In this paper, a novel approach Modified Monkey optimization (MMO) algorithm for solving optimal reactive power dispatch problem has been presented. MMO is a population based stochastic meta-heuristic algorithm and it is inspired by intelligent foraging behaviour of monkeys. This paper improves both local leader and global leader phases. The proposed (MMO) algorithm has been tested in standard IEEE 30 bus test system and simulation results show the worthy performance of the proposed algorithm in reducing the real power loss.
Enriched Firefly Algorithm for Solving Reactive Power Problemijeei-iaes
In this paper, Enriched Firefly Algorithm (EFA) is planned to solve optimal reactive power dispatch problem. This algorithm is a kind of swarm intelligence algorithm based on the response of a firefly to the light of other fireflies. In this paper, we plan an augmentation on the original firefly algorithm. The proposed algorithm extends the single population FA to the interacting multi-swarms by cooperative Models. The proposed EFA has been tested on standard IEEE 30 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.
NOVEL PSO STRATEGY FOR TRANSMISSION CONGESTION MANAGEMENTelelijjournal
In post deregulated era of power system load characteristics become more erratic. Unplanned transactions
of electrical power through transmission lines of particular path may occur due to low cost offered by
generating companies. As a consequence those lines driven close to their operating limits and becomes
congested as the lines are originally designed for traditional vertically integrated structure of power
system. This congestion in transmission lines is unpredictable with deterministic load flow strategy.
Rescheduling active and reactive power output of generators is the promising way to manage congestion.
In this paper Particle Swarm Optimization (PSO) with varying inertia weight strategy, with two variants
e1-PSO and e-2 PSO is applied for optimal solution of active and reactive power rescheduling for
managing congestion. The generators sensitivity technique is opted for identifying participating generators
for managing congestion. Proposed algorithm is tested on IEEE 30 bus system. Comparison is made
between results obtained from proposed techniques to that of results reported in previous literature.
Reduction of Active Power Loss byUsing Adaptive Cat Swarm Optimizationijeei-iaes
This paper presents, an Adaptive Cat Swarm Optimization (ACSO) for solving reactive power dispatch problem. Cat Swarm Optimization (CSO) is one of the new-fangled swarm intelligence algorithms for finding the most excellent global solution. Because of complication, sometimes conventional CSO takes a lengthy time to converge and cannot attain the precise solution. For solving reactive power dispatch problem and to improve the convergence accuracy level, we propose a new adaptive CSO namely ‘Adaptive Cat Swarm Optimization’ (ACSO). First, we take account of a new-fangled adaptive inertia weight to velocity equation and then employ an adaptive acceleration coefficient. Second, by utilizing the information of two previous or next dimensions and applying a new-fangled factor, we attain to a new position update equation composing the average of position and velocity information. The projected ACSO has been tested on standard IEEE 57 bus test system and simulation results shows clearly about the high-quality performance of the planned algorithm in tumbling the real power loss.
Memory Polynomial Based Adaptive Digital PredistorterIJERA Editor
Digital predistortion (DPD) is a baseband signal processing technique that corrects for impairments in RF
power amplifiers (PAs). These impairments cause out-of-band emissions or spectral regrowth and in-band
distortion, which correlate with an increased bit error rate (BER). Wideband signals with a high peak-to-average
ratio, are more susceptible to these unwanted effects. So to reduce these impairments, this paper proposes the
modeling of the digital predistortion for the power amplifier using GSA algorithm.
Embellished Particle Swarm Optimization Algorithm for Solving Reactive Power ...ijeei-iaes
This paper proposes Embellished Particle Swarm Optimization (EPSO) algorithm for solving reactive power problem .The main concept of Embellished Particle Swarm Optimization is to extend the single population PSO to the interacting multi-swarm model. Through this multi-swarm cooperative approach, diversity in the whole swarm community can be upheld. Concurrently, the swarm-to-swarm mechanism drastically speeds up the swarm community to converge to the global near optimum. In order to evaluate the performance of the proposed algorithm, it has been tested in standard IEEE 57,118 bus systems and results show that Embellished Particle Swarm Optimization (EPSO) is more efficient in reducing the Real power losses when compared to other standard reported algorithms.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
This paper introduces a solution of the economic load dispatch (ELD) problem using a hybrid approach of fuzzy logic and genetic algorithm (GA). The proposed method combines and extends the attractive features of both fuzzy logic and GA. The proposed approach is compared with lambda iteration method (LIM) and GA. The investigation reveals that the proposed approach can provide accurate solution with fast convergence characteristics and is superior to the GA and LIM.
