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.
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.
Performance based Comparison of Wind and Solar Distributed Generators using E...Editor IJLRES
Distributed Generation (DG) technologies have become more and more important in power systems. The objective of the paper is to optimize the distributed energy resource type and size based on uncertainties in the distribution network. The three things are considered in stand point of uncertainties are listed as, (i) Future load growth, (ii) Variation in the solar radiation, (iii) Wind output variation. The challenge in Optimal DG Placement (ODGP) needs to be solved with optimization problem with many objectives and constraints. The ODGP is going to be done here, by using Non-dominated Sorting Genetic Algorithm II (NSGA II). NSGA II is one among the available multi objective optimization algorithms with reduced computational complexity (O=MN2). Because of this prominent feature of NSGA II, it is widely applicable in all the multi objective optimization problems irrespective of disciplines. Hence it is selected to be employed here in order to obtain the reduced cost associated with the DG units. The proposed NSGA II is going to be applied on the IEEE 33-bus and the different performance characteristics were compared for both wind and solar type DG units.
Compromising between-eld-&-eed-using-gatool-matlabSubhankar Sau
Creating a compromising points between economic load dispatch & emission created from the plant to minimising those effects.
these are created by using MATLAB and GATOOL .
taking Weighted Sum Method,also Pareto optimal curve.
created by: SUBHANKAR SAU
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
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.
A MODIFIED ANT COLONY ALGORITHM FOR SOLVING THE UNIT COMMITMENT PROBLEMaeijjournal
Solving the unit commitment (UC) problem is one of the most complicated issues in power systems that its
exact solving can be calculated by perfect counting of entire possible compounds of generative units. UC is
equated as a nonlinear optimization with huge size. Purpose of solving this problem is to programming the
optimization of the generative units to minimize the full action cost regarding problem constraints. In this
article, a modified version of ant colony optimization (MACO) is introduced for solving the UC problem in
a power system. ACO algorithm is a powerful optimization method which has the capability of fleeing from
local minimums by performing flexible memory system. The efficiency of proposed method in two power
system containing 4 and 10 generative units is indicated. Comparison of obtained results from the proposed
method with results of the past well-known methods is a proof for suitability of performing the introduced
algorithm in economic input and output of generative units.
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.
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.
Performance based Comparison of Wind and Solar Distributed Generators using E...Editor IJLRES
Distributed Generation (DG) technologies have become more and more important in power systems. The objective of the paper is to optimize the distributed energy resource type and size based on uncertainties in the distribution network. The three things are considered in stand point of uncertainties are listed as, (i) Future load growth, (ii) Variation in the solar radiation, (iii) Wind output variation. The challenge in Optimal DG Placement (ODGP) needs to be solved with optimization problem with many objectives and constraints. The ODGP is going to be done here, by using Non-dominated Sorting Genetic Algorithm II (NSGA II). NSGA II is one among the available multi objective optimization algorithms with reduced computational complexity (O=MN2). Because of this prominent feature of NSGA II, it is widely applicable in all the multi objective optimization problems irrespective of disciplines. Hence it is selected to be employed here in order to obtain the reduced cost associated with the DG units. The proposed NSGA II is going to be applied on the IEEE 33-bus and the different performance characteristics were compared for both wind and solar type DG units.
Compromising between-eld-&-eed-using-gatool-matlabSubhankar Sau
Creating a compromising points between economic load dispatch & emission created from the plant to minimising those effects.
these are created by using MATLAB and GATOOL .
taking Weighted Sum Method,also Pareto optimal curve.
created by: SUBHANKAR SAU
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
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.
A MODIFIED ANT COLONY ALGORITHM FOR SOLVING THE UNIT COMMITMENT PROBLEMaeijjournal
Solving the unit commitment (UC) problem is one of the most complicated issues in power systems that its
exact solving can be calculated by perfect counting of entire possible compounds of generative units. UC is
equated as a nonlinear optimization with huge size. Purpose of solving this problem is to programming the
optimization of the generative units to minimize the full action cost regarding problem constraints. In this
article, a modified version of ant colony optimization (MACO) is introduced for solving the UC problem in
a power system. ACO algorithm is a powerful optimization method which has the capability of fleeing from
local minimums by performing flexible memory system. The efficiency of proposed method in two power
system containing 4 and 10 generative units is indicated. Comparison of obtained results from the proposed
method with results of the past well-known methods is a proof for suitability of performing the introduced
algorithm in economic input and output of generative units.
Stochastic control for optimal power flow in islanded microgridIJECEIAES
The problem of optimal power flow (OPF) in an islanded mircrogrid (MG) for hybrid power system is described. Clearly, it deals with a formulation of an analytical control model for OPF. The MG consists of wind turbine generator, photovoltaic generator, and diesel engine generator (DEG), and is in stochastic environment such as load change, wind power fluctuation, and sun irradiation power disturbance. In fact, the DEG fails and is repaired at random times so that the MG can significantly influence the power flow, and the power flow control faces the main difficulty that how to maintain the balance of power flow? The solution is that a DEG needs to be scheduled. The objective of the control problem is to find the DEG output power by minimizing the total cost of energy. Adopting the Rishel’s famework and using the Bellman principle, the optimality conditions obtained satisfy the Hamilton-Jacobi-Bellman equation. Finally, numerical examples and sensitivity analyses are included to illustrate the importance and effectiveness of the proposed model.
Electronics system thermal management optimization using finite element and N...TELKOMNIKA JOURNAL
The demand for high-performance, smaller-sized, and multi-functional electronics component
poses a great challenge to the thermal management issues in a printed circuit board (PCB)
design. Moreover, this thermal problem can affect the lifespan, performance, and the reliability of
the electronic system. This project presents the simulation of an optimal thermal distribution for various
samples of electronics components arrangement on PCB. The objectives are to find the optimum
components arrangement with minimal heat dissipation and cover small PCB area. Nelder-Mead
Optimization (NMO) with Finite Element method has been used to solve these multi-objective problems.
The results show that with the proper arrangement of electronics components, the area of PCB has been
reduced by 26% while the temperature of components is able to reduce up to 40%. Therefore, this study
significantly benefits for the case of thermal management and performance improvement onto the electronic
product and system.
An Improved Particle Swarm Optimization for Proficient Solving of Unit Commit...IDES Editor
This paper presents a new approach to solving the
short-term unit commitment problem using an improved
Particle Swarm Optimization (IPSO). The objective of this
paper is to find the generation scheduling such that the total
operating cost can be minimized, when subjected to a variety
of constraints. This also means that it is desirable to find the
optimal generating unit commitment in the power system for
the next H hours. PSO, which happens to be a Global
Optimization technique for solving Unit Commitment
Problem, operates on a system, which is designed to encode
each unit’s operating schedule with regard to its minimum
up/down time. In this, the unit commitment schedule is coded
as a string of symbols. An initial population of parent
solutions is generated at random. Here, each schedule is
formed by committing all the units according to their initial
status (“flat start”). Here the parents are obtained from a predefined
set of solution’s i.e. each and every solution is adjusted
to meet the requirements. Then, a random decommitment is
carried out with respect to the unit’s minimum down times. A
thermal Power System in India demonstrates the effectiveness
of the proposed approach; extensive studies have also been
performed for different power systems consist of 10, 26, 34
generating units. Numerical results are shown comparing the
cost solutions and computation time obtained by using the
IPSO and other conventional methods like Dynamic
Programming (DP), Legrangian Relaxation (LR) in reaching
proper unit commitment.
Comparative Study on the Performance of A Coherency-based Simple Dynamic Equi...IJAPEJOURNAL
Earlier, a simple dynamic equivalent for a power system external area containing a group of coherent generators was proposed in the literature. This equivalent is based on a new concept of decomposition of generators and a two-level generator aggregation. With the knowledge of only the passive network model of the external area and the total inertia constant of all the generators in this area, the parameters of this equivalent are determinable from a set of measurement data taken solely at a set of boundary buses which separates this area from the rest of the system. The proposed equivalent, therefore, does not require any measurement data at the external area generators. This is an important feature of this equivalent. In this paper, the results of a comparative study on the performance of this dynamic equivalent aggregation with the new inertial aggregation in terms of accuracy are presented. The three test systems that were considered in this comparative investigation are the New England 39-bus 10-generator system, the IEEE 162-bus 17-generator system and the IEEE 145-bus 50-generator system.
