OPTIMAL DISPATCH OF REAL POWER GENERATION USING PARTICLE SWARM OPTIMIZATION: A CASE STUDY OF EGBIN THERMAL STATION
|
|
- Emil Peters
- 5 years ago
- Views:
Transcription
1 OPTIMAL DISPATCH OF REAL POWER GENERATION USING PARTICLE SWARM OPTIMIZATION: A CASE STUDY OF EGBIN THERMAL STATION Onah C. O. 1, Agber J. U. 2 and Ikule F. T. 3 1, 2, 3 Department of Electrical and Electronics Engineering, University of Agriculture Makurdi, Nigeria Abstract- Optimal dispatch is one main option for scheduling generation to find an effective real and reactive power scheduling to power plants to meet load demand as well as to minimize the operating cost. Therefore, this paper presents Particle Swarm Optimization (PSO), an efficiently reliable nonlinear optimization and population based stochastic technique, for solving the real power optimum dispatch problem including transmission loss, for six steam generating units in Egbin thermal plant, with constraints satisfaction and operating generation cost minimization. The loss coefficient or B-matrix, the generators operating limits, the quadratic cost function of the generating units together with other PSO parameters like the inertia weight, acceleration constants etc are used to set up the PSO program in MATLAB environment. The results obtained by the stochastic approach show high proficiency, ability for fast convergence, easy computation and implementation of the code and robustness to cope with the nonlinearity of optimal load dispatch problem, in obtaining the global optimum dispatch solution. Keywords- Optimal Load Dispatch, Thermal Power generation, PSO, Loss coefficient, MATLAB, Stochastic I. INTRODUCTION One of the most significant operational functions of modern day energy management system is Optimal Load Dispatch (OLD).The size of electric power system is increasing at a great speed to meet the energy requirements. OLD pertains to optimum generation in an interconnected power system to minimize the cost of generation subject to relevant system constraints [1]. With the development of grid system, it becomes necessary to operate the plant unit most economically. This paper presents an optimization method (PSO), which would be used to solve complex optimization problems of Egbin thermal station, that are nonlinear, non-differentiable and multimodal and also to find optimal solution to the OLD problem including losses and generating operational limits. PSO parameters are selected to significantly determine the efficacy and computational behavior in optimizing the problem. Finally, Matlab program is developed to solve the OLD problem of a six unit plant using PSO technique. II. PROBLEM FORMULATION The fundamental objective of optimal load dispatch problem is to minimize the total fuel cost while satisfying the operational constraints of the power system. In OLD problem, the allocation of optimal power generation among the different generating units at minimum possible cost is done is such a way as to meet demand constraints and generating constraints. The OLD problem is formulated as the minimization of total fuel cost of generating units for the entire scheduling period subject to variety of constraints. The formulation of OLD problem is as follows. A. Objective Function Aggregating the objective and constraints, the problem can be mathematically formulated as a nonlinear constrained single objective optimization problem as shown in equation (1). Minimize [ (P), (P)] Subject to g (P) = 0 (1) h (P) 0 DOI: /IJMTER BZE9N 1
2 where g is the equality constraint representing the power balance, h is the inequality constraint representing the unit generation capacity, is the total generation cost or fuel cost and is the total power loss or transmission loss in the system. The overall operating cost of the network is equal to the summation of all generation units fuel cost function, in a power system as given in equation (2). Minimize The cost function in equation (2) can be approximated to a quadratic function of the power generation as shown in equation (3) and (4) respectively. where is the fuel cost function of the generating units in (N/h). are the fuel cost coefficient of the i-th generator and is the generated real power output by the i-th generator (MW). is the total fuel cost and n is the number of generators including the slack bus. B. Equality Constraint Power balance constraint, otherwise known as the Equality constraint is well thought out in two ways. The first excludes transmission loss while the second includes transmission losses in the system. In the first case, balance is met when the sum of generation equals the sum of load, considering the equation as loss-less as represented in equation (5): In case two, balance is met when the sum of generation equals the sum of system load and total transmission power losses [2]: where is the system load demand and is the transmission line loss. The loss coefficient method which was developed by Kron [3] and popularized by [4], is used to include the effect of transmission losses. B-matrix, which is also known as the transmission loss coefficients matrix is a square matrix with dimension of, where is the number of generation units in the system. Applying B-matrix gives a solution of generated powers for different units as the variables. Equation (7) shows the function for calculating using B-matrix method [5]. where is the total transmission loss in the system, is the generated power by the i-th and j-th generating units respectively and is the element of the B-matrix between i-th and j-th generating units. C. Inequality Constraints Inequality constraint is also known as power generator capacity constraint. The power output of each generating unit has minimum and maximum generation capacity according to its machine ratings and unit power lies in between these capacities. If the power output of a generator for optimum operation of the system is less than a pre-specified value, the unit is not put on the bus All rights Reserved 2
3 because it is not possible to generate that low value of power from the unit. This is shown as an inequality constraint in equation (8): where is the minimum and maximum power output limit of the i-th generator. III. PARTICLE SWARM OPTIMIZATION PSO is a population-based stochastic search optimization technique with most recent developments in the category of combinatorial meta-heuristic optimization first developed by Kennedy and Eberhart in 1995 [6]. PSO is inspired by social behavior of bird flocking or fish schooling. Amongst various versions of PSO, the most familiar version was proposed by Shi and Eberhart in 1998 [7]. A PSO algorithm searches in parallel using a swarm consisting of a number of particles to search out optimal solutions. Each particle s position represents a candidate solution to the optimization problem. Each particle is initialized with a random position and random velocity, and searches for optimal solution within the feasible range by updating generations. A fitness evaluation function is used to assign the fitness value of each particle. The best position among all particles is assigned, and the best position of each particle up to the current iteration is also assigned. At every iteration, each particle update its position based on its own best position called and the swarm overall best position called assigned at the previous iteration, and its previous velocity. In a PSO system, particles fly around in a multi-dimensional search space. During flight, each particle adjusts its position