Enriched Firefly Algorithm for Solving Reactive Power Problemijeei-iaes
In this paper, Enriched Firefly Algorithm (EFA) is planned to solve optimal reactive power dispatch problem. This algorithm is a kind of swarm intelligence algorithm based on the response of a firefly to the light of other fireflies. In this paper, we plan an augmentation on the original firefly algorithm. The proposed algorithm extends the single population FA to the interacting multi-swarms by cooperative Models. The proposed EFA has been tested on standard IEEE 30 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.
NOVEL PSO STRATEGY FOR TRANSMISSION CONGESTION MANAGEMENTelelijjournal
In post deregulated era of power system load characteristics become more erratic. Unplanned transactions
of electrical power through transmission lines of particular path may occur due to low cost offered by
generating companies. As a consequence those lines driven close to their operating limits and becomes
congested as the lines are originally designed for traditional vertically integrated structure of power
system. This congestion in transmission lines is unpredictable with deterministic load flow strategy.
Rescheduling active and reactive power output of generators is the promising way to manage congestion.
In this paper Particle Swarm Optimization (PSO) with varying inertia weight strategy, with two variants
e1-PSO and e-2 PSO is applied for optimal solution of active and reactive power rescheduling for
managing congestion. The generators sensitivity technique is opted for identifying participating generators
for managing congestion. Proposed algorithm is tested on IEEE 30 bus system. Comparison is made
between results obtained from proposed techniques to that of results reported in previous literature.
Reduction of Active Power Loss byUsing Adaptive Cat Swarm Optimizationijeei-iaes
This paper presents, an Adaptive Cat Swarm Optimization (ACSO) for solving reactive power dispatch problem. Cat Swarm Optimization (CSO) is one of the new-fangled swarm intelligence algorithms for finding the most excellent global solution. Because of complication, sometimes conventional CSO takes a lengthy time to converge and cannot attain the precise solution. For solving reactive power dispatch problem and to improve the convergence accuracy level, we propose a new adaptive CSO namely ‘Adaptive Cat Swarm Optimization’ (ACSO). First, we take account of a new-fangled adaptive inertia weight to velocity equation and then employ an adaptive acceleration coefficient. Second, by utilizing the information of two previous or next dimensions and applying a new-fangled factor, we attain to a new position update equation composing the average of position and velocity information. The projected ACSO has been tested on standard IEEE 57 bus test system and simulation results shows clearly about the high-quality performance of the planned algorithm in tumbling the real power loss.
Memory Polynomial Based Adaptive Digital PredistorterIJERA Editor
Digital predistortion (DPD) is a baseband signal processing technique that corrects for impairments in RF
power amplifiers (PAs). These impairments cause out-of-band emissions or spectral regrowth and in-band
distortion, which correlate with an increased bit error rate (BER). Wideband signals with a high peak-to-average
ratio, are more susceptible to these unwanted effects. So to reduce these impairments, this paper proposes the
modeling of the digital predistortion for the power amplifier using GSA algorithm.
Embellished Particle Swarm Optimization Algorithm for Solving Reactive Power ...ijeei-iaes
This paper proposes Embellished Particle Swarm Optimization (EPSO) algorithm for solving reactive power problem .The main concept of Embellished Particle Swarm Optimization is to extend the single population PSO to the interacting multi-swarm model. Through this multi-swarm cooperative approach, diversity in the whole swarm community can be upheld. Concurrently, the swarm-to-swarm mechanism drastically speeds up the swarm community to converge to the global near optimum. In order to evaluate the performance of the proposed algorithm, it has been tested in standard IEEE 57,118 bus systems and results show that Embellished Particle Swarm Optimization (EPSO) is more efficient in reducing the Real power losses when compared to other standard reported algorithms.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
This paper introduces a solution of the economic load dispatch (ELD) problem using a hybrid approach of fuzzy logic and genetic algorithm (GA). The proposed method combines and extends the attractive features of both fuzzy logic and GA. The proposed approach is compared with lambda iteration method (LIM) and GA. The investigation reveals that the proposed approach can provide accurate solution with fast convergence characteristics and is superior to the GA and LIM.