Optimal power flow based congestion management using enhanced genetic algorithmsIJECEIAES
Congestion management (CM) in the deregulated power systems is germane and of central importance to the power industry. In this paper, an optimal power flow (OPF) based CM approach is proposed whose objective is to minimize the absolute MW of rescheduling. The proposed optimization problem is solved with the objectives of total generation cost minimization and the total congestion cost minimization. In the centralized market clearing model, the sellers (i.e., the competitive generators) submit their incremental and decremental bid prices in a real-time balancing market. These can then be incorporated in the OPF problem to yield the incremental/ decremental change in the generator outputs. In the bilateral market model, every transaction contract will include a compensation price that the buyer-seller pair is willing to accept for its transaction to be curtailed. The modeling of bilateral transactions are equivalent to the modifying the power injections at seller and buyer buses. The proposed CM approach is solved by using the evolutionary based Enhanced Genetic Algorithms (EGA). IEEE 30 bus system is considered to show the effectiveness of proposed CM approach.
Reinforcement Learning for Building Energy Optimization Through Controlling o...Power System Operation
This paper presents a novel methodology to control HVAC system and minimize energy cost
on the premise of satisfying power system constraints. A multi-agent architecture based on game theory and
reinforcement learning is developed so as to reduce the cost and computational complexity of the microgrid.
The multi-agent architecture comprising agents, state variables, action variables, reward function and cost
game is formulated. The paper lls the gap between multi-agent HVAC systems control and power system
optimization and planning. The results and analysis indicate that the proposed algorithm is benecial to deal
with the problem of ``curse of dimensionality'' for multi-agent microgrid HVAC system control and speed
up learning of unknown power system conditions.
A progressive domain expansion method for solving optimal control problemTELKOMNIKA JOURNAL
Electricity generation at the hydropower stations in Nigeria has been below the expected value. While the hydro stations have a capacity to generate up to 2,380 MW, the daily average energy generated in 2017 was estimated at around 846 MW. A factor responsible for this is the lack of a proper control system to manage the transfer of resources between the cascaded Kainji-Jebba Hydropower stations operating in tandem. This paper addressed the optimal regulation of the operating head of the Jebba hydropower reservoir in the presence of system constraints, flow requirement and environmental factors that are weather-related. The resulting two-point boundary value problem was solved using the progressive expansion of domain technique as against the shooting or multiple shooting techniques. The results provide the optimal inflow required to keep the operating head of the Jebba reservoir at a nominal level, hence ensuring that the maximum number of turbo-alternator units are operated.
Critical Review of Different Methods for Siting and Sizing Distributed-genera...TELKOMNIKA JOURNAL
Due to several benefits attached to distributed generators such as reduction in line losses,
improved voltage profile, reliable system etc., the study on how to optimally site and size distributed
generators has been on the increase for more than two decades. This has propelled several
researchers to explore various scientific and engineering powerful simulation tools, valid and reliable
scientific methods like analytical, meta-heuristic and hybrid methods to optimally place and size
distributed generator(s) for optimal benefits. This study gives a critical review of different methods
used in siting and sizing distributed generators alongside their results, test systems and gaps in
literature.
Achieving Energy Proportionality In Server ClustersCSCJournals
a great amount of interests in the past few years. Energy proportionality is a principal to ensure that energy consumption is proportional to the system workload. Energy proportional design can effectively improve energy efficiency of computing systems. In this paper, an energy proportional model is proposed based on queuing theory and service differentiation in server clusters, which can provide controllable and predictable quantitative control over power consumption with theoretically guaranteed service performance. Futher study for the transition overhead is carried out corresponding strategy is proposed to compensate the performance degradation caused by transition overhead. The model is evaluated via extensive simulations and is justified by the real workload data trace. The results show that our model can achieve satisfied service performance while still preserving energy efficiency in the system.
The performance expectations for commercial wind turbines, from a variety of geograph- ical regions with differing wind regimes, present significant techno-commercial challenges to manufacturers. The determination of which commercial turbine types perform the best under differing wind regimes can provide unique insights into the complex demands of a concerned target market. In this paper, a comprehensive methodology is developed to explore the suitability of commercially available wind turbines (when operating as a group/array) to the various wind regimes occurring over a large target market. The three major steps of this methodology include: (i) characterizing the geographical variation of wind regimes in the target market, (ii) determining the best performing turbines (in terms of minimum COE accomplished) for different wind regimes, and (iii) developing a metric to investigate the performance-based expected market suitability of currently available tur- bine feature combinations. The best performing turbines for different wind regimes are determined using the Unrestricted Wind Farm Layout Optimization (UWFLO) method. Expectedly, the larger sized and higher rated-power turbines provide better performance at lower average wind speeds. However, for wind resources higher than class-4, the perfor- mances of lower-rated power turbines are fairly competitive, which could make them better choices for sites with complex terrain or remote location. In addition, turbines with direct drive are observed to perform significantly better than turbines with more conventional gear-based drive-train. The market considered in this paper is mainland USA, for which wind map information is obtained from NREL. Interestingly, it is found that overall higher rated-power turbines with relatively lower tower heights are most favored in the onshore US market.
In imaging science, the photo editing software packages can alter the original images without any
detecting traces of tampering. Hence, the image forgery detection technique plays an important role in
verifying the integrity of digital image forensics for authentication. The techniques such as
watermarking are used for authentication but it can be modified through third parties attack through
extraction. Malicious and digital imaging (digital products) tamper detection is the subject of this
article. In particular, we focus on a special type of digital forgery detection - copy attack campaign, in
which part of the image is copied and pasted into the image and the cover features a large image of
intentions another. In this paper, we investigate the dynamic forged copy detection problem, and
describes a highly efficient and reliable detection method that based on image source ANN
identification.. Even when the region is enhanced copy / retouching and background merger, and the
method can successfully identify counterfeit forgery when images are saved in a lossy format (such as
JPEG). The performance of the method's performance several forged images.
RSA is one of the most popular Public Key Cryptography based algorithm mainly used for digital
signatures, encryption/decryption etc. It is based on the mathematical scheme of factorization of very large
integers which is a compute-intensive process and takes very long time as well as power to perform.
Several scientists are working throughout the world to increase the speedup and to decrease the power
consumption of RSA algorithm while keeping the security of the algorithm intact. One popular technique
which can be used to enhance the performance of RSA is parallel programming. In this paper we are
presenting the survey of various parallel implementations of RSA algorithm involving variety of hardware
and software implementations.
A N A UTOMATION S YSTEM F OR T RACKING C OMMUNITY C ERTIFICATEijcsa
The e-governance project is an online web portal wh
ich aims to serve the public by providing their
community certificate. The objective of achieving e
-governance goes far beyond mere computerization of
standalone back office operations. This portal is d
eveloped to validate the information provided by th
e
public by governing authorities such as Tahsildar a
nd Revenue Inspector etc., It includes a provision
for
the public to track the status of the requested cer
tificate with certificate number whether it is appr
oved or
rejected or in progress via online. The goal for es
tablishing e-governance is to improve the user
friendliness of the administrative system and to si
mplify and improve the efficiency, reliability and
transparency of the administrative sector.
Stochastic control for optimal power flow in islanded microgridIJECEIAES
The problem of optimal power flow (OPF) in an islanded mircrogrid (MG) for hybrid power system is described. Clearly, it deals with a formulation of an analytical control model for OPF. The MG consists of wind turbine generator, photovoltaic generator, and diesel engine generator (DEG), and is in stochastic environment such as load change, wind power fluctuation, and sun irradiation power disturbance. In fact, the DEG fails and is repaired at random times so that the MG can significantly influence the power flow, and the power flow control faces the main difficulty that how to maintain the balance of power flow? The solution is that a DEG needs to be scheduled. The objective of the control problem is to find the DEG output power by minimizing the total cost of energy. Adopting the Rishel’s famework and using the Bellman principle, the optimality conditions obtained satisfy the Hamilton-Jacobi-Bellman equation. Finally, numerical examples and sensitivity analyses are included to illustrate the importance and effectiveness of the proposed model.
Electronics system thermal management optimization using finite element and N...TELKOMNIKA JOURNAL
The demand for high-performance, smaller-sized, and multi-functional electronics component
poses a great challenge to the thermal management issues in a printed circuit board (PCB)
design. Moreover, this thermal problem can affect the lifespan, performance, and the reliability of
the electronic system. This project presents the simulation of an optimal thermal distribution for various
samples of electronics components arrangement on PCB. The objectives are to find the optimum
components arrangement with minimal heat dissipation and cover small PCB area. Nelder-Mead
Optimization (NMO) with Finite Element method has been used to solve these multi-objective problems.
The results show that with the proper arrangement of electronics components, the area of PCB has been
reduced by 26% while the temperature of components is able to reduce up to 40%. Therefore, this study
significantly benefits for the case of thermal management and performance improvement onto the electronic
product and system.