according to its own experience and the experience of the neighboring particles, making use of the best position encountered by itself and its neighbors [8]. In the multi-dimensional space, where the optimal solution is sought, each particle in the swarm is moved toward the optimal point by adding a velocity with its position. The velocity of a particle is influenced by three components, namely, inertial, cognitive, and social. The inertial component simulates the inertial behavior of the bird to fly in the previous direction. The cognitive component models the memory of the bird about its previous best position, and the social components model the memory of the bird about the best position among the particles. The particle moves around the multidimensional search space until they find the optimal solution. The modified velocity of each agent can be calculated using the current velocity and the distance from and PSO has been successfully applied to global optimization problems with nonconvex or nonsmooth objective functions. In addition, PSO has demonstrated good properties and is easy in its concept and implementation and has few parameters to adjust. PSO, unlike most other stochastic optimization techniques requires relatively less computational burden or time. IV. PARAMETER SELECTION IN PARTICLE SWARM OPTIMIZATION PSO has a number of parameters that determine its behavior and efficacy in optimizing a given problem. A. Velocity Velocity of each particle can be modified by the following equation: where - Modified velocity of particle i at iteration t+1 is the weighting function, is the velocity of particle i at iteration t, - Cognitive acceleration constant, - Social acceleration constant, is the random number between 0 and 1, is the current position of particle i at iteration All rights Reserved 3
4 is the of particle i and is the of the group. The term is called the particle memory influence and is the swarm influence. where i = 1 n, n - Population size B. Position Modifications - modified position of particle i at iteration (t+1) change in time, measured in iteration step and time increment of iteration is 1. C. Acceleration Constant The learning factors and determines the impact of the, and the respectively. When the value of cognitive acceleration coefficient (C 1 ) increases, it enhances particles' attraction towards and decreases their attraction towards Also, increasing social acceleration coefficient in relation to cognitive acceleration coefficient increases attraction of particles towards Ozcan and Mohan (1999) [9] proposed setting C 1 = C 2 = 2 as a generally acceptable setting for most of the problems and is widely used in practical applications of PSO. D. Inertia weight Inertia weight in PSO plays an important role, because of its control on particle speed. The values = 0.9 and = 0.4 are widely accepted in literature. In current study, the value of inertia weight decreases linearly from 0.9 to 0.4 during a run time. The general selection of inertia weight is set according to the following equation: Where : Final inertia weight; : Initial inertia weight; The maximum number of iterations which is arbitrarily set as 500; : The iteration which is considered as the current iteration. E. Swarm Size Swarm size affects performance of PSO. Too few particles prompt the algorithm to get trapped in local optima, while too many particles slow down the algorithm. It is a problem dependent phenomenon and varies from problem to problem. F. Initialization Technique Random initialization of particles may facilitate the PSO algorithm to effectively explore the search space of various regions, detect solutions of better quality and enhance computational behavior of PSO. G. Number of Particles It is problem dependent. It is initialized with a few numbers of particles which is gradually increased. This will give the ideal number of particles. For the problem at hand, the number of particles chosen is 200. H. Dimension of Particles Dimension of particles would be specified by the problem to be optimized: D = (12) where number of particles and number of All rights Reserved 4
5 I. Stopping Criteria The maximum numbers of iterations that PSO accomplishes or the minimum error requirement are the stopping conditions. If the number of iteration reaches the maximum number of iteration set in PSO, then the latest is the optimal generation power unit, with minimum total generation cost at the maximum evaluation function iteration. Start Define Parameters: P min P max a, b, c, B, E, λ, P d, np, ng, It, ω, C 1 C 2 Initialize particle swarm with random position (P) and velocity vectors For each particle (i = 1,2 np), evaluate fitness Select the first particle as the global Set P i resulted so far as the Pbest for each I t 0 Set It = It + 1 Compute ω using equation (11) Update the velocity and position of the particles according to equations (9) and (10), ensuring all constraints are met Calculate the fitness of the new particles Check if Pnew < Pbest, if yes then Pbest = Pnew else maintain Pbest If Pbest < Gbest, then Gbest = Pbest otherwise Gbest = Gbest NO Is It = It YES Stop Figure 1. Flow Chart of Basic All rights Reserved 5
6 J. Algorithm of PSO The step-by-step algorithm for the proposed method is explained below: Step 1: Define parameters of PSO constants, C 1, C 2, n g, inertia weight and specify the maximum and minimum limits of generation power of each generating unit, maximum number of iterations to be performed, error, lambda, power demand, loss coefficient matrix and fuel cost co-efficient of each unit. Step 2: Initialize randomly the individuals of the population of all units according to the limit of each unit including individual dimensions, searching points and velocities. Step 3: Evaluate the fitness function of each particle using equation (13): (13) where F is the particle s fitness function, is lambda assumed to be 100 and E is the particle s error: Step 4: Assume minimum cost as the global best, that is, Step 5: Set P i obtained so far as the for each particle and the cost arising from them as cost. Step 6: Save the global best and its real power generation. Step 7: Set iteration count. Step 8: Compute the inertia weight according to equation (11). Step 9: Update particle s velocity using equation (9). If the velocity is out of range, then clamp the velocity of each particle: If (15) (16) Step 10: Modify the particle s new position using Step 11: Evaluate the fitness of the particle s new position. Step 12: For each individual particle, compare the particles fitness value with If the current fitness value is better than, then set the value equal to the current value and the position equal to the current particle s position. Step 13: Compare the best current fitness evaluation with the population. If the current value is better than the population, then reset the to the current best position and the fitness value to current fitness value. Step 14: Repeat steps 3-9 until a stopping criterion with maximum iteration is met. In Table 1, Oke-Aro and Ajah buses both have double circuit 330kV transmission lines, L1, L2 and L3, L4 respectively. They are both connected in parallel and hence, share the load and other parameters equally. Table 1. Bus names and their types Bus No. Bus Name Remark 1 Egbin Slack bus 2 Oke-Aro L1 and L2 PV bus 3 Ikeja West L3 PV bus 4 Benin L8 PV bus 5 Ajah L3 and L4 PV bus Table 2 presents the installed and generated capacities of the generating power units collated at Egbin Power Station on 24 th January, Unit ST1 was on outage, due to the fact that the All rights Reserved 6
