other approaches.
Soft Computing Technique Based Enhancement of Transmission System Lodability ...IJERA Editor
Due to the growth of electricity demands and transactions in power markets, existing power networks need to be enhanced in order to increase their loadability. The problem of determining the best locations for network reinforcement can be formulated as a mixed discrete-continuous nonlinear optimization problem (MDCP). The complexity of the problem makes extensive simulations necessary and the computational requirement is high. This paper compares the effectiveness of Evolutionary Programming (EP) and an ordinal optimization (OO) technique is proposed in this paper to solve the MDCP involving two types of flexible ac transmission systems (FACTS) devices, namely static var compensator (SVC) and thyristor controlled series compensator (TCSC), for system loadability enhancement. In this approach, crude models are proposed to cope with the complexity of the problem and speed up the simulations with high alignment confidence. The test and Validation of the proposed algorithm are conducted on IEEE 14–bus system and 22-bus Indian system.Simulation results shows that the proposed models permit the use of OO-based approach for finding good enough solutions with less computational efforts.
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.
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 Load Dispatch Problem with Valve – Point Effect Using a Binary Bat A...IDES Editor
This paper proposes application of BAT algorithm
for solving economic load dispatch problem. BAT
algorithmic rule is predicated on the localization
characteristics of micro bats. The proposed approach has
been examined and tested with the numerical results of
economic load dispatch problems with three and five
generating units with valve - point loading without
considering prohibited operating zones and ramp rate limits.
The results of the projected BAT formula are compared with
that of other techniques such as lambda iteration, GA, PSO,
APSO, EP, ABC and basic principle. For each case, the
projected algorithmic program outperforms the answer
reported for the existing algorithms. Additionally, the
promising results show the hardness, quick convergence
and potency of the projected technique.
Multi-objective Optimization Scheme for PID-Controlled DC MotorIAES-IJPEDS
DC Motor is the most basic electro-mechanical equipment and well-known for its merit and simplicity. The performance of DC motor is assessed based on several qualities that are most-likely contradictory each other, i.e. settling time and overshoot percentage. Most of controller’s optimization problems are multi-objective in nature since they normally have several conflicting objectives that must be met simultaneously. In this study, the grey relational analysis (GRA) was combined with Taguchi method to search the optimum PID parameter for multi-objective problem. First, a L9 (33) orthogonal array was used to plan out the processing parameters that would affect the DC motor’s speed. Then GRA was applied to overcome the drawback of single quality characteristics in the Taguchi method, and then the optimized PID parameter combination was obtained for multiple quality characteristics from the response table and the response graph from GRA. Signal-to-noise ratio (S/N ratio) calculation and analysis of variance (ANOVA) would be performed to find out the significant factors. Lastly, the reliability and reproducibility of the experiment was verified by confirming a confidence interval (CI) of 95%.
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
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 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.
Cuckoo Search Algorithm for Congestion Alleviation with Incorporation of Wind...IJECEIAES
The issue to alleviate congestion in the power system framework has emerged as an alluring field for the power system researchers. The research conducted in this article proposes a cuckoo search algorithm-based congestion alleviation strategy with the incorporation of wind farm. The bus sensitivity factor data are computed and utilized to sort out the sutiable position for the installation of the wind farm. The generators contributing in the real power rescheduleing process are selected as per the generator sensitivity values. The cuckoo search algorithm is implemented to minimize the congestion cost with the embodiment of the wind farm. The proposed method is tested on 39 bus New England framework and the results obtained with the cuckoo search-based congestion management approach outperforms the results opted with other heuristic optimization techniques in the past research literatures.