An Improved Particle Swarm Optimization for Proficient Solving of Unit Commit...IDES Editor
This paper presents a new approach to solving the
short-term unit commitment problem using an improved
Particle Swarm Optimization (IPSO). The objective of this
paper is to find the generation scheduling such that the total
operating cost can be minimized, when subjected to a variety
of constraints. This also means that it is desirable to find the
optimal generating unit commitment in the power system for
the next H hours. PSO, which happens to be a Global
Optimization technique for solving Unit Commitment
Problem, operates on a system, which is designed to encode
each unit’s operating schedule with regard to its minimum
up/down time. In this, the unit commitment schedule is coded
as a string of symbols. An initial population of parent
solutions is generated at random. Here, each schedule is
formed by committing all the units according to their initial
status (“flat start”). Here the parents are obtained from a predefined
set of solution’s i.e. each and every solution is adjusted
to meet the requirements. Then, a random decommitment is
carried out with respect to the unit’s minimum down times. A
thermal Power System in India demonstrates the effectiveness
of the proposed approach; extensive studies have also been
performed for different power systems consist of 10, 26, 34
generating units. Numerical results are shown comparing the
cost solutions and computation time obtained by using the
IPSO and other conventional methods like Dynamic
Programming (DP), Legrangian Relaxation (LR) in reaching
proper unit commitment.
Comparative Study on the Performance of A Coherency-based Simple Dynamic Equi...IJAPEJOURNAL
Earlier, a simple dynamic equivalent for a power system external area containing a group of coherent generators was proposed in the literature. This equivalent is based on a new concept of decomposition of generators and a two-level generator aggregation. With the knowledge of only the passive network model of the external area and the total inertia constant of all the generators in this area, the parameters of this equivalent are determinable from a set of measurement data taken solely at a set of boundary buses which separates this area from the rest of the system. The proposed equivalent, therefore, does not require any measurement data at the external area generators. This is an important feature of this equivalent. In this paper, the results of a comparative study on the performance of this dynamic equivalent aggregation with the new inertial aggregation in terms of accuracy are presented. The three test systems that were considered in this comparative investigation are the New England 39-bus 10-generator system, the IEEE 162-bus 17-generator system and the IEEE 145-bus 50-generator system.
Optimal power flow based congestion management using enhanced genetic algorithmsIJECEIAES
Congestion management (CM) in the deregulated power systems is germane and of central importance to the power industry. In this paper, an optimal power flow (OPF) based CM approach is proposed whose objective is to minimize the absolute MW of rescheduling. The proposed optimization problem is solved with the objectives of total generation cost minimization and the total congestion cost minimization. In the centralized market clearing model, the sellers (i.e., the competitive generators) submit their incremental and decremental bid prices in a real-time balancing market. These can then be incorporated in the OPF problem to yield the incremental/ decremental change in the generator outputs. In the bilateral market model, every transaction contract will include a compensation price that the buyer-seller pair is willing to accept for its transaction to be curtailed. The modeling of bilateral transactions are equivalent to the modifying the power injections at seller and buyer buses. The proposed CM approach is solved by using the evolutionary based Enhanced Genetic Algorithms (EGA). IEEE 30 bus system is considered to show the effectiveness of proposed CM approach.
Reinforcement Learning for Building Energy Optimization Through Controlling o...Power System Operation
This paper presents a novel methodology to control HVAC system and minimize energy cost
on the premise of satisfying power system constraints. A multi-agent architecture based on game theory and
reinforcement learning is developed so as to reduce the cost and computational complexity of the microgrid.
The multi-agent architecture comprising agents, state variables, action variables, reward function and cost
game is formulated. The paper lls the gap between multi-agent HVAC systems control and power system
optimization and planning. The results and analysis indicate that the proposed algorithm is benecial to deal
with the problem of ``curse of dimensionality'' for multi-agent microgrid HVAC system control and speed
up learning of unknown power system conditions.
A progressive domain expansion method for solving optimal control problemTELKOMNIKA JOURNAL
Electricity generation at the hydropower stations in Nigeria has been below the expected value. While the hydro stations have a capacity to generate up to 2,380 MW, the daily average energy generated in 2017 was estimated at around 846 MW. A factor responsible for this is the lack of a proper control system to manage the transfer of resources between the cascaded Kainji-Jebba Hydropower stations operating in tandem. This paper addressed the optimal regulation of the operating head of the Jebba hydropower reservoir in the presence of system constraints, flow requirement and environmental factors that are weather-related. The resulting two-point boundary value problem was solved using the progressive expansion of domain technique as against the shooting or multiple shooting techniques. The results provide the optimal inflow required to keep the operating head of the Jebba reservoir at a nominal level, hence ensuring that the maximum number of turbo-alternator units are operated.
Critical Review of Different Methods for Siting and Sizing Distributed-genera...TELKOMNIKA JOURNAL
Due to several benefits attached to distributed generators such as reduction in line losses,
improved voltage profile, reliable system etc., the study on how to optimally site and size distributed
generators has been on the increase for more than two decades. This has propelled several
researchers to explore various scientific and engineering powerful simulation tools, valid and reliable
scientific methods like analytical, meta-heuristic and hybrid methods to optimally place and size
distributed generator(s) for optimal benefits. This study gives a critical review of different methods
used in siting and sizing distributed generators alongside their results, test systems and gaps in
literature.
Achieving Energy Proportionality In Server ClustersCSCJournals
a great amount of interests in the past few years. Energy proportionality is a principal to ensure that energy consumption is proportional to the system workload. Energy proportional design can effectively improve energy efficiency of computing systems. In this paper, an energy proportional model is proposed based on queuing theory and service differentiation in server clusters, which can provide controllable and predictable quantitative control over power consumption with theoretically guaranteed service performance. Futher study for the transition overhead is carried out corresponding strategy is proposed to compensate the performance degradation caused by transition overhead. The model is evaluated via extensive simulations and is justified by the real workload data trace. The results show that our model can achieve satisfied service performance while still preserving energy efficiency in the system.
The performance expectations for commercial wind turbines, from a variety of geograph- ical regions with differing wind regimes, present significant techno-commercial challenges to manufacturers. The determination of which commercial turbine types perform the best under differing wind regimes can provide unique insights into the complex demands of a concerned target market. In this paper, a comprehensive methodology is developed to explore the suitability of commercially available wind turbines (when operating as a group/array) to the various wind regimes occurring over a large target market. The three major steps of this methodology include: (i) characterizing the geographical variation of wind regimes in the target market, (ii) determining the best performing turbines (in terms of minimum COE accomplished) for different wind regimes, and (iii) developing a metric to investigate the performance-based expected market suitability of currently available tur- bine feature combinations. The best performing turbines for different wind regimes are determined using the Unrestricted Wind Farm Layout Optimization (UWFLO) method. Expectedly, the larger sized and higher rated-power turbines provide better performance at lower average wind speeds. However, for wind resources higher than class-4, the perfor- mances of lower-rated power turbines are fairly competitive, which could make them better choices for sites with complex terrain or remote location. In addition, turbines with direct drive are observed to perform significantly better than turbines with more conventional gear-based drive-train. The market considered in this paper is mainland USA, for which wind map information is obtained from NREL. Interestingly, it is found that overall higher rated-power turbines with relatively lower tower heights are most favored in the onshore US market.
In imaging science, the photo editing software packages can alter the original images without any
detecting traces of tampering. Hence, the image forgery detection technique plays an important role in
verifying the integrity of digital image forensics for authentication. The techniques such as
watermarking are used for authentication but it can be modified through third parties attack through
extraction. Malicious and digital imaging (digital products) tamper detection is the subject of this
article. In particular, we focus on a special type of digital forgery detection - copy attack campaign, in
which part of the image is copied and pasted into the image and the cover features a large image of
intentions another. In this paper, we investigate the dynamic forged copy detection problem, and
describes a highly efficient and reliable detection method that based on image source ANN
identification.. Even when the region is enhanced copy / retouching and background merger, and the
method can successfully identify counterfeit forgery when images are saved in a lossy format (such as
JPEG). The performance of the method's performance several forged images.
RSA is one of the most popular Public Key Cryptography based algorithm mainly used for digital
signatures, encryption/decryption etc. It is based on the mathematical scheme of factorization of very large
integers which is a compute-intensive process and takes very long time as well as power to perform.
Several scientists are working throughout the world to increase the speedup and to decrease the power
consumption of RSA algorithm while keeping the security of the algorithm intact. One popular technique
which can be used to enhance the performance of RSA is parallel programming. In this paper we are
presenting the survey of various parallel implementations of RSA algorithm involving variety of hardware
and software implementations.