Solving the Power Purchase Cost Optimization Problem with Improved DE AlgorithmIJEACS
Under the deregulation of generation market in China, all distributed generators will particular in electric power bidding. Therefore power purchase cost optimization (PPCO) problem has been getting more attention of power grid Company. However, under the competition principle, they can purchase power from several of power plants, therefor, there exist continuous and integral variables in purchase cost model, which is difficult to solve by classical linear optimization method. An improved differential evolution algorithm is proposed and employed to solve the PPCO problem, which targets on minimum purchase cost, considering the supply and demand balance, generation and transfer capability as constraints. It yields the global optimum solution of the PPCO problem. The numerical results show that the proposed algorithm can solve the PPCO problem and saves the costs of power purchase. It has a widely practical value of application.
Firefly Algorithm to Opmimal Distribution of Reactive Power Compensation Units IJECEIAES
The issue of electric power grid mode of optimization is one of the basic directions in power engineering research. Currently, methods other than classical optimization methods based on various bio-heuristic algorithms are applied. The problems of reactive power optimization in a power grid using bio-heuristic algorithms are considered. These algorithms allow obtaining more efficient solutions as well as taking into account several criteria. The Firefly algorithm is adapted to optimize the placement of reactive power sources as well as to select their values. A key feature of the proposed modification of the Firefly algorithm is the solution for the multi-objective optimization problem. Algorithms based on a bio-heuristic process can find a neighborhood of global extreme, so a local gradient descent in the neighborhood is applied for a more accurate solution of the problem. Comparison of gradient descent, Firefly algorithm and Firefly algorithm with gradient descent is carried out.
Implementation of an Effective Biogeography Based Algorithm
(EBBO) for Economic Load Dispatch (ELD) problems in power system in order to obtain optimal
economic dispatch with minimum generation cost. Approach: A viable methodology has been
implemented for a 20 unit generator system to minimize the fuel cost function considering the
transmission loss and system operating limit constraints and is compared with other approaches such as
BBO, Lambda Iteration and Hopfield Model. Results: Proposed algorithm has been applied to ELD
problems for verifying its feasibility and the comparison of results are tabulated and pictorial
visualization for convergence of EBBO is represented. Conclusion: Comparing with the other existing
techniques, the EBBO gives better result by considering the quality of the solution obtained. This
method could be an alternative approach for solving the ELD problems in practical power system.
Hybrid Quantum Genetic Particle Swarm Optimization Algorithm For Solving Opti...paperpublications3
Abstract: This paper presents hybrid particle swarm algorithm for solving the multi-objective reactive power dispatch problem. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. Evolutionary algorithm and Swarm Intelligence algorithm (EA, SI), a part of Bio inspired optimization algorithm, have been widely used to solve numerous optimization problem in various science and engineering domains. In this paper, a framework of hybrid particle swarm optimization algorithm, called Hybrid quantum genetic particle swarm optimization (HQGPSO), is proposed by reasonably combining the Q-bit evolutionary search of quantum particle swarm optimization (QPSO) algorithm and binary bit evolutionary search of genetic particle swarm optimization (GPSO) in order to achieve better optimization performances. The proposed HQGPSO also can be viewed as a kind of hybridization of micro-space based search and macro-space based search, which enriches the searching behavior to enhance and balance the exploration and exploitation abilities in the whole searching space. In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other algorithms.
Keywords: quantum particle swarm optimization, genetic particle swarm optimization, hybrid algorithm Optimization, Swarm Intelligence, optimal reactive power, Transmission loss.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Determining optimal location and size of capacitors in radial distribution ne...IJECEIAES
In this study, the problem of optimal capacitor location and size determination (OCLSD) in radial distribution networks for reducing losses is unraveled by moth swarm algorithm (MSA). MSA is one of the most powerful meta-heuristic algorithm that is taken from the inspiration of the food source finding behavior of moths. Four study cases of installing different numbers of capacitors in the 15-bus radial distribution test system including two, three, four and five capacitors areemployed to run the applied MSA for an investigation of behavior and assessment of performances. Power loss and the improvement of voltage profile obtained by MSA are compared with those fromother methods. As a result, it can be concluded that MSA can give a good truthful and effective solution method for OCLSD problem.