Bi-objective Optimization Apply to Environment a land Economic Dispatch Probl...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Advanced SOM & K Mean Method for Load Curve Clustering IJECEIAES
From the load curve classification for one customer, the main features such as the seasonal factors, the weekday factors influencing on the electricity consumption may be extracted. By this way some utilities can make decision on the tariff by seasons or by day in week. The popular clustering techniques are the SOM & K-mean or Fuzzy K-mean. SOM &Kmean is a prominent approach for clustering with a two-level approach: first, the data set will be clustered using the SOM and in the second level, the SOM will be clustered by K-mean. In the first level, two training algorithms were examined: sequential and batch training. For the second level, the K-mean has the results that are strongly depended on the initial values of the centers. To overcome this, this paper used the subtractive clustering approach proposed by Chiu in 1994 to determine the centers. Because the effective radius in Chiu’s method has some influence on the number of centers, the paper applied the PSO technique to find the optimum radius. To valid the proposed approach, the test on well-known data samples is carried out. The applications for daily load curves of one Southern utility are presented.
Firefly Algorithm to Opmimal Distribution of Reactive Power Compensation Units IJECEIAES
The issue of electric power grid mode of optimization is one of the basic directions in power engineering research. Currently, methods other than classical optimization methods based on various bio-heuristic algorithms are applied. The problems of reactive power optimization in a power grid using bio-heuristic algorithms are considered. These algorithms allow obtaining more efficient solutions as well as taking into account several criteria. The Firefly algorithm is adapted to optimize the placement of reactive power sources as well as to select their values. A key feature of the proposed modification of the Firefly algorithm is the solution for the multi-objective optimization problem. Algorithms based on a bio-heuristic process can find a neighborhood of global extreme, so a local gradient descent in the neighborhood is applied for a more accurate solution of the problem. Comparison of gradient descent, Firefly algorithm and Firefly algorithm with gradient descent is carried out.
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
Transfer of ut information from fpga through ethernet interfaceeSAT 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
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.
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
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.
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
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
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.
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
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.
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.
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.
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.
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
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
Technical engineering in industrial ippc as a key tool for ambient air qualit...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.
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
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.
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.
A Study of Load Flow Analysis Using Particle Swarm OptimizationIJERA Editor
Load flow study is done to determine the power system static states (voltage magnitudes and voltage angles) at each bus to find the steady state working condition of a power system. It is important and most frequently car-ried out study performed by power utilities for power system planning, optimization, operation and control. In this project a Particle Swarm Optimization (PSO) is proposed to solve load flow problem under different load-ing/ contingency conditions for computing bus voltage magnitudes and angles of the power system. With the increasing size of power system, this is very necessary to finding the solution to maximize the utilization of ex-isting system and to provide adequate voltage support. For this the good voltage profile is must. STATCOM, if placed optimally can be effective in providing good voltage profile and in turn resulting into stable power sys-tem. The study presents a hybrid particle swarm based methodology for solving load flow in electrical power systems. Load flow is an electrical engineering well-known problem which provides the system status in the steady-state and is required by several functions performed in power system control centers.
A Hybrid Formulation between Differential Evolution and Simulated Annealing A...TELKOMNIKA JOURNAL
The aim of this paper is to solve the optimal reactive power dispatch (ORPD) problem.
Metaheuristic algorithms have been extensively used to solve optimization problems in a reasonable time
without requiring in-depth knowledge of the treated problem. The perform ance of a metaheuristic requires
a compromise between exploitation and exploration of the search space. However, it is rarely to have the
two characteristics in the same search method, where the current emergence of hybrid methods. This
paper presents a hybrid formulation between two different metaheuristics: differential evolution (based on a
population of solution) and simulated annealing (based on a unique solution) to solve ORPD. The first one
is characterized with the high capacity of exploration, while the second has a good exploitation of the
search space. For the control variables, a mixed representation (continuous/discrete), is proposed. The
robustness of the method is tested on the IEEE 30 bus test system.
Hybrid Particle Swarm Optimization for Solving Multi-Area Economic Dispatch P...ijsc
We consider the Multi-Area Economic Dispatch problem (MAEDP) in deregulated power system environment for practical multi-area cases with tie line constraints. Our objective is to generate allocation to the power generators in such a manner that the total fuel cost is minimized while all operating constraints are satisfied. This problem is NP-hard. In this paper, we propose Hybrid Particle Swarm Optimization (HGAPSO) to solve MAEDP. The experimental results are reported to show the efficiency of proposed algorithms compared to Particle Swarm Optimization with Time-Varying Acceleration Coefficients (PSO-TVAC) and RCGA.