A N A UTOMATION S YSTEM F OR T RACKING C OMMUNITY C ERTIFICATEijcsa
The e-governance project is an online web portal wh
ich aims to serve the public by providing their
community certificate. The objective of achieving e
-governance goes far beyond mere computerization of
standalone back office operations. This portal is d
eveloped to validate the information provided by th
e
public by governing authorities such as Tahsildar a
nd Revenue Inspector etc., It includes a provision
for
the public to track the status of the requested cer
tificate with certificate number whether it is appr
oved or
rejected or in progress via online. The goal for es
tablishing e-governance is to improve the user
friendliness of the administrative system and to si
mplify and improve the efficiency, reliability and
transparency of the administrative sector.
The foremost by-product of this paper is the automation of geological undertakings, for instance, dealing
with exceptionally thin sections of rocks that were subjected to deformation alongside finite steps of time
which can be recorded in video for later analysis using image processing and numerical analysis
procedures. Markers are used in order to trace gradients of deformation over a sample and study other
mechanical properties. Image processing and video sequence analysis can be a very powerful investigation
tool and this paper shows preliminary results from its use on microtectonics. The proposed algorithm is a
combination of two well-known approaches: feature extraction and block matching.
Image Inpainting System Model Based on Evaluationijcsa
Image segmentation algorithm and inpainting algorithm are the key ingredients in the process of inpainting
after studying many image-inpainting algorithms. Therefore, analyzing, comparing and verifying the
segment algorithm and inpainting algorithms, the system model which owns segment and repair evaluation
function is constructed, so it can optimize the segmentation algorithms and the inpainting algorithms;
finally make the inpainting result better. There is segmentation module and inpainting module in the system
model, the former is to segment damaged area, and the latter is to repair image. They adopt expert system,
which extract image characteristics and optimize segmentation algorithms and inpainting algorithms by
heuristic rules in the knowledge database, evaluate the result of the inpainting which can feedback the
heuristic rules for selecting better algorithms, finally adopt the best segmentation and inpainting algorithm.
System model synthesizes two key ingredients of segmentation and inpainting, so that it can enhance the
inpainting effect, and that the system will be constructed actually need to further study and to carry out.
Hadoop is an open source implementation of the MapReduce Framework in the realm of distributed processing.
A Hadoop cluster is a unique type of computational cluster designed for storing and analyzing large datasets
across cluster of workstations. To handle massive scale data, Hadoop exploits the Hadoop Distributed File
System termed as HDFS. The HDFS similar to most distributed file systems share a familiar problem on data
sharing and availability among compute nodes, often which leads to decrease in performance. This paper is an
experimental evaluation of Hadoop's computing performance which is made by designing a rack aware cluster
that utilizes the Hadoop’s default block placement policy to improve data availability. Additionally, an adaptive
data replication scheme that relies on access count prediction using Langrange’s interpolation is adapted to fit
the scenario. To prove, experiments were conducted on a rack aware cluster setup which significantly reduced
the task completion time, but once the volume of the data being processed increases there is a considerable
cutback in computational speeds due to update cost. Further the threshold level for balance between the update
cost and replication factor is identified and presented graphically.
EVALUATION OF PARTICLE SWARM OPTIMIZATION ALGORITHM IN PREDICTION OF THE CAR ...ijcsa
Road traffic accidents are the most common accidents that annually Endangers lives of many people in the world. Our country Iran is one of the countries with highest incidence and mortality due to accidents that has been introduced. So it’s requires identification of underlay in dimensions in this field. Due to the increasing amount of car accidents in order to increase volume of information related to car accidents and needs to explore and reveal hidden dependencies and very long time among this information. So using traditional methods to discover these complex relations don't response between involved factors and we need to use new techniques. Considering that main aim of this paper is to find best relationship between volumes of information in shortest time. So, in this paper, we classify accidents in West Azerbaijan province in Iran by accident type (damage, injury, death) and we describe it by using Particle Swarm Optimization (PSO) algorithm
A SURVEY ON OPTIMIZATION APPROACHES TO TEXT DOCUMENT CLUSTERINGijcsa
Text Document Clustering is one of the fastest growing research areas because of availability of huge amount of information in an electronic form. There are several number of techniques launched for clustering documents in such a way that documents within a cluster have high intra-similarity and low inter-similarity to other clusters. Many document clustering algorithms provide localized search in effectively navigating, summarizing, and organizing information. A global optimal solution can be obtained by applying high-speed and high-quality optimization algorithms. The optimization technique performs a globalized search in the entire solution space. In this paper, a brief survey on optimization approaches to text document clustering is turned out.
Application of hidden markov model in question answering systemsijcsa
By the increase of the volume of the saved information on web, Question Answering (QA) systems have been very important for Information Retrieval (IR). QA systems are a specialized form of information retrieval. Given a collection of documents, a Question Answering system attempts to retrieve correct answers to questions posed in natural language. Web QA system is a sample of QA systems that in this system answers retrieval from web environment doing. In contrast to the databases, the saved information on web does not follow a distinct structure and are not generally defined. Web QA systems is the task of automatically answering a question posed in Natural Language Processing (NLP). NLP techniques are used in applications that make queries to databases, extract information from text, retrieve relevant documents from a collection, translate from one language to another, generate text responses, or recognize spoken words converting them into text. To find the needed information on a mass of the non-structured information we have to use techniques in which the accuracy and retrieval factors are implemented well. In this paper in order to well IR in web environment, The QA system in designed and also implemented based on the Hidden Markov Model (HMM)
TEST CASE PRIORITIZATION USING FUZZY LOGIC BASED ON REQUIREMENT PRIORITIZINGijcsa
Boolean expressions are popularly used for modelling decisions or conditions in specifications or source
programs and they are very much prone to introduction of faults. Even for a Boolean expression with few
numbers of literals the possible number of test cases can be quite large. Boolean expressions with n
variables require 2n
test cases to distinguish from faulty expression. In practice, n can be quite large and
there are examples of specification having Boolean expressions with 30 or more variables. To test a system
based on Boolean specification in limited time, it is not possible to execute all test cases so prioritization is
required which leads to early fault detection in testing life cycle. There are various testing strategies for
generation of test cases for Boolean specifications like MUMCUT, which generate fewer test cases then 2
n
with high probabilities of finding errors but their prioritization are not considering the criteria from user’s
perspective. We have proposed a new approach which prioritizes test cases based on requirement
prioritization. Our aim is to find the severe faults from user’s perspective early in the testing process and
hence to improve the quality of the software .This paper considers method for assigning weight value on the
basis of factors which generates the criteria for test case prioritization for Boolean Specifications. These
factors are: Business Value Measure (BVM), Project Change Volatility (PCV), and Development
Complexity (DC). Priority is assigned to test cases based upon these factors using fuzzy logic model.
EVENT DRIVEN ROUTING PROTOCOLS FOR WIRELESS SENSOR NETWORK- A SURVEYijcsa
Advances in embedded systems have resulted in the development of wireless sensor networks, which not
only provide unique opportunities for monitoring but also controlling homes, cities and the environments.
Recent advancements in wireless sensor network have resulted into many new protocols some of them are
specifically designed for sensor network for detecting the event and routing the event related information to
the base station in efficient manner. This paper surveys recent event driven routing protocols for wireless
sensor network. We have compared various event driven routing protocols using different parameters like
Sink Centric, Node Centric, Reliability, Congestion control, Energy Efficiency, Loss reliability and loss
recovery. We have also described LEACH and MECN protocols but as they are not e
Retina is a layer which is found at the back side of the eye ball which plays main role for visualization. Any
disease in the retina leads to severe problems. Blood vessels segmentation and classification of retinal
vessels into arteries and veins is an essential thing for detection of various diseases like Diabetic
Retinography etc. This paper discusses about various existing methodologies for classification of retinal
image into artery and vein which are helpful for the detection of various diseases in retinal fundus image.
This process is basis for the AVR calculation i.e. for the calculation of average diameter of arteries to
veins. One of the symptoms of Diabetic Retinography causes abnormally wide veins and this leads to low
ratio of AVR. Diseases like high blood pressure and pancreas also have abnormal AVR. Thus classification
of blood vessels into arteries and veins is more important. Retinal fundus images are available on the
publically available Database like DRIVE [5], INSPIREAVR [6], VICAVR [7].
Genetic Algorithm for Solving the Economic Load DispatchSatyendra Singh
In this paper, comparative study of two approaches, Genetic Algorithm
(GA) and Lambda Iteration method (LIM) have been used to provide
the solution of the economic load dispatch (ELD) problem. The ELD
problem is defined as to minimize the total operating cost of a power
system while meeting the total load plus transmission losses within
generation limits. GA and LIM have been used individually for solving
two cases, first is three generator test system and second is ten
generator test system. The results are compared which reveals that GA
can provide more accurate results with fast convergence characteristics
and is superior to LIM.