7 step-up transformer was damaged, the generating capability of Unit ST2 decreased due to Vacuum problem and Unit ST6 was on 6 to 7 days maintenance. Unit Table 2 Egbin thermal statios installed and generated capacities Installed Capacity (MW) Installed Capacity (MWh) Generated Capacity (MW) Generated Capacity (MWh) ST OUT - ST ST ST ST ST Total Table 3. Cost coefficient and power limits of Egbin power plant Unit No. ai (N/hr) bi(n/mwhr) ci(n/mw 2 hr) Minimum Power (P min ) Maximum Power (P max ) ST ST ST ST ST ST The power limits in Table 3 is operated at a range of 25 to 100 percent maximum continuous rating (MCR). Table 4. Bus data Bus Name Bus No. Voltage, V Angle, P (pu) Q (pu) (pu) (degree) Egbin Oke-Aro Ikeja West Benin Ajah Table 5. Loss coefficient matrix VI. RESULTS AND DISCUSSION The simulation result for the cost of real power generation scheduled for different load demand is displayed in TABLE 6. Table 6. Best power output for six generating units at different load demands Power Demand All rights Reserved 7
8 Fitness in Naira/hr Fitness in Naira/hr Fitness in Naira/hr Fitness in Naira/hr International Journal of Modern Trends in Engineering and Research (IJMTER) (MW) P1 (MW) P2 (MW) P3 (MW) P4 (MW) P5 (MW) P6 (MW) Total Power Output (MW) Total Generation 57, , , , , Cost (N/hr) Transmission Loss (MW) Count Elapsed Time (secs) Plots for the optimized power output and the number of iteration for different load demands are depicted in Fig 3 to Fig 6. Simulated results shows optimal reduction in the fitness level of the particle or generation cost for 500 iterations. As the load demand increases, the transmission loss and generation cost also increases but at an optimal rate. The Figures thereby shows that, the proposed algorithm improves the quality of the solution as well as found a better optimal solution to the OLD problem for different load demands. 5.9 x 104 Plot of Fitness of Best Particle per 6 x 104 Plot of Fitness of Best Particle per Figure 2. Plot of fitness against number of iterations at 991MW load 6.08 x Plot of Fitness of Best Particle per 5.9 Figure 4. Plot of fitness against number of iterations at 1010MW load Figure 3. Plot of fitness against number of iterations at 1000MW load 6.09 x Plot of Fitness of Best Particle per 6 Figure 5. Plot of fitness against number of iterations at 1021MW All rights Reserved 8
9 Fitness in Naira/hr International Journal of Modern Trends in Engineering and Research (IJMTER) 6.18 x 104 Plot of Fitness of Best Particle per Figure 6. Plot of fitness against number of iterations at 1029MW load VII. CONCLUSION The developed PSO optimization technique has been successfully applied for the solution of the optimal dispatch in power system in this paper. The successful implementation of the proposed PSO algorithm on Egbin thermal station considering transmission losses proved to be the required method for solving optimal dispatch of real power generation problem. It has been observed that the PSO technique is capable of optimizing any given OLD problem irrespective of load demand. From the analysis of the proposed PSO technique which was implemented in MATLAB environment using Egbin six generator systems as case study considering transmission losses, proves that PSO is highly efficient, accurate and has capacity to minimize the fuel cost of generators and satisfies each and every constraint. Thus, PSO technique can be successfully applied to solve OLD problems in the real world power systems. REFERENCES [1] S. Prabakaran and S. V. Kumar, Security Constrained Optimal Load Dispatch using HPSO Technique for Thermal Scheduling Problems, International Journal of Research in Engineering and Technology, vol. 02 (05): , 2013 [2] K. Balamurugan, R. Muralisachithnndam and S. R. Krishnan, Differential Evolution Based Solution for Combined Economic and Emission Power Dispatch with Valve Loading Effect, International Journal on Electrical Engineering and Informatics, vol. 6 (1): 74-92, 2014 [3] G. Kron, A Set of Principles to Interconnect the Solution of Physical Systems, Journal of Applied Physics, vol. 24 (8): , 1953 [4] L. K. Kirchmayer, H. H. Happ, G. W. Stagg and J. F. Hohenstein, Direct Calculation of Transmission Loss Formula, AIEE Transaction vol. 79 (3): , 1960 [5] R. Rasoul, F. O. Moh d, Y. Rubiyah and K. Marzuki, Solving Economic Dispatch Problem using Particles Swarm Optimization by an Evolutionary Technique for Initializing Particles, Journal of Theoretical and Applied Information Technology, vol. 46 (2): , 2012 [6] J. Kennedy and R. Eberhart, Particle Swarm Optimization, Proceedings of IEEE International Conference on Neural Networks IV, , 1995 [7] Y. Shi and R. C. Eberhart, A Modified Particle Swarm Optimizer, Proceedings of IEEE International Conference on Computational Intelligence, 69-73, 1998 [8] M. J. Khan and H. Mahala, Particle Swarm Optimization by Natural Exponent Inertia Weight for Economic Load Dispatch, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 3 (12): , 2014 [9] E. Ozcan and C. Mohan, Particle Swarm Optimization: Surfing the Waves, Proceedings of IEEE International Congress on Evolutionary Computation, , All rights Reserved 9
CAPACITOR PLACEMENT USING FUZZY AND PARTICLE SWARM OPTIMIZATION METHOD FOR MAXIMUM ANNUAL SAVINGS
CAPACITOR PLACEMENT USING FUZZY AND PARTICLE SWARM OPTIMIZATION METHOD FOR MAXIMUM ANNUAL SAVINGS M. Damodar Reddy and V. C. Veera Reddy Department of Electrical and Electronics Engineering, S.V. University,
More informationOPTIMAL POWER FLOW BASED ON PARTICLE SWARM OPTIMIZATION
U.P.B. Sci. Bull., Series C, Vol. 78, Iss. 3, 2016 ISSN 2286-3540 OPTIMAL POWER FLOW BASED ON PARTICLE SWARM OPTIMIZATION Layth AL-BAHRANI 1, Virgil DUMBRAVA 2 Optimal Power Flow (OPF) is one of the most
More informationOn Optimal Power Flow
On Optimal Power Flow K. C. Sravanthi #1, Dr. M. S. Krishnarayalu #2 # Department of Electrical and Electronics Engineering V R Siddhartha Engineering College, Vijayawada, AP, India Abstract-Optimal Power
More informationCHAPTER 3 FUZZIFIED PARTICLE SWARM OPTIMIZATION BASED DC- OPF OF INTERCONNECTED POWER SYSTEMS
51 CHAPTER 3 FUZZIFIED PARTICLE SWARM OPTIMIZATION BASED DC- OPF OF INTERCONNECTED POWER SYSTEMS 3.1 INTRODUCTION Optimal Power Flow (OPF) is one of the most important operational functions of the modern
More informationApplying Particle Swarm Optimization to Adaptive Controller Leandro dos Santos Coelho 1 and Fabio A. Guerra 2
Applying Particle Swarm Optimization to Adaptive Controller Leandro dos Santos Coelho 1 and Fabio A. Guerra 2 1 Production and Systems Engineering Graduate Program, PPGEPS Pontifical Catholic University
More informationOPTIMAL LOCATION AND SIZING OF DISTRIBUTED GENERATOR IN RADIAL DISTRIBUTION SYSTEM USING OPTIMIZATION TECHNIQUE FOR MINIMIZATION OF LOSSES
780 OPTIMAL LOCATIO AD SIZIG OF DISTRIBUTED GEERATOR I RADIAL DISTRIBUTIO SYSTEM USIG OPTIMIZATIO TECHIQUE FOR MIIMIZATIO OF LOSSES A. Vishwanadh 1, G. Sasi Kumar 2, Dr. D. Ravi Kumar 3 1 (Department of
More informationRegular paper. Particle Swarm Optimization Applied to the Economic Dispatch Problem
Rafik Labdani Linda Slimani Tarek Bouktir Electrical Engineering Department, Oum El Bouaghi University, 04000 Algeria. rlabdani@yahoo.fr J. Electrical Systems 2-2 (2006): 95-102 Regular paper Particle
More informationApplication of Teaching Learning Based Optimization for Size and Location Determination of Distributed Generation in Radial Distribution System.