Evaluation of IEEE 57 Bus System for Optimal Power Flow AnalysisIJERA Editor
The analysis of load flow in a network under steady state operation is challenging task especially subjected to
inequality constraints in which the system operates. No doubt, that the load flow system analysis is an important
aspect for power system analysis and design. The basic analysis technique for power flow is to find different
parameters including magnitude and phase angle of voltage at each bus with active and reactive power flows in
each transmission lines. Thus, load flow analysis is important numerical analysis for any power system. In this
regard, this experiment is studied to evaluate IEEE 57 bus system for optimal flow analysis.
PRACTICAL IMPLEMENTION OF GAOPF ON INDIAN 220KV TRANSMISSION SYSTEMecij
This paper presents the practical implementation of developed genetic algorithm based optimal power flow algorithms. These algorithms are tested on IEEE30 bus system and the results were presented in the paper [8]. The same algorithms now tested on 220KV Washi zone Indian power transmission system . The GAOPF with fixed penalty and Fuzzy based variable penalty tested on 220KV transmission system consists of 52 bus and 88lines. The fuel costs ,computational time and the system condition were studied and the results are presented in this paper .Also the available load transfer capability of the 220KV system for congestion management is also presented
Optimal Power Flow with Reactive Power Compensation for Cost And Loss Minimiz...ijeei-iaes
One of the concerns of power system planners is the problem of optimum cost of generation as well as loss minimization on the grid system. This issue can be addressed in a number of ways; one of such ways is the use of reactive power support (shunt capacitor compensation). This paper used the method of shunt capacitor placement for cost and transmission loss minimization on Nigerian power grid system which is a 24-bus, 330kV network interconnecting four thermal generating stations (Sapele, Delta, Afam and Egbin) and three hydro stations to various load points. Simulation in MATLAB was performed on the Nigerian 330kV transmission grid system. The technique employed was based on the optimal power flow formulations using Newton-Raphson iterative method for the load flow analysis of the grid system. The results show that when shunt capacitor was employed as the inequality constraints on the power system, there is a reduction in the total cost of generation accompanied with reduction in the total system losses with a significant improvement in the system voltage profile
Optimal power flow solution with current injection model of generalized inte...IJECEIAES
Optimal power flow (OPF) solutions with generalized interline power flow controller (GIPFC) devices play an imperative role in enhancing the power system’s performance. This paper used a novel ant lion optimization (ALO) algorithm which is amalgamated with Lévy flight operator, and an effectual algorithm is proposed named as, ameliorated ant lion optimization (AALO) algorithm. It is being implemented to solve single objective OPF problem with the latest flexible alternating current transmission system (FACTS) controller named as GIPFC. GIPFC can control a couple of transmission lines concurrently and it also helps to control the sending end voltage. In this paper, current injection modeling of GIPFC is being incorporated in conventional Newton-Raphson (NR) load flow to improve voltage of the buses and focuses on minimizing the considered objectives such as generation fuel cost, emissions, and total power losses by fulfilling equality, in-equality. For optimal allocation of GIPFC, a novel Lehmann-SymanzikZimmermann (LSZ) approach is considered. The proposed algorithm is validated on single benchmark test functions such as Sphere, Rastrigin function then the proposed algorithm with GIPFC has been testified on standard IEEE-30 bus system.
Short Term Electrical Load Forecasting by Artificial Neural NetworkIJERA Editor
This paper presents an application of artificial neural networks for short-term times series electrical load
forecasting. An adaptive learning algorithm is derived from system stability to ensure the convergence of
training process. Historical data of hourly power load as well as hourly wind power generation are sourced from
European Open Power System Platform. The simulation demonstrates that errors steadily decrease in training
with the adaptive learning factor starting at different initial value and errors behave volatile with constant
learning factors with different values
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.