TASK SCHEDULING USING AMALGAMATION OF MET HEURISTICS SWARM OPTIMIZATION ALGOR...Journal For Research
Cloud Computing is the latest networking technology and also popular archetype for hosting the application and delivering of services over the network. The foremost technology of the cloud computing is virtualization which enables of building the applications, dynamically sharing of resources and providing diverse services to the cloud users. With virtualization, a service provider can guarantee Quality of Service to the user at the same time as achieving higher server consumption and energy competence. One of the most important challenges in the cloud computing environment is the VM placemnt and task scheduling problem. This paper focus on Metaheuristic Swarm Optimisation Algorithms(MSOA) deals with the problem of VM placement and Task scheduling in cloud environment. The MSOA is a simple parallel algorithm that can be applied in different ways to resolve the task scheduling problems. The proposed algorithm is considered an amalgamation of the SO algorithm and the Cuckoo search (CS) algorithm; called MSOACS. The proposed algorithm is evaluated using Cloudsim Simulator. The results proves the reduction of the makespan and increase the utilization ratio of the proposed MSOACS algorithm compared with SOA algorithms and Randomised Allocation Allocation (RA).
task scheduling in cloud datacentre using genetic algorithmSwathi Rampur
Task scheduling and resource provisioning is the core and challenging issues in cloud environment. Processes running in the cloud environment will race for available resources in order to complete their tasks with the minimum execution time; it is clear that we need an efficient scheduling technique for mapping between processes running and available resources. In this research paper, we are presented a non-traditional optimization technique, which mimics the process of evolution and based on the mechanics of natural selection and natural genetics called Genetic algorithm (GA), which minimizes the execution time and in turn reduces computation cost. We had done comparison with Round Robin algorithm and used CloudSim toolkit for our tests, results shows that Meta heuristic GA gives better performance than other scheduling algorithm.
IMPROVED SWARM INTELLIGENCE APPROACH TO MULTI OBJECTIVE ED PROBLEMSSuganthi Thangaraj
Electrical power industry restructuring has created highly vibrant and competitive market that altered many aspects of the power industry. In this changed scenario, scarcity of energy resources, increasing power generation cost, environment concern, ever growing demand for electrical energy necessitate optimal economic dispatch. Practical economic dispatch (ED) problems have nonlinear, non-convex type objective function with intense equality and inequality constraints. The conventional optimization methods are not able to solve such problems as due to local optimum solution convergence. Metaheuristic optimization techniques especially Improved Particle Swarm Optimization (IPSO) has gained an incredible recognition as the solution algorithm for such type of ED problems in last decade. The application of IPSO in ED problem, which is considered as one of the most complex optimization problem has been summarized in present paper. This paper illustrates successful implementation of the Improved Particle Swarm Optimization (IPSO) to Economic Load Dispatch Problem (ELD). Power output of each generating unit and optimum fuel cost obtained using IPSO algorithm has been compared with conventional techniques. The results obtained shows that IPSO algorithm converges to optimal fuel cost with reduced computational time when compared to PSO and GA for the three, six and IEEE 30 bus system.
Optimum Location of DG Units Considering Operation ConditionsEditor IJCATR
The optimal sizing and placement of Distributed Generation units (DG) are becoming very attractive to researchers these days. In this paper a two stage approach has been used for allocation and sizing of DGs in distribution system with time varying load model. The strategic placement of DGs can help in reducing energy losses and improving voltage profile. The proposed work discusses time varying loads that can be useful for selecting the location and optimizing DG operation. The method has the potential to be used for integrating the available DGs by identifying the best locations in a power system. The proposed method has been demonstrated on 9-bus test system.
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.
Improvement of voltage profile for large scale power system using soft comput...TELKOMNIKA JOURNAL
In modern power system operation, control, and planning, reactive power as part of power system component is very important in order to supply electrical load such as an electric motor. However, the reactive current that flows from the generator to load demand can cause voltage drop and active power loss. Hence, it is essential to install a compensating device such as a shunt capacitor close to the load bus to improve the voltage profile and decrease the total power loss of transmission line system. This paper presents the application of a genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC)) to obtain the optimal size of the shunt capacitor where those capacitors are located on the critical bus. The effectiveness of the proposed technique is examined by utilizing Java-Madura-Bali (JAMALI) 500 kV power system grid as the test system. From the simulation results, the PSO and ABC algorithms are providing satisfactory results in obtaining the capacitor size and can reduce the total power loss of around 15.873 MW. Moreover, a different result is showed by the GA approach where the power loss in the JAMALI 500kV power grid can be compressed only up to 15.54 MW or 11.38% from the power system operation without a shunt capacitor. The three soft computing techniques could also maintain the voltage profile within 1.05 p.u and 0.95 p.u.
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.
Profit based unit commitment for GENCOs using Parallel PSO in a distributed c...IDES Editor
In the deregulated electricity market, each
generating company has to maximize its own profit by
committing suitable generation schedule termed as profit
based unit commitment (PBUC). This article proposes a
Parallel Particle Swarm Optimization (PPSO) solution to the
PBUC problem. This method has better convergence
characteristics in obtaining optimum solution. The proposed
approach uses a cluster of computers performing parallel
operations in a distributed environment for obtaining the
PBUC solution. The time complexity and the solution quality
with respect to the number of processors in the cluster are
thoroughly tested. The method has been applied to 10 unit
system and the results show that the proposed PPSO in a
distributed cluster constantly outperforms the other methods
which are available in the literature.
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.
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.
A MODIFIED ANT COLONY ALGORITHM FOR SOLVING THE UNIT COMMITMENT PROBLEMAEIJjournal2
Solving the unit commitment (UC) problem is one of the most complicated issues in power systems that its
exact solving can be calculated by perfect counting of entire possible compounds of generative units. UC is
equated as a nonlinear optimization with huge size. Purpose of solving this problem is to programming the
optimization of the generative units to minimize the full action cost regarding problem constraints. In this
article, a modified version of ant colony optimization (MACO) is introduced for solving the UC problem in
a power system. ACO algorithm is a powerful optimization method which has the capability of fleeing from
local minimums by performing flexible memory system. The efficiency of proposed method in two power
system containing 4 and 10 generative units is indicated. Comparison of obtained results from the proposed
method with results of the past well-known methods is a proof for suitability of performing the introduced
algorithm in economic input and output of generative units.
A MODIFIED ANT COLONY ALGORITHM FOR SOLVING THE UNIT COMMITMENT PROBLEMAEIJjournal2
Solving the unit commitment (UC) problem is one of the most complicated issues in power systems that its exact solving can be calculated by perfect counting of entire possible compounds of generative units. UC is equated as a nonlinear optimization with huge size. Purpose of solving this problem is to programming the optimization of the generative units to minimize the full action cost regarding problem constraints. In this article, a modified version of ant colony optimization (MACO) is introduced for solving the UC problem in a power system. ACO algorithm is a powerful optimization method which has the capability of fleeing from
local minimums by performing flexible memory system. The efficiency of proposed method in two power system containing 4 and 10 generative units is indicated. Comparison of obtained results from the proposed
method with results of the past well-known methods is a proof for suitability of performing the introduced
algorithm in economic input and output of generative units.
Advanced Energy: An International Journal (AEIJ)AEIJjournal2
Solving the unit commitment (UC) problem is one of the most complicated issues in power systems that its
exact solving can be calculated by perfect counting of entire possible compounds of generative units. UC is
equated as a nonlinear optimization with huge size. Purpose of solving this problem is to programming the
optimization of the generative units to minimize the full action cost regarding problem constraints. In this
article, a modified version of ant colony optimization (MACO) is introduced for solving the UC problem in
a power system. ACO algorithm is a powerful optimization method which has the capability of fleeing from
local minimums by performing flexible memory system. The efficiency of proposed method in two power
system containing 4 and 10 generative units is indicated. Comparison of obtained results from the proposed
method with results of the past well-known methods is a proof for suitability of performing the introduced
algorithm in economic input and output of generative units.
A Simple Approach for Optimal Generation Scheduling to Maximize GENCOs Profit...IJAPEJOURNAL
In this paper an attempt has been made to solve the profit based unit commitment problem (PBUC) using pre-prepared power demand (PPD) table with an artificial bee colony (ABC) algorithm. The PPD-ABC algorithm appears to be a robust and reliable optimization algorithm for the solution of PBUC problem. The profit based unit commitment problem is considered as a stochastic optimization problem in which the objective is to maximize their own profit and the decisions are needed to satisfy the standard operating constraints. The PBUC problem is solved by the proposed methodology in two stages. In the first step, the unit commitment scheduling is performed by considering the pre-prepared power demand (PPD) table and then the problem of fuel cost and revenue function is solved using ABC Algorithm. The PPD table suggests the operator to decide the units to be put into generation there by reducing the complexity of the problem. The proposed approach is demonstrated on 10 units 24 hour and 50 units 24 hour test systems and numerical results are tabulated. Simulation result shows that this approach effectively maximizes the GENCO’s profit than those obtained by other optimizing methods.