Application of Teaching Learning Based Optimization for Size and Location Determination of Distributed Generation in Radial Distribution System. Khyati Mistry Electrical Engineering Department. Sardar
More informationOptimal Placement and Sizing of Distributed Generation for Power Loss Reduction using Particle Swarm Optimization
Available online at www.sciencedirect.com Energy Procedia 34 (2013 ) 307 317 10th Eco-Energy and Materials Science and Engineering (EMSES2012) Optimal Placement and Sizing of Distributed Generation for
More informationParticle Swarm Optimization. Abhishek Roy Friday Group Meeting Date:
Particle Swarm Optimization Abhishek Roy Friday Group Meeting Date: 05.25.2016 Cooperation example Basic Idea PSO is a robust stochastic optimization technique based on the movement and intelligence of
More informationPARTICLE SWARM OPTIMISATION (PSO)
PARTICLE SWARM OPTIMISATION (PSO) Perry Brown Alexander Mathews Image: http://www.cs264.org/2009/projects/web/ding_yiyang/ding-robb/pso.jpg Introduction Concept first introduced by Kennedy and Eberhart
More informationContents Economic dispatch of thermal units
Contents 2 Economic dispatch of thermal units 2 2.1 Introduction................................... 2 2.2 Economic dispatch problem (neglecting transmission losses)......... 3 2.2.1 Fuel cost characteristics........................
More informationACTA UNIVERSITATIS APULENSIS No 11/2006
ACTA UNIVERSITATIS APULENSIS No /26 Proceedings of the International Conference on Theory and Application of Mathematics and Informatics ICTAMI 25 - Alba Iulia, Romania FAR FROM EQUILIBRIUM COMPUTATION
More informationB-Positive Particle Swarm Optimization (B.P.S.O)
Int. J. Com. Net. Tech. 1, No. 2, 95-102 (2013) 95 International Journal of Computing and Network Technology http://dx.doi.org/10.12785/ijcnt/010201 B-Positive Particle Swarm Optimization (B.P.S.O) Muhammad
More informationA Particle Swarm Optimization for Reactive Power Optimization
ISSN (e): 2250 3005 Vol, 04 Issue, 11 November 2014 International Journal of Computational Engineering Research (IJCER) A Particle Swarm Optimization for Reactive Power Optimization Suresh Kumar 1, Sunil
More informationReactive Power Contribution of Multiple STATCOM using Particle Swarm Optimization
Reactive Power Contribution of Multiple STATCOM using Particle Swarm Optimization S. Uma Mageswaran 1, Dr.N.O.Guna Sehar 2 1 Assistant Professor, Velammal Institute of Technology, Anna University, Chennai,
More informationMulti-objective Emission constrained Economic Power Dispatch Using Differential Evolution Algorithm
Multi-objective Emission constrained Economic Power Dispatch Using Differential Evolution Algorithm Sunil Kumar Soni, Vijay Bhuria Abstract The main aim of power utilities is to provide high quality power
More informationSOULTION TO CONSTRAINED ECONOMIC LOAD DISPATCH
SOULTION TO CONSTRAINED ECONOMIC LOAD DISPATCH SANDEEP BEHERA (109EE0257) Department of Electrical Engineering National Institute of Technology, Rourkela SOLUTION TO CONSTRAINED ECONOMIC LOAD DISPATCH
More informationSOLUTION TO ECONOMIC LOAD DISPATCH USING PSO
SOLUTION TO ECONOMIC LOAD DISPATCH USING PSO A thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Technology in Electrical Engineering By MAHESH PRASAD MISHRA,108ee007
More informationVedant V. Sonar 1, H. D. Mehta 2. Abstract
Load Shedding Optimization in Power System Using Swarm Intelligence-Based Optimization Techniques Vedant V. Sonar 1, H. D. Mehta 2 1 Electrical Engineering Department, L.D. College of Engineering Ahmedabad,
More informationSolving Numerical Optimization Problems by Simulating Particle-Wave Duality and Social Information Sharing
International Conference on Artificial Intelligence (IC-AI), Las Vegas, USA, 2002: 1163-1169 Solving Numerical Optimization Problems by Simulating Particle-Wave Duality and Social Information Sharing Xiao-Feng
More informationELECTRICITY GENERATION SCHEDULING AN IMPROVED FOR FIREFLY OPTIMIZATION ALGORITHM
International Research Journal of Engineering and Technology (IRJET) e-issn: -00 Volume: 0 Issue: 0 June -01 www.irjet.net p-issn: -00 ELECTRICITY GENERATION SCHEDULING AN IMPROVED FOR FIREFLY OPTIMIZATION
More informationEconomic Operation of Power Systems
Economic Operation of Power Systems Section I: Economic Operation Of Power System Economic Distribution of Loads between the Units of a Plant Generating Limits Economic Sharing of Loads between Different
More informationOptimal Placement of Multi DG Unit in Distribution Systems Using Evolutionary Algorithms
IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) e-issn: 2278-1676,p-ISSN: 2320-3331, Volume, Issue 6 Ver. IV (Nov Dec. 2014), PP 47-52 www.iosrjournals.org Optimal Placement of Multi
More informationA Novel Approach for Complete Identification of Dynamic Fractional Order Systems Using Stochastic Optimization Algorithms and Fractional Calculus
5th International Conference on Electrical and Computer Engineering ICECE 2008, 20-22 December 2008, Dhaka, Bangladesh A Novel Approach for Complete Identification of Dynamic Fractional Order Systems Using
More informationFuzzy adaptive catfish particle swarm optimization
ORIGINAL RESEARCH Fuzzy adaptive catfish particle swarm optimization Li-Yeh Chuang, Sheng-Wei Tsai, Cheng-Hong Yang. Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan
More informationA PSO APPROACH FOR PREVENTIVE MAINTENANCE SCHEDULING OPTIMIZATION
2009 International Nuclear Atlantic Conference - INAC 2009 Rio de Janeiro,RJ, Brazil, September27 to October 2, 2009 ASSOCIAÇÃO BRASILEIRA DE ENERGIA NUCLEAR - ABEN ISBN: 978-85-99141-03-8 A PSO APPROACH