Optimal Reactive Power Dispatch using Crow Search Algorithm IJECEIAES
The optimal reactive power dispatch is a kind of optimization problem that plays a very important role in the operation and control of the power system. This work presents a meta-heuristic based approach to solve the optimal reactive power dispatch problem. The proposed approach employs Crow Search algorithm to find the values for optimal setting of optimal reactive power dispatch control variables. The proposed way of approach is scrutinized and further being tested on the standard IEEE 30-bus, 57-bus and 118-bus test system with different objectives which includes the minimization of real power losses, total voltage deviation and also the enhancement of voltage stability. The simulation results procured thus indicates the supremacy of the proposed approach over the other approaches cited in the literature.
This paper presents the implementation of multiple distributed generations planning in distribution system using computational intelligence technique. A pre-developed computational intelligence optimization technique named as Embedded Meta EP-Firefly Algorithm (EMEFA) was utilized to determine distribution loss and penetration level for the purpose of distributed generation (DG) installation. In this study, the Artificial Neural Network (ANN) was used in order to solve the complexity of the multiple DG concepts. EMEFA-ANN was developed to optimize the weight of the ANN to minimize the mean squared error. The proposed method was validated on IEEE 69 Bus distribution system with several load variations scenario. The case study was conducted based on the multiple unit of DG in distribution system by considering the DGs are modeled as type I which is capable of injecting real power. Results obtained from the study could be utilized by the utility and energy commission for loss reduction scheme in distribution system.
Hybrid method for achieving Pareto front on economic emission dispatch IJECEIAES
In this paper hybrid method, Modified Nondominated Sorted Genetic Algorithm (MNSGA-II) and Modified Population Variant Differential Evolution(MPVDE) have been placed in effect in achieving the best optimal solution of Multiobjective economic emission load dispatch optimization problem. In this technique latter, one is used to enforce the assigned percent of the population and the remaining with the former one. To overcome the premature convergence in an optimization problem diversity preserving operator is employed, from the tradeoff curve the best optimal solution is predicted using fuzzy set theory. This methodology validated on IEEE 30 bus test system with six generators, IEEE 118 bus test system with fourteen generators and with a forty generators test system. The solutions are dissimilitude with the existing metaheuristic methods like Strength Pareto Evolutionary Algorithm-II, Multiobjective differential evolution, Multiobjective Particle Swarm optimization, Fuzzy clustering particle swarm optimization, Nondominated sorting genetic algorithm-II.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
ARTIFICIAL NEURAL NETWORK APPROACH TO MODELING OF POLYPROPYLENE REACTORijac123
This paper shows modeling of highly nonlinear polymerization process using the artificial neural network approach for the model predictive purposes. Polymerization occurs in a fluidized bed polypropylene reactor using Ziegler - Natta catalyst and the main objective was modeling of the reactor production rate.
The data set used for an identification of the model is a real process data received from an existing polypropylene plant and the identified model is a nonlinear autoregressive neural network with the exogenous input. Performance of a trained network has been verified using the real process data and the
ability of the production rate prediction is shown in the conclusion.
Genetic algorithm based Different Re-dispatching Scheduling of Generator Unit...IDES Editor
Proper pricing of active power is an important issue
in deregulated power environment. This paper presents a
flexible formulation for determining short run marginal cost
of synchronous generators using genetic algorithm based
different re-dispatching scheduling considering economic load
dispatch as well as optimized loss condition. By integrating
genetic algorithm based solution, problem formulation became
easier. The solution obtained from this methodology is quite
encouraging and useful in the economic point of view and it
has been observed that for calculating short run marginal
cost, generator re-dispatching solution is better than classical
method solution. The proposed approach is efficient in the
real time application and allows for carrying out active power
pricing independently. The paper includes test result of IEEE
30 bus standard test system.
Hybrid Particle Swarm Optimization for Multi-objective Reactive Power Optimiz...IDES Editor
This paper presents a new hybrid particle swarm
optimization (HPSO) method for solving multi-objective real
power optimization problem. The objectives of the
optimization problem are to minimize the losses and to
maximize the voltage stability margin. The proposed method
expands the original GA and PSO to tackle the mixed –integer
non- linear optimization problem and achieves the voltage
stability enhancement with continuous and discrete control
variables such as generator terminal voltages, tap position of
transformers and reactive power sources. A comparison is made
with conventional, GA and PSO methods for the real power
losses and this method is found to be effective than other
methods. It is evaluated on the IEEE 30 and 57 bus test system,
and the simulation results show the effectiveness of this
approach for improving voltage stability of the system.