Economic dispatch by optimization techniquesIJECEIAES
The current paper offers the solution strategy for the economic dispatch problem in electric power system implementing ant lion optimization algorithm (ALOA) and bat algorithm (BA) techniques. In the power network, the economic dispatch (ED) is a short-term calculation of the optimum performance of several electricity generations or a plan of outputs of all usable power generation units from the energy produced to fulfill the necessary demand, although equivalent and unequal specifications need to be achieved at minimal fuel and carbon pollution costs. In this paper, two recent meta-heuristic approaches are introduced, the BA and ALOA. A rigorous stochastically developmental computing strategy focused on the action and intellect of ant lions is an ALOA. The ALOA imitates ant lions' hunting process. The introduction of a numerical description of its biological actions for the solution of ED in the power framework. These algorithms are applied to two systems: a small scale three generator system and a large scale six generator. Results show were compared on the metrics of convergence rate, cost, and average run time that the ALOA and BA are suitable for economic dispatch studies which is clear in the comparison set with other algorithms. Both of these algorithms are tested on IEEE-30 bus reliability test system.
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 power flow with distributed energy sources using whale optimization a...IJECEIAES
Renewable energy generation is increasingly attractive since it is non-polluting and viable. Recently, the technical and economic performance of power system networks has been enhanced by integrating renewable energy sources (RES). This work focuses on the size of solar and wind production by replacing the thermal generation to decrease cost and losses on a big electrical power system. The Weibull and Lognormal probability density functions are used to calculate the deliverable power of wind and solar energy, to be integrated into the power system. Due to the uncertain and intermittent conditions of these sources, their integration complicates the optimal power flow problem. This paper proposes an optimal power flow (OPF) using the whale optimization algorithm (WOA), to solve for the stochastic wind and solar power integrated power system. In this paper, the ideal capacity of RES along with thermal generators has been determined by considering total generation cost as an objective function. The proposed methodology is tested on the IEEE-30 system to ensure its usefulness. Obtained results show the effectiveness of WOA when compared with other algorithms like non-dominated sorting genetic algorithm (NSGA-II), grey wolf optimization (GWO) and particle swarm optimization-GWO (PSOGWO).
Evolutionary algorithm solution for economic dispatch problemsIJECEIAES
A modified firefly algorithm (FA) was presented in this paper for finding a solution to the economic dispatch (ED) problem. ED is considered a difficult topic in the field of power systems due to the complexity of calculating the optimal generation schedule that will satisfy the demand for electric power at the lowest fuel costs while satisfying all the other constraints. Furthermore, the ED problems are associated with objective functions that have both quality and inequality constraints, these include the practical operation constraints of the generators (such as the forbidden working areas, nonlinear limits, and generation limits) that makes the calculation of the global optimal solutions of ED a difficult task. The proposed approach in this study was evaluated in the IEEE 30-Bus test-bed, the evaluation showed that the proposed FA-based approach performed optimally in comparison with the performance of the other existing optimizers, such as the traditional FA and particle swarm optimization. The results show the high performance of the modified firefly algorithm compared to the other methods.
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.
AN INTEGER-LINEAR ALGORITHM FOR OPTIMIZING ENERGY EFFICIENCY IN DATA CENTERSijfcstjournal
Nowadays, to meet the enormous computational requests, energy consumption, the largest part which is
related to idle resources, is strictly increased as a great part of a data center's budget. So, minimizing
energy consumption is one of the most important issues in the field of green computing. In this paper, we
present a mathematical model formed as integer-linear programming which minimizes energy consumption
and maximizes user’s satisfaction, simultaneously. However, migration variables, as principal decision
variables of the model, can be relaxed to continuous activities in some practical problems. This constraint
relaxation helps a decision maker to find faster solutions that are usually good approximations for
optimum. Near feasible solutions (infeasible solutions that are desirably close to the feasible region) have
been investigated as another relaxation considering the kind of solutions. For this purpose, we initially
present a measure to evaluate the amount of infeasibility of solutions and then let the model consider an
extended region including solutions with remissible infeasibility, if necessary.
Similar to A new approach to the solution of economic dispatch using particle Swarm optimization with simulated annealing (20)
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
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A new approach to the solution of economic dispatch using particle Swarm optimization with simulated annealing
1. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.3, June 2013
DOI:10.5121/ijcsa.2013.3304 37
A NEW APPROACH TO THE SOLUTION OF
ECONOMIC DISPATCH USING PARTICLE
SWARM OPTIMIZATION WITH SIMULATED
ANNEALING
V.Karthikeyan1
S.Senthilkumar2
and V.J.Vijayalakshmi3
1Department of Electronics and communication engineering, SVSCE,
Coimbatore India
karthick77keyan@gmail.com
2Department of Electronics and communication engineering, SVSCE,
Coimbatore India
sentheeyan@yahoo.co.in
3Department of Electrical and electronics engineering, SKCET, Coimbatore,
India
vijik810@gmail.com
ABSTRACT
Economic Dispatch is an important optimization task in power system. It is the process of allocating
generation among the committed units such that the constraints imposed are satisfied and the energy
requirements are minimized. More just, the soft computing method has received supplementary
concentration and was used in a quantity of successful and sensible applications. Here, an attempt has
been made to find out the minimum cost by using Particle Swarm Optimization (PSO) Algorithm using the
data of three generating units. In this work, data has been taken such as the loss coefficients with the max-
min power limit and cost function. PSO and Simulated Annealing (SA) are applied to find out the minimum
cost for different power demand. When the results are compared with the traditional technique, PSO seems
to give a better result with better convergence characteristic. All the methods are executed in MATLAB
environment. The effectiveness and feasibility of the proposed method were demonstrated by three
generating unit’s case study. The experiment showed encouraging results, suggesting that the proposed
approach of computation is capable of efficiently determining higher quality solutions addressing
economic dispatch problems.
KEYWORDS
Economic dispatch, particle swarm optimization, prohibited zones, ramp rate limits, simulated annealing.
1. INTRODUCTION
Power systems should be operated under a high degree of economy for competition of
deregulation. Unit commitment is an important optimization task addressing this crucial concern
for power system operations. Since Economic Dispatch (ED) is the fundamental issue during unit
commitment process, it should be important to obtain a higher quality solution from ED
efficiently. In essence, the objective of ED is to minimize total generation costs, while satisfying
power demands and constraints [1] Previous solutions to ED problems have applied various
mathematical programming methods and optimization techniques, such as the Lambda-iteration
method [2], base point and participation factors method [3], the gradient method [4], and the
2. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.3, June 2013
38
Newton method [2]. Unfortunately, these methods contained essential assumptions which were
the incremental costs of the generators should be monotonically increasing functions [2]. These
assumptions proved to be infeasible for practical implementation because of their nonlinear
characteristics of prohibited zones or valve-point effects in real generators. Therefore, dynamic
programming [5], nonlinear programming [6], and their modification techniques for solving ED
issues have been presented. However, these methods may cause dimensional problem when
applied to modern power systems due to the large number of generators. In the past decade,
several computational algorithm techniques, such as Genetic Algorithms (GAs) [7], [8],
Simulated Annealing (SA) [9], Tabu Searches (TS) [10], and Artificial Neural Networks (ANNs)
[11], [12] encompass been second-hand to tackle power optimization subjects. These algorithms
are in the form of probabilistic heuristics, with global search properties. Though GA methods
have been employed successfully to solve complex optimization problems, recent research has
identified deficiencies in its performance. This degradation in efficiency is apparent in
applications with highly epistatic objective functions (i.e., when optimized parameters are highly
correlated), thereby, hampering crossover and mutation operations and compromising the
improved fitness of offspring because population chromosomes contain similar structures. In
addition, their average fitness becomes high toward the end of the evolutionary process [17].
Moreover, the premature convergence of GA degrades its performance by reducing its search
capability, leading to a higher probability of being trapped to a local optimum [22]. Recently, a
global optimization technique called Particle Swarm Optimization (PSO) [13]–[15] has been used
to solve real time issues and aroused researchers’ interest due to its flexibility and efficiency.
Limitations of the classic greedy search technique, which restricts allowed forms of fitness
functions, as well as continuity of the variables used, can be entirely eliminated. The PSO, first
introduced by Kennedy and Eberhart [16], is a modern heuristic algorithm developed through the
simulation of a simplified social system. It was found to be robust in solving continuous
nonlinear optimization problems [19], [21]. In general, the PSO method is faster than the SA
method because the PSO contains parallel search techniques. However, similar to the GA, the
main adversity of the PSO is premature convergence, which might occur when the particle and
group best solutions are trapped into local minimums during the search process. Localization
occurs because particles have the tendency to fly to local, or near local, optimums, therefore,
particles will concentrate to a small region and the global exploration ability will be weakened.