More informationBeta Damping Quantum Behaved Particle Swarm Optimization
Beta Damping Quantum Behaved Particle Swarm Optimization Tarek M. Elbarbary, Hesham A. Hefny, Atef abel Moneim Institute of Statistical Studies and Research, Cairo University, Giza, Egypt tareqbarbary@yahoo.com,
More informationSingle objective optimization using PSO with Interline Power Flow Controller
Single objective optimization using PSO with Interline Power Flow Controller Praveen.J, B.Srinivasa Rao jpraveen.90@gmail.com, balususrinu@vrsiddhartha.ac.in Abstract Optimal Power Flow (OPF) problem was
More informationUNIT-I ECONOMIC OPERATION OF POWER SYSTEM-1
UNIT-I ECONOMIC OPERATION OF POWER SYSTEM-1 1.1 HEAT RATE CURVE: The heat rate characteristics obtained from the plot of the net heat rate in Btu/Wh or cal/wh versus power output in W is shown in fig.1
More informationOptimal capacitor placement and sizing using combined fuzzy-hpso method
MultiCraft International Journal of Engineering, Science and Technology Vol. 2, No. 6, 2010, pp. 75-84 INTERNATIONAL JOURNAL OF ENGINEERING, SCIENCE AND TECHNOLOGY www.ijest-ng.com 2010 MultiCraft Limited.
More informationInternational Research Journal of Engineering and Technology (IRJET) e-issn: Volume: 03 Issue: 03 Mar p-issn:
Optimum Size and Location of Distributed Generation and for Loss Reduction using different optimization technique in Power Distribution Network Renu Choudhary 1, Pushpendra Singh 2 1Student, Dept of electrical
More informationAn Adaptive Approach to Posistioning And Optimize Size of DG Source to Minimise Power Loss in Distribution Network
International Journal of Engineering Research and Development e-issn: 2278-067X, p-issn: 2278-800X, www.ijerd.com Volume 12, Issue 10 (October 2016), PP.52-57 An Adaptive Approach to Posistioning And Optimize
More informationSelected paper. Particle Swarm Optimization Based Technique for Optimal Placement of Overcurrent Relay in a Power System
Amir Syazani Saidan 1,*, Nur Ashida Salim 2, Muhd Azri Abdul Razak 2 J. Electrical Systems Special issue AMPE2015 Selected paper Particle Swarm Optimization Based Technique for Optimal Placement of Overcurrent
More informationParticle swarm optimization (PSO): a potentially useful tool for chemometrics?
Particle swarm optimization (PSO): a potentially useful tool for chemometrics? Federico Marini 1, Beata Walczak 2 1 Sapienza University of Rome, Rome, Italy 2 Silesian University, Katowice, Poland Rome,
More informationUnit Commitment Using Soft Computing Techniques
International Journal of Electrical Engineering. ISSN 0974-2158 Volume 8, Number 3 (2015), pp. 289-299 International Research Publication House http://www.irphouse.com Unit Commitment Using Soft Computing
More informationSTUDY OF PARTICLE SWARM FOR OPTIMAL POWER FLOW IN IEEE BENCHMARK SYSTEMS INCLUDING WIND POWER GENERATORS
Southern Illinois University Carbondale OpenSIUC Theses Theses and Dissertations 12-1-2012 STUDY OF PARTICLE SWARM FOR OPTIMAL POWER FLOW IN IEEE BENCHMARK SYSTEMS INCLUDING WIND POWER GENERATORS Mohamed
More informationReactive Power and Voltage Control of Power Systems Using Modified PSO
J. Energy Power Sources Vol. 2, No. 5, 2015, pp. 182-188 Received: March 29, 2015, Published: May 30, 2015 Journal of Energy and Power Sources www.ethanpublishing.com Reactive Power and Voltage Control
More informationReactive Power Management using Firefly and Spiral Optimization under Static and Dynamic Loading Conditions
1 Reactive Power Management using Firefly and Spiral Optimization under Static and Dynamic Loading Conditions Ripunjoy Phukan, ripun000@yahoo.co.in Abstract Power System planning encompasses the concept
More informationThe Parameters Selection of PSO Algorithm influencing On performance of Fault Diagnosis
The Parameters Selection of Algorithm influencing On performance of Fault Diagnosis Yan HE,a, Wei Jin MA and Ji Ping ZHANG School of Mechanical Engineering and Power Engineer North University of China,
More informationOptimal capacitor placement and sizing via artificial bee colony
International Journal of Smart Grid and Clean Energy Optimal capacitor placement and sizing via artificial bee colony Mohd Nabil Muhtazaruddin a*, Jasrul Jamani Jamian b, Danvu Nguyen a Nur Aisyah Jalalludin
More informationOptimal Placement & sizing of Distributed Generator (DG)
Chapter - 5 Optimal Placement & sizing of Distributed Generator (DG) - A Single Objective Approach CHAPTER - 5 Distributed Generation (DG) for Power Loss Minimization 5. Introduction Distributed generators
More informationBinary Particle Swarm Optimization with Crossover Operation for Discrete Optimization
Binary Particle Swarm Optimization with Crossover Operation for Discrete Optimization Deepak Singh Raipur Institute of Technology Raipur, India Vikas Singh ABV- Indian Institute of Information Technology
More informationComparison of Loss Sensitivity Factor & Index Vector methods in Determining Optimal Capacitor Locations in Agricultural Distribution
6th NATIONAL POWER SYSTEMS CONFERENCE, 5th-7th DECEMBER, 200 26 Comparison of Loss Sensitivity Factor & Index Vector s in Determining Optimal Capacitor Locations in Agricultural Distribution K.V.S. Ramachandra
More informationSwarm intelligence approach to the solution of optimal power flow
J. Indian Inst. Sci., Sept. Oct. 2006, 86, 439 455 Indian Institute of Science. Swarm intelligence approach to the solution of optimal power flow Department of Electrical Engineering, Indian Institute