Dwindling of real power loss by using Improved Bees Algorithmpaperpublications3
Abstract: In this paper, a new Improved Bees Algorithm (IBA) is proposed for solving reactive power dispatch problem. The aim of this paper is to utilize an optimization algorithm called the improved Bees Algorithm, inspired from the natural foraging behaviour of honey bees, to solve the reactive power dispatch problem. The IBA algorithm executes both an exploitative neighbourhood search combined with arbitrary explorative search. The proposed Improved Imperialist Competitive Algorithm (IBA) algorithm has been tested on standard IEEE 57 bus test system and simulation results show clearly the high-quality performance of the projected algorithm in reducing the real power loss.
Keywords: Optimal Reactive Power, Transmission loss, honey bee, foraging behaviour, waggle dance, bee’s algorithm, swarm intelligence, swarm-based optimization, adaptive neighbourhood search, site abandonment, random search.
A Hybrid Formulation between Differential Evolution and Simulated Annealing A...TELKOMNIKA JOURNAL
The aim of this paper is to solve the optimal reactive power dispatch (ORPD) problem.
Metaheuristic algorithms have been extensively used to solve optimization problems in a reasonable time
without requiring in-depth knowledge of the treated problem. The perform ance of a metaheuristic requires
a compromise between exploitation and exploration of the search space. However, it is rarely to have the
two characteristics in the same search method, where the current emergence of hybrid methods. This
paper presents a hybrid formulation between two different metaheuristics: differential evolution (based on a
population of solution) and simulated annealing (based on a unique solution) to solve ORPD. The first one
is characterized with the high capacity of exploration, while the second has a good exploitation of the
search space. For the control variables, a mixed representation (continuous/discrete), is proposed. The
robustness of the method is tested on the IEEE 30 bus test system.
Passerine swarm optimization algorithm for solving optimal reactive power dis...IJAAS Team
This paper presents Passerine Swarm Optimization Algorithm (PSOA) for solving optimal reactive power dispatch problem. This algorithm is based on behaviour of social communications of Passerine bird. Basically, Passerine bird has three common behaviours: search behaviour, adherence behaviour and expedition behaviour. Through the shared communications Passerine bird will search for the food and also run away from hunters. By using the Passerine bird communications and behaviour, five basic rules have been created in the PSOA approach to solve the optimal reactive power dispatch problem. Key aspect is to reduce the real power loss and also to keep the variables within the limits. Proposed Passerine Swarm Optimization Algorithm (PSOA) has been tested in standard IEEE 30 bus test system and simulations results reveal about the better performance of the proposed algorithm in reducing the real power loss and enhancing the static voltage stability margin.
Wolf Search Algorithm for Solving Optimal Reactive Power Dispatch Problemijeei-iaes
This paper presents a new bio-inspired heuristic optimization algorithm called the Wolf Search Algorithm (WSA) for solving the multi-objective reactive power dispatch problem. Wolf Search algorithm is a new bio – inspired heuristic algorithm which based on wolf preying behaviour. The way wolves search for food and survive by avoiding their enemies has been imitated to formulate the algorithm for solving the reactive power dispatches. And the speciality of wolf is possessing both individual local searching ability and autonomous flocking movement and this special property has been utilized to formulate the search algorithm .The proposed (WSA) algorithm has been tested on standard IEEE 30 bus test system and simulation results shows clearly about the good performance of the proposed algorithm .
Solving the Power Purchase Cost Optimization Problem with Improved DE AlgorithmIJEACS
Under the deregulation of generation market in China, all distributed generators will particular in electric power bidding. Therefore power purchase cost optimization (PPCO) problem has been getting more attention of power grid Company. However, under the competition principle, they can purchase power from several of power plants, therefor, there exist continuous and integral variables in purchase cost model, which is difficult to solve by classical linear optimization method. An improved differential evolution algorithm is proposed and employed to solve the PPCO problem, which targets on minimum purchase cost, considering the supply and demand balance, generation and transfer capability as constraints. It yields the global optimum solution of the PPCO problem. The numerical results show that the proposed algorithm can solve the PPCO problem and saves the costs of power purchase. It has a widely practical value of application.