A Novel Approach to GSA, GA and Wavelet Transform to Design Fuzzy Logic Contr...IAES-IJPEDS
This paper proposes a novel approach for obtaining a closed loop control
scheme based on Fuzzy Logic Controller to regulate the output voltage
waveform of multilevel inverter. Fuzzy Logic Controller is used to guide and
control the inverter to synthesize a stepped output voltage waveform with
reduced harmonics. In this paper, three different intelligent soft-computing
methods are used to design a fuzzy system to be used as a closed loop control
system for regulating the inverter output. Gravitational Search Algorithm
and Genetic Algorithm are used as optimization methods to evaluate
switching angles for different combination of input voltages applied to MLI.
Wavelet Transform is used as synthesizing technique to shape stepped output
waveform of inverter using orthogonal wavelet sets. The proposed FLC
controlled method is carried out for a wider range of input dc voltages by
considering ±10% variations in nominal voltage value. A 7-level inverter is
used to validate the results of proposed control methods. The three proposed
methods are then compared in terms of various parameters like
computational time, switching angles and THD to justify the performance
and system flexibility. Finally, hardware based results are also obtained to
verify the viability of the proposed method.
Combination of Immune Genetic Particle Swarm Optimization algorithm with BP a...paperpublications3
Abstract:In this paper, merging Immune Genetic Particle Swarm Optimization algorithm (IGPSO) with BP algorithm to optimize BP Neural Network parameter i.e., BPIGPSO amalgamation to solve optimal reactive power dispatch algorithm. The basic perception is that first training BP neural network with IGPSO to find out a comparatively optimal solution, then take the network parameter at this time as the preliminary parameter of BP algorithm to carry out the training, finally searching the optimal solution. The proposed BPIGPSO has been tested on standard IEEE 57 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.
Keywords:BP neural network, Immune Genetic Particle Swarm Optimization algorithm, Optimal Reactive Power, Transmission loss.
Hybrid Quantum Genetic Particle Swarm Optimization Algorithm For Solving Opti...paperpublications3
Abstract: This paper presents hybrid particle swarm algorithm for solving the multi-objective reactive power dispatch problem. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. Evolutionary algorithm and Swarm Intelligence algorithm (EA, SI), a part of Bio inspired optimization algorithm, have been widely used to solve numerous optimization problem in various science and engineering domains. In this paper, a framework of hybrid particle swarm optimization algorithm, called Hybrid quantum genetic particle swarm optimization (HQGPSO), is proposed by reasonably combining the Q-bit evolutionary search of quantum particle swarm optimization (QPSO) algorithm and binary bit evolutionary search of genetic particle swarm optimization (GPSO) in order to achieve better optimization performances. The proposed HQGPSO also can be viewed as a kind of hybridization of micro-space based search and macro-space based search, which enriches the searching behavior to enhance and balance the exploration and exploitation abilities in the whole searching space. In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other algorithms.
Keywords: quantum particle swarm optimization, genetic particle swarm optimization, hybrid algorithm Optimization, Swarm Intelligence, optimal reactive power, Transmission loss.
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Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
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Event Management System Vb Net Project Report.pdfKamal Acharya
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My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
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CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
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Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
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Security constrained optimal load dispatch using hpso technique for thermal scheduling problems
1. IJRET: International Journal of Research in Engineering and Technology ISSN: 2319-1163
__________________________________________________________________________________________
Volume: 02 Issue: 05 | May-2013, Available @ http://www.ijret.org 777
SECURITY CONSTRAINED OPTIMAL LOAD DISPATCH USING HPSO
TECHNIQUE FOR THERMAL SCHEDULING PROBLEMS
S.Prabakaran1
, V.Senthil Kumar2
1
Research Scholar, 2
Associate Professor, Department of Electrical and Electronics Engineering, Anna University,
Chennai – 600025, Tamilnadu, India
prabakaran110768@gmail.com
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.