More informationOptimal Compensation of Reactive Power in Transmission Networks using PSO, Cultural and Firefly Algorithms
Volume 114 No. 9 2017, 367-388 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu ijpam.eu Optimal Compensation of Reactive Power in Transmission Networks using
More informationParticle swarm optimization approach to portfolio optimization
Nonlinear Analysis: Real World Applications 10 (2009) 2396 2406 Contents lists available at ScienceDirect Nonlinear Analysis: Real World Applications journal homepage: www.elsevier.com/locate/nonrwa Particle
More informationDistributed vs Bulk Power in Distribution Systems Considering Distributed Generation
Distributed vs Bulk Power in Distribution Systems Considering Distributed Generation Abdullah A. Alghamdi 1 and Prof. Yusuf A. Al-Turki 2 1 Ministry Of Education, Jeddah, Saudi Arabia. 2 King Abdulaziz
More informationMinimization of Energy Loss using Integrated Evolutionary Approaches
Minimization of Energy Loss using Integrated Evolutionary Approaches Attia A. El-Fergany, Member, IEEE, Mahdi El-Arini, Senior Member, IEEE Paper Number: 1569614661 Presentation's Outline Aim of this work,
More informationApplication of Artificial Neural Network in Economic Generation Scheduling of Thermal Power Plants
Application of Artificial Neural Networ in Economic Generation Scheduling of Thermal ower lants Mohammad Mohatram Department of Electrical & Electronics Engineering Sanjay Kumar Department of Computer
More informationA Particle Swarm Optimization (PSO) Primer
A Particle Swarm Optimization (PSO) Primer With Applications Brian Birge Overview Introduction Theory Applications Computational Intelligence Summary Introduction Subset of Evolutionary Computation Genetic
More informationJ. Electrical Systems 10-1 (2014): Regular paper. Optimal Power Flow and Reactive Compensation Using a Particle Swarm Optimization Algorithm
Ahmed Elsheikh 1, Yahya Helmy 1, Yasmine Abouelseoud 1,*, Ahmed Elsherif 1 J. Electrical Systems 10-1 (2014): 63-77 Regular paper Optimal Power Flow and Reactive Compensation Using a Particle Swarm Optimization
More informationApplication of GA and PSO Tuned Fuzzy Controller for AGC of Three Area Thermal- Thermal-Hydro Power System
International Journal of Computer Theory and Engineering, Vol. 2, No. 2 April, 2 793-82 Application of GA and PSO Tuned Fuzzy Controller for AGC of Three Area Thermal- Thermal-Hydro Power System S. K.
More informationA Particle Swarm Based Method for Composite System Reliability Analysis
A Particle Swarm Based Method for Composite System Reliability Analysis Ramesh Earla, Shashi B. Patra, Student Member, IEEE and Joydeep Mitra, Senior Member, IEEE Abstract This paper presents a new method
More informationOptimal tunning of lead-lag and fuzzy logic power system stabilizers using particle swarm optimization
Available online at www.sciencedirect.com Expert Systems with Applications Expert Systems with Applications xxx (2008) xxx xxx www.elsevier.com/locate/eswa Optimal tunning of lead-lag and fuzzy logic power
More informationCapacitor Placement for Economical Electrical Systems using Ant Colony Search Algorithm
Capacitor Placement for Economical Electrical Systems using Ant Colony Search Algorithm Bharat Solanki Abstract The optimal capacitor placement problem involves determination of the location, number, type
More informationWIND SPEED ESTIMATION IN SAUDI ARABIA USING THE PARTICLE SWARM OPTIMIZATION (PSO)
WIND SPEED ESTIMATION IN SAUDI ARABIA USING THE PARTICLE SWARM OPTIMIZATION (PSO) Mohamed Ahmed Mohandes Shafique Rehman King Fahd University of Petroleum & Minerals Saeed Badran Electrical Engineering
More information04-Economic Dispatch 2. EE570 Energy Utilization & Conservation Professor Henry Louie
04-Economic Dispatch EE570 Energy Utilization & Conservation Professor Henry Louie 1 Topics Example 1 Example Dr. Henry Louie Consider two generators with the following cost curves and constraints: C 1
More information03-Economic Dispatch 1. EE570 Energy Utilization & Conservation Professor Henry Louie
03-Economic Dispatch 1 EE570 Energy Utilization & Conservation Professor Henry Louie 1 Topics Generator Curves Economic Dispatch (ED) Formulation ED (No Generator Limits, No Losses) ED (No Losses) ED Example
More informationAutomatic Generation Control of interconnected Hydro Thermal system by using APSO scheme
IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) e-issn: 2278-1676,p-ISSN: 2320-3331 PP 50-57 www.iosrjournals.org Automatic Generation Control of interconnected Hydro Thermal system
More informationA Fast Method for Embattling Optimization of Ground-Based Radar Surveillance Network
A Fast Method for Embattling Optimization of Ground-Based Radar Surveillance Network JIANG Hai University of Chinese Academy of Sciences National Astronomical Observatories, Chinese Academy of Sciences
More informationDistributed Particle Swarm Optimization
Distributed Particle Swarm Optimization Salman Kahrobaee CSCE 990 Seminar Main Reference: A Comparative Study of Four Parallel and Distributed PSO Methods Leonardo VANNESCHI, Daniele CODECASA and Giancarlo
More informationAbstract. 2. Dynamical model of power system
Optimization Of Controller Parametersfornon-Linear Power Systems Using Different Optimization Techniques Rekha 1,Amit Kumar 2, A. K. Singh 3 1, 2 Assistant Professor, Electrical Engg. Dept. NIT Jamshedpur
More informationEconomic planning and operation in electric power system using meta-heuristics based on Cuckoo Search Algorithm