Firefly Algorithm to Opmimal Distribution of Reactive Power Compensation Units IJECEIAES
The issue of electric power grid mode of optimization is one of the basic directions in power engineering research. Currently, methods other than classical optimization methods based on various bio-heuristic algorithms are applied. The problems of reactive power optimization in a power grid using bio-heuristic algorithms are considered. These algorithms allow obtaining more efficient solutions as well as taking into account several criteria. The Firefly algorithm is adapted to optimize the placement of reactive power sources as well as to select their values. A key feature of the proposed modification of the Firefly algorithm is the solution for the multi-objective optimization problem. Algorithms based on a bio-heuristic process can find a neighborhood of global extreme, so a local gradient descent in the neighborhood is applied for a more accurate solution of the problem. Comparison of gradient descent, Firefly algorithm and Firefly algorithm with gradient descent is carried out.
Implementation of an Effective Biogeography Based Algorithm
(EBBO) for Economic Load Dispatch (ELD) problems in power system in order to obtain optimal
economic dispatch with minimum generation cost. Approach: A viable methodology has been
implemented for a 20 unit generator system to minimize the fuel cost function considering the
transmission loss and system operating limit constraints and is compared with other approaches such as
BBO, Lambda Iteration and Hopfield Model. Results: Proposed algorithm has been applied to ELD
problems for verifying its feasibility and the comparison of results are tabulated and pictorial
visualization for convergence of EBBO is represented. Conclusion: Comparing with the other existing
techniques, the EBBO gives better result by considering the quality of the solution obtained. This
method could be an alternative approach for solving the ELD problems in practical power system.
Hybrid Quantum Genetic Particle Swarm Optimization Algorithm For Solving Opti...paperpublications3
Abstract: This paper presents hybrid particle swarm algorithm for solving the multi-objective reactive power dispatch problem. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. Evolutionary algorithm and Swarm Intelligence algorithm (EA, SI), a part of Bio inspired optimization algorithm, have been widely used to solve numerous optimization problem in various science and engineering domains. In this paper, a framework of hybrid particle swarm optimization algorithm, called Hybrid quantum genetic particle swarm optimization (HQGPSO), is proposed by reasonably combining the Q-bit evolutionary search of quantum particle swarm optimization (QPSO) algorithm and binary bit evolutionary search of genetic particle swarm optimization (GPSO) in order to achieve better optimization performances. The proposed HQGPSO also can be viewed as a kind of hybridization of micro-space based search and macro-space based search, which enriches the searching behavior to enhance and balance the exploration and exploitation abilities in the whole searching space. In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other algorithms.
Keywords: quantum particle swarm optimization, genetic particle swarm optimization, hybrid algorithm Optimization, Swarm Intelligence, optimal reactive power, Transmission loss.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Determining optimal location and size of capacitors in radial distribution ne...IJECEIAES
In this study, the problem of optimal capacitor location and size determination (OCLSD) in radial distribution networks for reducing losses is unraveled by moth swarm algorithm (MSA). MSA is one of the most powerful meta-heuristic algorithm that is taken from the inspiration of the food source finding behavior of moths. Four study cases of installing different numbers of capacitors in the 15-bus radial distribution test system including two, three, four and five capacitors areemployed to run the applied MSA for an investigation of behavior and assessment of performances. Power loss and the improvement of voltage profile obtained by MSA are compared with those fromother methods. As a result, it can be concluded that MSA can give a good truthful and effective solution method for OCLSD problem.
Optimal Reactive Power Dispatch using Crow Search Algorithm IJECEIAES
The optimal reactive power dispatch is a kind of optimization problem that plays a very important role in the operation and control of the power system. This work presents a meta-heuristic based approach to solve the optimal reactive power dispatch problem. The proposed approach employs Crow Search algorithm to find the values for optimal setting of optimal reactive power dispatch control variables. The proposed way of approach is scrutinized and further being tested on the standard IEEE 30-bus, 57-bus and 118-bus test system with different objectives which includes the minimization of real power losses, total voltage deviation and also the enhancement of voltage stability. The simulation results procured thus indicates the supremacy of the proposed approach over the other approaches cited in the literature.