Keywords: Evolutionary Programming, Gaussian Mutation, Particle Swarm Optimization, Hybrid PSO, Optimal Load
Dispatch, Line Flow Constraints
---------------------------------------------------------------------***-------------------------------------------------------------------------
1. INTRODUCTION:
Optimal Load Dispatch (OLD) pertains to optimum generation
in an interconnected power system to minimize the cost of
generation subject to relevant system constraints. In this paper
the line loading (MVA) and voltage constraints, important for
any practical implementation of short term OLD, are taken
into consideration. The control of voltages, real and reactive
power limit (MVA limit) on the transmission line is one of the
most important activities in the modern power system. In the
past, many mathematical programming models and
optimization technique have been applied to solve the OLD
problems. These methods include lambda iteration method [1],
base point, participation factor, gradient method, etc.
However, the base case operating constraints, line flow limits
and load bus voltage magnitude limits are not consider in this
methods. Ringlee et al.[2] to solved a non-convex OLD
problem using Dynamic Programming (DP) but this has
disadvantage namely the computational requirements of the
DP based method depend on the size of the discrete capacity
step (10MW, 20MW) used, Which is usual accuracy required
in the OLD schedule. Dommel et al [3] presented a Non-
Linear Programming (NLP) technique to solve Optimal Power
Flow (OPF) problem in which the line flow constraints and
voltage limits are included. Nanda et al [4] have developed an
algorithm to solve the OLD problem with line flow constraints
using modified coordination equations.
Linear programming methods are fast and reliable, but the
main disadvantage is the piece-wise linear cost approximation.
NLP methods have a problem of convergence and algorithm
complexity.
Stochastic searching algorithms such as Simulate Annealing
(SA) [5] and Hopfield neural network methods [6] have also
been used to solve the non-convex OLD problem. However,
these methods require external training routines. Baskar et al
[7] proposed a participation factor in conjunction with the
improved lambda based algorithm (GA) to solve OLD
problem but this has disadvantage that the line flow limits are
not considered and it leads to overload on the lines. Though
Meta heuristic algorithm such as GA has been employed to
solve OLD problems, recent research has identified some
deficient in GA performance. The premature convergence of
GA degrades its performance and reduces its search capability
that leads to a higher probability towards obtaining local
minimum. The main objective of the present work is to
develop and study the absolute as well as relative performance
of following techniques applied to the power system OLD
problem with line flow constraints, voltage on each bus, mini-
mum and maximum generating limits and power balance
constraint. The PSO Algorithm was applied to solve various
optimal load dispatch problems [10],[11],[12]. The security
constraints OLD Problem is solved and necessary software has
been developed using the following techniques:
1. Evolutionary Programming (EP) [8]
2. Particle Swarm Optimization (PSO) [9]
3. Proposed Hybrid PSO
2. IJRET: International Journal of Research in Engineering and Technology ISSN: 2319-1163
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2. PROBLEM FORMULATION:
Optimization of fuel cost for generation has been formulated
based on a classical OLD problem with line flow constraints.
For a given power system network, the optimized cost of
generation is given by the following equation,
n
Min F T (P) = ∑ F i (Pi) (1)
i=1
Subject to
(i) Power balance equation
n
∑ Pgi = Pd + Pl (2)
i =1
(ii) The power flow equation of power network
g (׀v,׀θ ) = 0 (3)
Where, Pi (׀v,׀ θ) – Pinet
g (׀v,׀θ) = Qi (׀v,׀θ ) – Qinet
Pm (׀v,׀θ ) – Pinet
(iii) The inequality constraint on real power generation Pgi of
each unit i,
Pgimin ≤ Pgi ≤ Pgimax (4)
(iv) The inequality constraint on voltage of each PQ bus
Vimin ≤ Vi ≤ Vimax (5)
(v) Power limit on transmission line
MVAfp,q ≤ MVAfp,qmax (6)
Total fuel cost of generation FT in terms of control variables
generator power can be expressed as
n
F (Pi) = ∑ (aiPgi2 + bi Pgi + ci ) $/hr (7)
i=1
3. OVERVIEW OF EP AND PSO:
Four decades earlier EP was proposed for evolution of finite
state machines, in order to solve a prediction task. Since then,
several modifications, enhancements, and implementations
have been proposed and investigated. Mutation is often
implemented by adding a random number or a vector from a
certain distribution (e.g., a Gaussian distribution in the case of
EP to a parent). The degree of variation of Gaussian mutation
is controlled by its standard deviation, which is also known as
a „strategy parameter‟ in an evolutionary search.