SHIBAURA INSTITUTE OF TECHNOLOGY Economic planning and operation in electric power system using meta-heuristics based on Cuckoo Search Algorithm by Nguyen Phuc Khai A thesis submitted in partial fulfillment
More informationUNIVERSITY OF NAIROBI
UNIVERSITY OF NAIROBI FACULTY OF ENGINEERING DEPARTMENT OF ELECTRICAL AND INFORMATION ENGINEEERING HYDROTHERMAL ECONOMIC DISPATCH USING PARTICLE SWARM OPTIMIZATION (P.S.O) PROJECT INDEX: 055 SUBMITTED
More informationMinimization of Reactive Power Using Particle Swarm Optimization
Minimization of Reactive Power Using Particle Swarm Optimization 1 Vivek Kumar Jain, 2 Himmat Singh, 3 Laxmi Srivastava 1, 2, 3 Department of Electrical Engineering, Madhav Institute of Technology and
More informationA PARTICLE SWARM OPTIMIZATION TO OPTIMAL SHUNT-CAPACITOR PLACEMENT IN RADIAL DISTRIBUTION SYSTEMS
ISSN (Print) : 30 3765 ISSN (Online): 78 8875 (An ISO 397: 007 Certified Organization) ol., Issue 0, October 03 A PARTICLE SWARM OPTIMIZATION TO OPTIMAL SHUNT-CAPACITOR PLACEMENT IN RADIAL DISTRIBUTION
More informationOptimal Capacitor placement in Distribution Systems with Distributed Generators for Voltage Profile improvement by Particle Swarm Optimization
Optimal Capacitor placement in Distribution Systems with Distributed Generators for Voltage Profile improvement by Particle Swarm Optimization G. Balakrishna 1, Dr. Ch. Sai Babu 2 1 Associate Professor,
More informationLevy Differential Evolutionary Particle Swarm Optimization (LEVY DEEPSO)
1 Levy Differential Evolutionary Particle Swarm Optimization (LEVY DEEPSO) Developers: Kartik S. Pandya, CHARUSAT-INDIA S.K. Joshi, MSU-INDIA S.N. Singh, IIT-K-INDIA 2 Particle Swarm Optimization[1] Particles:
More information, and ignoring all load currents, determine
ECE43 Test 3 Dec 8, 5 Q. (33 pts.) The Zbus for the above 3-bus network with bus as reference, in per unit, is given to be 3.87 j.798 j.8 j Z.798 j.87 j.8 j bus.8 j.8 j j Assuming that the prefault values
More informationECONOMIC OPERATION OF POWER SYSTEMS USING HYBRID OPTIMIZATION TECHNIQUES
SYNOPSIS OF ECONOMIC OPERATION OF POWER SYSTEMS USING HYBRID OPTIMIZATION TECHNIQUES A THESIS to be submitted by S. SIVASUBRAMANI for the award of the degree of DOCTOR OF PHILOSOPHY DEPARTMENT OF ELECTRICAL
More informationOptimal Capacitor Placement in Radial Distribution System to minimize the loss using Fuzzy Logic Control and Hybrid Particle Swarm Optimization
Optimal Capacitor Placement in Radial Distribution System to minimize the loss using Fuzzy Logic Control and Hybrid Particle Swarm Optimization 1 S.Joyal Isac, 2 K.Suresh Kumar Department of EEE, Saveetha
More informationHybrid particle swarm algorithm for solving nonlinear constraint. optimization problem [5].
Hybrid particle swarm algorithm for solving nonlinear constraint optimization problems BINGQIN QIAO, XIAOMING CHANG Computers and Software College Taiyuan University of Technology Department of Economic
More informationPerformance Evaluation of IIR Filter Design Using Multi-Swarm PSO
Proceedings of APSIPA Annual Summit and Conference 2 6-9 December 2 Performance Evaluation of IIR Filter Design Using Multi-Swarm PSO Haruna Aimi and Kenji Suyama Tokyo Denki University, Tokyo, Japan Abstract
More informationArtificial Intelligence Based Approach for Identification of Current Transformer Saturation from Faults in Power Transformers
37 pp.37:46 Artificial Intelligence Based Approach for Identification of Current Transformer Saturation from Faults in Power Transformers A. R. Moradi 1, Y. Alinejad Beromi 2, K. Kiani 3, Z. Moravej 4
More informationHybrid Big Bang - Big Crunch Algorithm for Optimal Reactive Power Dispatch by Loss and Voltage Deviation Minimization
Hybrid Big Bang - Big Crunch Algorithm for Reactive Power Dispatch by Loss and Voltage Deviation Minimization S.Sakthivel, Professor, V.R.S. College of Engg. and Tech., Arasur-607 107, Villupuram Dt, Tamil
More informationIntegrated PSO-SQP technique for Short-term Hydrothermal Scheduling
Integrated PSO-SQP technique for Short-term Hydrothermal Scheduling Shashank Gupta 1, Nitin Narang 2 Abstract his paper presents short-term fixed and variable head hydrotherml scheduling. An integrated
More informationAvailable online at ScienceDirect. Procedia Computer Science 20 (2013 ) 90 95
Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 20 (2013 ) 90 95 Complex Adaptive Systems, Publication 3 Cihan H. Dagli, Editor in Chief Conference Organized by Missouri
More informationOptimal Performance Enhancement of Capacitor in Radial Distribution System Using Fuzzy and HSA
IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) e-issn: 2278-1676,p-ISSN: 2320-3331, Volume 9, Issue 2 Ver. I (Mar Apr. 2014), PP 26-32 Optimal Performance Enhancement of Capacitor in
More informationHybrid PSO-ANN Application for Improved Accuracy of Short Term Load Forecasting
Hybrid PSO-ANN Application for Improved Accuracy of Short Term Load Forecasting A. G. ABDULLAH, G. M. SURANEGARA, D.L. HAKIM Electrical Engineering Education Department Indonesia University of Education
More informationMODIFIED DIRECT-ZBR METHOD PSO POWER FLOW DEVELOPMENT FOR WEAKLY MESHED ACTIVE UNBALANCED DISTRIBUTION SYSTEMS
MODIFIED DIRECT-ZBR METHOD PSO POWER FLOW DEVELOPMENT FOR WEAKLY MESHED ACTIVE UNBALANCED DISTRIBUTION SYSTEMS Suyanto, Indri Suryawati, Ontoseno Penangsang, Adi Soeprijanto, Rony Seto Wibowo and DF Uman
More informationPower Quality improvement of Distribution System by Optimal Location and Size of DGs Using Particle Swarm Optimization
72 Power Quality improvement of Distribution System by Optimal Location and Size of DGs Using Particle Swarm Optimization Ankita Mishra 1, Arti Bhandakkar 2 1(PG Scholar, Department of Electrical & Electronics