On the contrary, the most significant characteristic of SA is its probabilistic jumping property,
called the metropolis process. However, by adjusting the temperature, the metropolis process can
be controlled. It has been theoretically proven that the SA technique converges asymptotically to
the global optimum solution with probability, ONE [18], [19], provided that certain conditions are
satisfied. Therefore, a novel SA-PSO approach is proposed in this paper. The salient features of
PSO and SA are hybrid to create an innovative approach, which can generate high-quality
solutions within shorter calculation times and offers more stable convergence characteristics.
Moreover, to consider the nonlinear characteristics of a generator, such as prohibited operating
zones and valve-point effects for actual power system operations, an effective coding scheme for
particle representation is also proposed to prevent constraints violation during the SA-PSO
process. The feasibility of the proposed method was demonstrated on four different systems and
then compared with the real-coded PSO [15], GA [8], [30], and evolutionary algorithm [29]
methods regarding solution quality and computational efficiency.
2. PROBLEM FORMULATIONS
The objective of ED is to minimize the total generation costs of a power system over an
appropriate period (usually one hour), while satisfying various constraints. Cost efficiency is the
most important sub problem of power system operations. Due to the highly nonlinearity
characteristics of power systems and generators, ED belongs to a class of nonlinear programming
optimization methods containing equality and inequality constraints. Practically speaking, while
the scheduled combined units for each specific period of operation are listed from unit
3. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.3, June 2013
39
commitment, the ED planning must perform the optimal generation dispatch among the operating
units in order to satisfy the system load demands and practical operation constraints of generators,
which include ramp rate limits, maximum and minimum limits, and prohibited operating zones.
Generally, the generation cost function is usually expressed as a quadratic polynomial. However,
it is more practical to consider the valve-point effects for fossil-fuel-based plants. Therefore, the
cost function considered in this paper can be represented as follows: The Let Ci mean the cost,
expressed for example in dollars per hour, of producing energy in the generator unit I. The total
controllable system production cost therefore will be
(1)
The generated real power PGi accounts for the major influence on Ci. The individual real
generation are raised by increasing the prime mover torques, and this requires a cost of increased
expenditure of fuel. The reactive generations QGi do not have any measurable influence on ci
because they are controlled by controlling by field current. The individual production cost ci of
generators unit I is therefore for all practical purposes a function only of PGi, and for the overall
controllable production cost, we thus have
i (PGi) (2)
When the cost function C can be written as a sum of terms where each term depends only upon
one independent variable.
2.1. System Constraints
Broadly speaking there are two types of constraints
i) Equality constraints
ii) Inequality constraints
The inequality constraints are of two types (i) Hard type and, (ii) Soft type. The hard type
are those which are definite and specific like the tapping range of an on-load tap changing
transformer whereas soft type are those which have some flexibility associated with them like the
nodal voltages and phase angles between the nodal voltages, etc. Soft inequality constraints have
been very efficiently handled by penalty function methods.
2.1.1 Equality Constraints
From observation we can conclude that cost function is not affected by the reactive power
demand. So the full attention is given to the real power balance in the system. Power balance
requires that the controlled generation variables PGi abbey the constraints equation shown in (3)
Pd = i (PGi) (3)
2.1.2 Inequality Constraints
The Inequality Constraints of various cases are given below.
2.1.2.1 Generator Constraints
The KVA loading in a generator is given by and this should not exceed a pre
specified value of power because of the temperature rise conditions
4. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.3, June 2013
40
• The maximum active power generation of a source is limited again by thermal
consideration and also minimum power generation is limited by the flame instability of a
boiler. If the power output of a generator for optimum operation of the system is less than
a pre-specified value P min , the unit is not put on the bus bar because it is not possible to
generat that low value of power from the unit. Hence the generator power P cannot be
outside the range stated by the inequalit Pmin P Pmax
• Similarly the maximum and minimum reactive power generation of a source is limited.
The maximum reactive power is limited because of overheating of rotor and minimum is
limited because of the stability limit of machine. Hence the generator powers Pp cannot
be outside the range stated by inequality, i.e. Qp min Qp Qp max
2.1.2.2 Voltage Constraints
It is essential that the voltage magnitudes and phase angles at various nodes should vary within
certain limits. The normal operating angle of transmission lies between 30 to 45 degrees for
transient stability reasons. A lower limit of delta assures proper utilization of transmission
capacity.
2.1.2.3 Running Spare Capacity Constraints
These constraints are required to meet
a) The forced outages of one or more alternators on the system and
b) The unexpected load on the system
The total generation should be such that in addition to meeting load demand and losses a
minimum spare capacity should be available i.e. G ≥ Pp + Pso
Where G is the total generation and PSO is some pre-specified power. A well planned system is
one in which this spare capacity PSO is minimum.
2.1.2.4 Transmission Line Constraints
The flow of active and reactive power through the transmission line circuit is limited by
the thermal capability of the circuit and is expressed as,
Cp ≤ Cp max
Where Cp max is the maximum loading capacity of the line.
2.1.2.5 Network security constraints
If initially a system is operating satisfactorily and there is an outage, may be scheduled or
forced one, It is natural that is an outage, may be scheduled or forced one, it is natural that some
of the constraints of the system will be violated. The complexity of these constraints (in terms of
10 numbers of constraints) is increased when a large system is under study. In this a study is to be
made with outage of one branch at a time and then more than one branch at a time. The natures of
constraints are same as voltage and transmission line constraints.
5. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.3, June 2013
41
3. PROPOSED METHOD
3.1 PSO - An Optimization Tool
Particle swarm optimization (PSO) is a population based stochastic optimization technique
developed by Dr.Ebehart and Dr. Kennedy in 1995, inspired by social behaviour of bird flocking
or fish schooling. PSO shares many similarities with evolutionary computation techniques such as
Genetic Algorithms (GA). The system is initialized with a population of random solutions and
searches for optima by updating generations. However, unlike GA, PSO has no evolution
operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly
through the problem space by following the current optimum particles. The detailed information
will be given in following sections. Compared to GA, the advantages of PSO are that PSO is easy
to implement and there are few parameters to adjust.
3.2 Background of Simulated Annealing
For certain problems, Simulated Annealing (SA) may be more efficient than exhaustive
enumeration — provided that the goal is merely to find an acceptably good solution in a fixed
amount of time, rather than the best possible solution. The name and inspiration come from
annealing in metallurgy, a technique involving heating and controlled cooling of a material to
increase the size of its crystals and reduce their defects. The heat causes the atoms to become
unstuck from their initial positions (a local minimum of the internal energy) and wander
randomly through states of higher energy; the slow cooling gives them more chances of finding
configurations with lower internal energy than the initial one. By analogy with this physical
process, each step of the SA algorithm replaces the current solution by a random "nearby"
solution, chosen with a probability that depends both on the difference between the corresponding
function values and also on a global parameter T (called the temperature), that is gradually
decreased during the process. The dependency is such that the current solution changes almost
randomly when T is large, but increasingly "downhill" (for a minimization problem) as T goes to
zero. The allowance for "uphill" moves potentially saves the method from becoming stuck at
local optima—which are the bane of greedier methods. The method was independently described
by Scott Kirkpatrick, C. Daniel Gelatt and Mario P. Vecchi in 1983 and by Vlado Černý in 1985.
The method is an adaptation of the Metropolis-Hastings algorithm, a Monte Carlo method to
generate sample states of a thermodynamic system, invented by M.N. Rosenbluth in a paper by
N. Metropolis et al. in 1953.
3.3 Particle Swarm Optimization
PSO simulates the behaviours 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. So what's the best strategy to find the food? 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. In PSO, each single solution is a "bird" in the search space. We call it "particle". All of
particles have fitness values, which are evaluated by the fitness function to be optimized, and
have velocities, which direct the flying of the particles. The particles fly through the problem
space by following the current optimum particles. PSO is initialized with a group of random
particles (solutions) 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
6. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.3, June 2013
42
population as its topological neighbours, 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
(4) and (5).
Vi
(u+1)
= w * Vi
(u)
+ C1 * rand( )* (pbesti - Pi
(u)
) + C2 * rand( ) * (gbesti - Pi
(u)
) (4)
Pi
(u+1)
= Pi
(u)
+ Vi
(u+1)
(5)
Where The term rand ( )* (pbesti - Pi
(u)
) is called particle memory influence.
The term rand( ) * (gbesti - Pi
(u)
) is called swarm influence.