PSO is a population based optimization method first proposed
by Kennedy and Eberhart. According to the background of
PSO and simulation of swarm of bird, Kennedy and Eberhart
[10] [11] developed a PSO concept.
PSO is basically developed through simulation of bird
flocking in two- dimensional space. The position of each agent
is represented by XY axis position and also the velocity is
expressed by Vx (velocity of X axis) and Vy (velocity of Y
axis). Modification of the agent (particle) position is realized
by the position and velocity information. Bird flock- ing
optimizes a certain objective function. Each agent knows its
best value so far (pbest) and its XY position. This information
is analogy of personal experiences of each agent. Moreover,
each agent knows the best value so far in the group (gbest)
among pbests. This information is analogy of knowledge of
how other agents around them have performed. Each agent
tries to modify its position using the following information:
The current position (x, y),
The current velocities (Vx, Vy),
The distance between the current position and pbest,
The distance between current position and gbest.
This modification can be represented by the concept of
velocity. Velocity of each agent can be modified by the
following equation
Vit+1 = W Vit + C1* rand1* (pbest i-sit) + C2*rand2*(gbest –
sik0) (8)
The following weighing function is usually utilized in eqn (8)
W = Wmax - (Wmax - Wmin) * iter (9)
itermax
Using the above equation, a certain velocity, which gradually
gets close to pbest and gbest can be Calculated. The current
position can be modified by the following equation
Sit+1=Sit+Vi(t+1) (10)
The first term of the right hand side of (8) is corresponding to
diversification in the search procedure. The second and third
terms of that are corresponding to intensification in the search
procedure.
The PSO method has a well-balanced mechanism to utilize
diversification and intensification in the search procedure
efficiently. Figure 1 shows the concept of modification of a
searching point by PSO. Figure 2 shows the general flowchart
of PSO method.
Fig 1: Concept of modification of a searching point by PSO
3. IJRET: International Journal of Research in Engineering and Technology ISSN: 2319-1163
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Volume: 02 Issue: 05 | May-2013, Available @ http://www.ijret.org 779
S
t
Current searching point
S
t+1
Modified searching point
V
k
Current velocity
V
k+1
Modified velocity
Vpbest Velocity based on pbest
Vgbest Velocity based on gbest
Fig2. Flow Chart of General PSO Method
4. DEVELOPMENT OF HYBRID PSO METHOD
Using the above concepts, the Hybrid PSO (PSO Combined
EP) algorithm can be expressed as given below. Initial
searching points (real power generation of generators) and
velocities are usually generated randomly within allowable
range. The current searching point is set to pbest for each
agent. The best evaluated value (minimum augmented fuel
cost value in OLD problem) of pbest is set to gbest and gbest
value stored.
Modification of searching point of each agent is changed using
(8), (9) and (10) and the evaluation values are calculated
(augmented fuel cost value in OLD problem). If the evaluation
value of each agent is better than the previous pbest, the value
is set to pbest. If the best pbest is better than previous gbest,
the value is set to gbest. Modification of searching points
using gaussian random variable with 0 mean and standard
deviation proportional to scaled cost values (EP method) and
the evaluation values are calculated. If the evaluation value of
each agent is better than the previous pbest, the value is set to
pbest. If the best pbest is better than previous gbest, the value
is set to gbest. Modification of searching points using cauchy
random variable (EP method) and the evaluation values are
calculated. Figure 3 shows the flowchart of proposed HPSO
method.