More informationComparative Analysis of Jaya Optimization Algorithm for Economic Dispatch Solution
Comparative Analysis of Jaya Optimization Algorithm for Economic Dispatch Solution Swaraj Banerjee 1, Dipu Sarkar 2 1,2 Departement of Electrical and Electronics Engineering, National Institute of Technology
More informationMeta Heuristic Harmony Search Algorithm for Network Reconfiguration and Distributed Generation Allocation
Department of CSE, JayShriram Group of Institutions, Tirupur, Tamilnadu, India on 6 th & 7 th March 2014 Meta Heuristic Harmony Search Algorithm for Network Reconfiguration and Distributed Generation Allocation
More informationArtificial immune system based algorithms for optimization and self-tuning control in power systems
Scholars' Mine Masters Theses Student Research & Creative Works 007 Artificial immune system based algorithms for optimization and self-tuning control in power systems Mani Hunjan Follow this and additional
More informationPROMPT PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER DISPATCH PROBLEM
PROMPT PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER DISPATCH PROBLEM K. Lenin 1 Research Scholar Jawaharlal Nehru Technological University Kukatpally,Hyderabad 500 085, India
More informationV-Formation as Optimal Control
V-Formation as Optimal Control Ashish Tiwari SRI International, Menlo Park, CA, USA BDA, July 25 th, 2016 Joint work with Junxing Yang, Radu Grosu, and Scott A. Smolka Outline Introduction The V-Formation
More informationON THE USE OF RANDOM VARIABLES IN PARTICLE SWARM OPTIMIZATIONS: A COMPARATIVE STUDY OF GAUSSIAN AND UNIFORM DISTRIBUTIONS
J. of Electromagn. Waves and Appl., Vol. 23, 711 721, 2009 ON THE USE OF RANDOM VARIABLES IN PARTICLE SWARM OPTIMIZATIONS: A COMPARATIVE STUDY OF GAUSSIAN AND UNIFORM DISTRIBUTIONS L. Zhang, F. Yang, and
More informationMATPOWER as Educational Tool for Solving Optimal Power Flow Problems on a Simulated Nigerian Power Grid
International Journal of Engineering Science Invention ISSN (Online): 2319 6734, ISSN (Print): 2319 6726 Volume 2 Issue7 ǁ July. 2013 ǁ PP.73-78 MATPOWER as Educational Tool for Solving Optimal Power Flow
More informationB.E. / B.Tech. Degree Examination, April / May 2010 Sixth Semester. Electrical and Electronics Engineering. EE 1352 Power System Analysis
B.E. / B.Tech. Degree Examination, April / May 2010 Sixth Semester Electrical and Electronics Engineering EE 1352 Power System Analysis (Regulation 2008) Time: Three hours Answer all questions Part A (10
More informationAutomatic Generation Control. Meth Bandara and Hassan Oukacha
Automatic Generation Control Meth Bandara and Hassan Oukacha EE194 Advanced Controls Theory February 25, 2013 Outline Introduction System Modeling Single Generator AGC Going Forward Conclusion Introduction
More informationEVALUATION OF THE IMPACT OF POWER SECTOR REFORM ON THE NIGERIA POWER SYSTEM TRANSIENT STABILITY
EVALUATION OF THE IMPACT OF POWER SECTOR REFORM ON THE NIGERIA POWER SYSTEM TRANSIENT STABILITY F. I. Izuegbunam * Department of Electrical & Electronic Engineering, Federal University of Technology, Imo
More informationA Study of the Factors Influencing the Optimal Size and Site of Distributed Generations
Journal of Clean Energy Technologies, Vol. 2, No. 1, January 2014 A Study of the Factors Influencing the Optimal Size and Site of Distributed Generations Soma Biswas, S. K. Goswami, and A. Chatterjee system
More informationPower system reconfiguration and loss minimization for a distribution systems using Catfish PSO algorithm
Front. Energy 2014, 8(4): 434 442 DOI 10.1007/s11708-014-0313-y RESEARCH ARTICLE K Sathish KUMAR, S NAVEEN Power system reconfiguration and loss minimization for a distribution systems using Catfish PSO
More informationMulti Objective Economic Load Dispatch problem using A-Loss Coefficients
Volume 114 No. 8 2017, 143-153 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu ijpam.eu Multi Objective Economic Load Dispatch problem using A-Loss Coefficients
More informationInternational Journal of Mechatronics, Electrical and Computer Technology
A Hybrid Algorithm for Optimal Location and Sizing of Capacitors in the presence of Different Load Models in Distribution Network Reza Baghipour* and Seyyed Mehdi Hosseini Department of Electrical Engineering,
More informationSecondary Frequency Control of Microgrids In Islanded Operation Mode and Its Optimum Regulation Based on the Particle Swarm Optimization Algorithm
International Academic Institute for Science and Technology International Academic Journal of Science and Engineering Vol. 3, No. 1, 2016, pp. 159-169. ISSN 2454-3896 International Academic Journal of
More informationARTIFICIAL INTELLIGENCE
BABEŞ-BOLYAI UNIVERSITY Faculty of Computer Science and Mathematics ARTIFICIAL INTELLIGENCE Solving search problems Informed local search strategies Nature-inspired algorithms March, 2017 2 Topics A. Short
More informationLimiting the Velocity in the Particle Swarm Optimization Algorithm
Limiting the Velocity in the Particle Swarm Optimization Algorithm Julio Barrera 1, Osiris Álvarez-Bajo 2, Juan J. Flores 3, Carlos A. Coello Coello 4 1 Universidad Michoacana de San Nicolás de Hidalgo,
More informationGenetic Algorithm for Solving the Economic Load Dispatch
International Journal of Electronic and Electrical Engineering. ISSN 0974-2174, Volume 7, Number 5 (2014), pp. 523-528 International Research Publication House http://www.irphouse.com Genetic Algorithm
More information