This paper presents the implementation of multiple distributed generations planning in distribution system using computational intelligence technique. A pre-developed computational intelligence optimization technique named as Embedded Meta EP-Firefly Algorithm (EMEFA) was utilized to determine distribution loss and penetration level for the purpose of distributed generation (DG) installation. In this study, the Artificial Neural Network (ANN) was used in order to solve the complexity of the multiple DG concepts. EMEFA-ANN was developed to optimize the weight of the ANN to minimize the mean squared error. The proposed method was validated on IEEE 69 Bus distribution system with several load variations scenario. The case study was conducted based on the multiple unit of DG in distribution system by considering the DGs are modeled as type I which is capable of injecting real power. Results obtained from the study could be utilized by the utility and energy commission for loss reduction scheme in distribution system.
Hybrid method for achieving Pareto front on economic emission dispatch IJECEIAES
In this paper hybrid method, Modified Nondominated Sorted Genetic Algorithm (MNSGA-II) and Modified Population Variant Differential Evolution(MPVDE) have been placed in effect in achieving the best optimal solution of Multiobjective economic emission load dispatch optimization problem. In this technique latter, one is used to enforce the assigned percent of the population and the remaining with the former one. To overcome the premature convergence in an optimization problem diversity preserving operator is employed, from the tradeoff curve the best optimal solution is predicted using fuzzy set theory. This methodology validated on IEEE 30 bus test system with six generators, IEEE 118 bus test system with fourteen generators and with a forty generators test system. The solutions are dissimilitude with the existing metaheuristic methods like Strength Pareto Evolutionary Algorithm-II, Multiobjective differential evolution, Multiobjective Particle Swarm optimization, Fuzzy clustering particle swarm optimization, Nondominated sorting genetic algorithm-II.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
ARTIFICIAL NEURAL NETWORK APPROACH TO MODELING OF POLYPROPYLENE REACTORijac123
This paper shows modeling of highly nonlinear polymerization process using the artificial neural network approach for the model predictive purposes. Polymerization occurs in a fluidized bed polypropylene reactor using Ziegler - Natta catalyst and the main objective was modeling of the reactor production rate.
The data set used for an identification of the model is a real process data received from an existing polypropylene plant and the identified model is a nonlinear autoregressive neural network with the exogenous input. Performance of a trained network has been verified using the real process data and the
ability of the production rate prediction is shown in the conclusion.
Genetic algorithm based Different Re-dispatching Scheduling of Generator Unit...IDES Editor
Proper pricing of active power is an important issue
in deregulated power environment. This paper presents a
flexible formulation for determining short run marginal cost
of synchronous generators using genetic algorithm based
different re-dispatching scheduling considering economic load
dispatch as well as optimized loss condition. By integrating
genetic algorithm based solution, problem formulation became
easier. The solution obtained from this methodology is quite
encouraging and useful in the economic point of view and it
has been observed that for calculating short run marginal
cost, generator re-dispatching solution is better than classical
method solution. The proposed approach is efficient in the
real time application and allows for carrying out active power
pricing independently. The paper includes test result of IEEE
30 bus standard test system.
Hybrid Particle Swarm Optimization for Multi-objective Reactive Power Optimiz...IDES Editor
This paper presents a new hybrid particle swarm
optimization (HPSO) method for solving multi-objective real
power optimization problem. The objectives of the
optimization problem are to minimize the losses and to
maximize the voltage stability margin. The proposed method
expands the original GA and PSO to tackle the mixed –integer
non- linear optimization problem and achieves the voltage
stability enhancement with continuous and discrete control
variables such as generator terminal voltages, tap position of
transformers and reactive power sources. A comparison is made
with conventional, GA and PSO methods for the real power
losses and this method is found to be effective than other
methods. It is evaluated on the IEEE 30 and 57 bus test system,
and the simulation results show the effectiveness of this
approach for improving voltage stability of the system.
Dwindling of real power loss by using Improved Bees Algorithmpaperpublications3
Abstract: In this paper, a new Improved Bees Algorithm (IBA) is proposed for solving reactive power dispatch problem. The aim of this paper is to utilize an optimization algorithm called the improved Bees Algorithm, inspired from the natural foraging behaviour of honey bees, to solve the reactive power dispatch problem. The IBA algorithm executes both an exploitative neighbourhood search combined with arbitrary explorative search. The proposed Improved Imperialist Competitive Algorithm (IBA) algorithm has been tested on standard IEEE 57 bus test system and simulation results show clearly the high-quality performance of the projected algorithm in reducing the real power loss.