Vi
(u)
is the velocity of ith
particle at iteration ‘u’ must lie in the range Vmin ≤ Vi ≤ Vmax
In general, the inertia weight w is set according to the following equation,
` (6)
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
7. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.3, June 2013
43
Figure1. Flow Chart for Particle Swarm Optimization
4. SIMULATED ANNEALING AND PSO
Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization
problem of locating a good approximation to the global optimum of a given function in a large
search space. It is often used when the search space is discrete (e.g., all tours that visit a given set
of cities). For certain problems, simulated annealing may be more efficient than exhaustive
enumeration — provided that the goal is merely to find an acceptably good solution in a fixed
amount of time, rather than the best possible solution. Recently there have been significant
research efforts to apply evolutionary computation (EC) techniques for the purposes of evolving
one or more aspects of Simulated Annealing. Evolutionary computation methodologies have been
applied to three main attributes of Simulated Annealing.
8. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.3, June 2013
44
Figure 2. Concept Diagram of the Proposed Algorithm
Most of the work involving the evolution of SA has focused on the network weights and
topological structure. The selection of fitness function depends on the research goals. For a
classification problem, the rate of misclassified patterns can be viewed as the fitness value. The
advantage of the EC is that EC can be used in cases with non-differentiable PE transfer functions
and no gradient information available. The disadvantages are
1. The performance is not competitive in some problems.
2. Representation of the weights is difficult and the genetic operators have to be carefully selected
or developed.
It is faster and gets better results in most cases.
5. IMPLEMENTATION OF THE PROPOSED METHOD
Particle Swarm optimization (PSO) is a population based algorithm in which each particle is
considered as s solution in the multimodal optimization space. There are several types of PSO
proposed but here in this work very simplest form of PSO is taken to solve the Economic
Dispatch (ED) problem. The particles are generated keeping the constraints in mind for each
generating unit. When economic load dispatch problem considered it can be classified in two
different ways.
1. Economic load dispatch without considering the transmission line losses
2. Economic load dispatch considering the transmission line losses.
5.1 Economic Dispatch without loss
When any optimization process is applied to the ED problem some constraints are considered. In
this work two different constraints are considered. Among them the equality constraint is
summation of all the generating power must be equal to the load demand and the inequality
constraint is the powers generated must be within the limit of maximum and minimum active
power of each unit. The sequential steps of the proposed PSO method are given below.
Step 1:
The individuals of the population are randomly initialized according to the limit of each unit
including individual dimensions. The velocities of the different particles are also randomly
9. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.3, June 2013
45
generated keeping the velocity within the maximum and minimum value of the velocities. These
initial individuals must be feasible candidate solutions that satisfy the practical operation
constraints.
Step 2:
Each set of solution in the space should satisfy the equality constraints .So equality constraints are
checked. If any combination doesn’t satisfy the constraints then they are set according to the
power balance equation.
Step 3:
The evaluation function of each individual Pgi, is calculated in the population using the
evaluation function F .Here F is
F= a× (P gi) 2
+b× P gi +c (7)
Where a, b, c are constants. The present value is set as the pbest value.
Step 4:
Each pbest values are compared with the other pbest values in the population. The best evaluation
value among the p-bests is denoted as gbest.
Step 5:
The member velocity v of each individual Pg is modified according to the velocity update
equation
Vid
(u+1)
=w *Vi
(u)
+C1*rand ( )*(pbest id -Pgid
(u)
) +C2*rand ( )*(gbestid -Pgid
(u)
) (8)
Where u is the number of iteration
Step 6:
The velocity components constraint occurring in the limits from the following conditions are
checked
Vdmin
= -0.5*Pmin
Vdmax
= +0.5*Pmax
Step 7:
The position of each individual Pg is modified according to the position update equation
Pgid
(u+1)
= Pgid
(u)
+ Vid
(u+1)
(9)
Step 8:
If the evaluation value of each individual is better than previous pbest, the current value is set to
be pbest. If the best pbest is better than gbest, the value is set to be gbest.
Step 9:
If the number of iterations reaches the maximum, then go to step 10.Otherwise, go to step 2.
Step 10:
The individual that generates the latest gbest is the optimal generation power of each unit
with the minimum total generation cost.
5.2 Economic Dispatch with loss
When the losses are considered the optimization process becomes little bit complicated. Since the
losses are dependent on the power generated of the each unit, in each generation the loss changes,
The P-loss can be found out by using the equation
P L = Pm Bmn Pn (10)
10. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.3, June 2013
46
Where Bmn the loss co-efficient and the loss co-efficient can be calculated from the load flow
equations or it may be given in the problem. However in this work for simplicity the loss
coefficient are given which are the approximate one. Some parts are neglected. The sequential
steps to find the optimum solution are
Step 1:
The power of each unit, velocity of particles, is randomly generated which must be in the
maximum and minimum limit. These initial individuals must be feasible candidate solutions that
satisfy the practical operation constraints.
Step 2:
Each set of solution in the space should satisfy the following equation
gi= PD + PL (11)
PL calculated by using above equation (6.4).Then equality constraints are checked. If any
combination doesn’t satisfy the constraints then they are set according to the power balance
equation.
Pd = PD + PL – i (12)
Step 3:
The cost function of each individual Pgi, is calculated in the population using the evaluation
function F .Here F is
F = a× (Pgi) 2
+ b ×Pgi + c (13)
Where a, b, c are constants. The present value is set as the pbest value.
Step 4:
Each pbest values are compared with the other pbest values in the population. The best evaluation
value among the pbest is denoted as gbest.
Step 5:
The member velocity v of each individual Pg is updated according to the velocity update
equation.
Vid
(u+1)
=w *Vi
(u)
+C1*rand ( )*(pbest id -Pgid
(u)
) +C2*rand ( )*( gbestid -Pgid
(u)
) (14)
Where u is the number of iteration
Step 6:
The velocity components constraint occurring in the limits from the following conditions are
checked
Vd min = -0.5*Pmin
Vdmax = +0.5*Pmax
Step 7:
The position of each individual Pg is modified according to the position update equation
Pgid
(u+1)
= Pgid
(u)
+ Vid
(u+1)
(15)
Step 8:
The cost function of each new is calculated. If the evaluation value of each individual is
better than previous pbest; the current value is set to be pbest. If the best pbest is better than
gbest, the value is set to be gbest.
11. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.3, June 2013
47
Step 9:
If the number of iterations reaches the maximum, then go to step 10.Otherwise, go to step
2.
Step 10:
The individual that generates the latest gbest is the optimal generation power of each unit
with the minimum total generation cost.
6. RESULTS AND DISCUSSION
The proposed coding scheme for the solution of Economic Dispatch using Particle Swarm
Optimization algorithm and Simulated Annealing technique has been tested on the three unit
system. Here we have taken C1 = 1.99 and C2 = 1.99 and the maximum value of w is chosen 0.9
and minimum value is chosen 0.4.the velocity limits are selected as Vmax= 0.5*Pmax and the
minimum velocity is selected as Vmin= -0.5*Pmin. There are 10 number of particles selected in the
population. The cost curve (the plot between the number of iterations and the cost) for the three
unit system considered here is given below. For different value of C1 and C2 the cost curve
converges in the different region. So, the best value is taken for the minimum cost of the problem.
If the numbers of particles are increased then cost curve converges faster. It can be observed the
loss has no effect on the cost characteristic. The simulation output is shown in Fig.3.
Figure 3. Simulation Result - Cost Curve
7. CONCLUSIONS
This paper presents a new approach to address practical ED issues. Power crisis is one of the
major issues of concern all over the world today. The production is not enough to meet the
demands of consumers. Under these circumstances the power system should be efficient in
Economic Load Dispatch which minimizes the total generating cost. This project presents a new
approach to address practical ED issues. A new approach to the solution of ED using Particle
Swarm Optimization with Simulated Annealing has been proposed, and proven by a systematic
simulation processes. Through the proposed coding scheme, constraints of ED problems can be
effectively released during the search process, therefore, the solution quality, as well as the
calculation time, is greatly improved. The proposed approach has been demonstrated by three unit
system and proven to have superior features, including high quality solutions, stable convergence
characteristics, and good computational efficiency. The generation limits and the demand are
considered for practical use in the proposed method. The encouraging simulation results showed
that the proposed method is capable of obtaining more efficient, higher quality solutions for ED
problems. In future, the most problematic line flow constraints and security constraints will be
considered which leads to the Security Constrained Economic Dispatch (SCED). For handling the
12. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.3, June 2013
48
line flow constraints, in addition to the Particle Swarm Optimization (PSO) algorithm with
Simulated Annealing(SA), the Newton Raphson method (NR) will be used as well as for handling
the security constraints also. Furthermore, the generating cost will be minimized and the Security
Constrained Economic Dispatch will be targeted.
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