Fig.3. Flow Chart of HPSO methods
Start
Initialize all control variables and parameters of EP and PSO
Generate initial population randomly
Evaluate fitness function for each member of the old population
If termination
reaches?
Print the
Results
Update the position and velocity of each member in old
population
Do the Gaussian random mutation of EP
Generation = Generation + 1
Yes
No
4. IJRET: International Journal of Research in Engineering and Technology ISSN: 2319-1163
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Volume: 02 Issue: 05 | May-2013, Available @ http://www.ijret.org 780
4.1 Step-by-step Procedure of Proposed Hybrid PSO
Method with Line Flow Constraints
The search procedure of the proposed Hybrid PSO method for
OLD problems with Line flow constraints is given below.
Step 1: Initialize randomly the real power generation Pg i of
the population according to the limit of each unit (except slack
bus) including the individual dimensions, searching points and
velocities. Initial velocity limits of each individual are as
Vd
max
= 0.5 Pd
max
, Vd
min
= - 0.5 Pd
min
n n
Where, Pd
MIN
= ∑ Pt
MIN
Pd
MIN
= ∑ Pt
max
i=1 i=1
Step 2: Compute slack bus generator vector (Ps), losses and
line flows using New- ton-Raphson load flow method for the
above generators.
Step 3: To account for slack unit limit violation, branch power
flow limit violation and voltage limit violation, the total
operating cost is augmented by non-negative penalty terms
K1, K2 and K3.Augmented cost FT calculated using (11).
(11)
Step 4: The minimum augmented fuel cost value among the
population is taken as best value. The best augmented fuel cost
value in the population is denoted as gbest and remaining
individuals are assigned as pbest.
Step 5: Modify the member velocity V of the each individual
Pgi using (12)
(12)
i =1, 2,……..n d = 1, 2,…………….m
where „n‟ is the population size; „m‟ is the number of units
and the „w‟ value is set using (9).
Step 6:
Step 7: Modify member position of each individual Pgi using
equation (13),
(13)
Step 8: Pgid
(t+1)
Must satisfy the capacity limits of the
generator as in (14)
(14)
Step 9: Modified member positions in Step 8 are taken as
initial value for N-R load flow method. Compute slack bus
power loss and line flows using N-R load flow method.
Step 10: Calculate the augmented fuel cost using equation
(11). Assign gbest and pbest value. If the current gbest value is
better than gbest value in Step 4 current value is set to gbest. If
current pbest value is better than pbest value in Step 4 cur-
rent value is set to pbest.
Step 11: Pgid
(t+1)
is created using Gaussian mutation as in (15)
and (16).
(15)
(16)
Check capacity limits of the generating units using (14),
replacing Pgid
(t+1)
by Pgid
(t+1)'
Step 12: Modified member positions in Step 11 are taken as
initial value for N-R load flow method. Compute slack bus
power loss and line flows using N-R load flow method.
Step 13: Calculate the augmented fuel cost using (11). Assign
gbest and pbest value. If the current gbest value is better than
gbest value in Step 10 current value is set to gbest. If current
pbest value is better than pbest value in Step 10 current value
is set to pbest.
Step 14: If the iteration reaches the Maximum go to Step 15,
otherwise go to Step 4, the gbest and pbest values in Step 4
replaced by latest gbest and pbest values from Step 13.
Step 15: Individual that generates the latest gbest value is the
optimal generation of each unit with minimum fuel cost
satisfying all the Line flow constraints.
5. EXAMPLES AND DISCUSSION:
A comparative study of EP, PSO and Hybrid PSO methods
were performed on IEEE 14 and 30 bus systems. The upper
and lower voltage limits at all the buses except slack bus were
taken as 1.01 and 0.95 respectively; the slack bus voltage was
fixed to its specified of 1.06 PU. The line flows were
computed using Newton-Raphson Method. Software has been
developed in MATLAB to solve OLD problems using EP,
PSO and Hybrid PSO methods and tested on Core i5 3.0 GHz
4GB RAM 500GB HDD Capacity personal Computer. Cost
coefficient taken from [12] for IEEE 14 and 32 bus systems
for implementing EP technique and PSO technique population
5. IJRET: International Journal of Research in Engineering and Technology ISSN: 2319-1163
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Volume: 02 Issue: 05 | May-2013, Available @ http://www.ijret.org 781
size = 20, maximum number of generations = 100, is taken
and the optimal solution was obtained in 50 trails
Case Study 1: IEEE 14-Bus System
The summarized results of IEEE 14 bus system are given in
table 1 provides of EP results obtained by various optimization
methods and the complete line flow results with and without
line flow constraints using Hybrid PSO given in table 2. The
star marked line was over loaded with economic generation
scheduled when the line flow constrains are not considered.
For IEEE14 bus system [12] demand of 259MW is taken. The
results clearly show that the Hybrid PSO method is superior
over the conventional PSO and EP methods.
Table1: Summary of Results 14 Bus system with Line Flow
Constraints
Method
P1
(MW)
P2
(MW)
P3
(MW)
Losses
(MW)
Optimum
Fuel Cost
$/hr
EP 92.72 78.90 94.42 7.06 1103.9
PSO 88.14 89.07 88.72 6.93 1114.9
Hybrid
PSO
114.44 52.18 100.00 7.62 1091.2
Table2: Line Flow Results of Hybrid PSO IEEE 14-bus
Line
Designation
Base
case
Line
Flow
in pu
Line flow
with
Constraints
in pu
Line Flow
Without
Constraints
in pu
Max.line
Flow in
pu
1-2 1.5741 0.5124 1.1025**
1.0000
1-5 0.7485 0.3265 0.5014 1.0000
2-3 0.7312 0.6321 0.6124 1.0000
2-4 0.5142 0.3974 0.4215 0.5000
2-5 0.4258 0.2581 0.2146 0.5000
3-4 0.2145 0.3145 0.3698 0.5000
4-5 0.6589 0.6587 0.6123 1.0000
4-7 0.2985 0.1478 0.0968 1.0000
4-9 0.1859 0.0125 0.0478 0.5000
5-6 0.4125 0.2698 0.1254 0.5000
6-11 0.8965 0.2875 0.1632 0.5000
6-12 0.0125 0.1487 0.0968 1.0000
6-13 0.1547 0.2154 0.2365 1.0000
7-8 0.1985 0.1854 0.1587 0.5000
7-9 0.2968 0.1758 0.1968 0.5000
9-10 0.0321 0.1548 0.1245 0.5000
9-14 0.0214 0.0251 0.0621 0.5000
10-11 0.0145 0.0362 0.0141 1.0000
2-13 0.0245 0.0369 0.1544 1.0000
13-14 0.0241 0.1634 0.1457 0.5000
** - Line Violation
Case Study 2: IEEE 30-BUS System
The summarized results of IEEE 30 bus system given in Table
3 provides of EP results obtained by various optimization
methods. For IEEE 30 bus system demand of 283.4 MW is
taken. Line flow limits, bus voltage limits, capacity limit to
consideration. The results clearly show that the proposed
HPSO out performs the other methods.
Table3: Summary of IEEE 30 bus system with line flow
constraints
Method
P1
(MW)
P2
(MW)
P3
(MW)
Losses
(MW)
Optimum
Fuel Cost
$/hr
EP 119.62 79.34 96.22 10.78 1186.9
PSO 96.93 96.74 98.42 7.49 1199.3
HPSO 129.42 66.27 96.20 8.28 1185.2
CONCLUSIONS
The EP technique, PSO and Hybrid PSO algorithms were
tested on IEEE 14 and IEEE 30 Bus systems and results were
presented. The MVA line flow limits of the test systems were
incorporated and the overload lines were observed. In the
proposed HPSO method the performance of the PSO is greatly
improved by incorporating EP features. The proposed method
has been demonstrated to have superior features including
stable convergence characteristics and avoid premature
convergence. The proposed approach is relatively efficient,
reliable and well suitable for large and practical utility
systems.
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