Keywords: Optimal Reactive Power, Transmission loss, honey bee, foraging behaviour, waggle dance, bee’s algorithm, swarm intelligence, swarm-based optimization, adaptive neighbourhood search, site abandonment, random search.
A Hybrid Formulation between Differential Evolution and Simulated Annealing A...TELKOMNIKA JOURNAL
The aim of this paper is to solve the optimal reactive power dispatch (ORPD) problem.
Metaheuristic algorithms have been extensively used to solve optimization problems in a reasonable time
without requiring in-depth knowledge of the treated problem. The perform ance of a metaheuristic requires
a compromise between exploitation and exploration of the search space. However, it is rarely to have the
two characteristics in the same search method, where the current emergence of hybrid methods. This
paper presents a hybrid formulation between two different metaheuristics: differential evolution (based on a
population of solution) and simulated annealing (based on a unique solution) to solve ORPD. The first one
is characterized with the high capacity of exploration, while the second has a good exploitation of the
search space. For the control variables, a mixed representation (continuous/discrete), is proposed. The
robustness of the method is tested on the IEEE 30 bus test system.
Passerine swarm optimization algorithm for solving optimal reactive power dis...IJAAS Team
This paper presents Passerine Swarm Optimization Algorithm (PSOA) for solving optimal reactive power dispatch problem. This algorithm is based on behaviour of social communications of Passerine bird. Basically, Passerine bird has three common behaviours: search behaviour, adherence behaviour and expedition behaviour. Through the shared communications Passerine bird will search for the food and also run away from hunters. By using the Passerine bird communications and behaviour, five basic rules have been created in the PSOA approach to solve the optimal reactive power dispatch problem. Key aspect is to reduce the real power loss and also to keep the variables within the limits. Proposed Passerine Swarm Optimization Algorithm (PSOA) has been tested in standard IEEE 30 bus test system and simulations results reveal about the better performance of the proposed algorithm in reducing the real power loss and enhancing the static voltage stability margin.
Wolf Search Algorithm for Solving Optimal Reactive Power Dispatch Problemijeei-iaes
This paper presents a new bio-inspired heuristic optimization algorithm called the Wolf Search Algorithm (WSA) for solving the multi-objective reactive power dispatch problem. Wolf Search algorithm is a new bio – inspired heuristic algorithm which based on wolf preying behaviour. The way wolves search for food and survive by avoiding their enemies has been imitated to formulate the algorithm for solving the reactive power dispatches. And the speciality of wolf is possessing both individual local searching ability and autonomous flocking movement and this special property has been utilized to formulate the search algorithm .The proposed (WSA) algorithm has been tested on standard IEEE 30 bus test system and simulation results shows clearly about the good performance of the proposed algorithm .
Voltage Profile Enhancement and Reduction of Real Power loss by Hybrid Biogeo...ijeei-iaes
This paper presents Hybrid Biogeography algorithm for solving the multi-objective reactive power dispatch problem in a power system. Real Power Loss minimization and maximization of voltage stability margin are taken as the objectives. Artificial bee colony optimization (ABC) is quick and forceful algorithm for global optimization. Biogeography-Based Optimization (BBO) is a new-fangled biogeography inspired algorithm. It mainly utilizes the biogeography-based relocation operator to share the information among solutions. In this work, a hybrid algorithm with BBO and ABC is projected, and named as HBBABC (Hybrid Biogeography based Artificial Bee Colony Optimization), for the universal numerical optimization problem. HBBABC merge the searching behavior of ABC with that of BBO. Both the algorithms have different solution probing tendency like ABC have good exploration probing tendency while BBO have good exploitation probing tendency. HBBABC used to solve the reactive power dispatch problem and the proposed technique has been tested in standard IEEE30 bus test system.
Development of deep reinforcement learning for inverted pendulumIJECEIAES
This paper presents a modification of the deep Q-network (DQN) in deep reinforcement learning to control the angle of the inverted pendulum (IP). The original DQN method often uses two actions related to two force states like constant negative and positive force values which apply to the cart of IP to maintain the angle between the pendulum and the Y-axis. Due to the changing of too much value of force, the IP may make some oscillation which makes the performance system could be declined. Thus, a modified DQN algorithm is developed based on neural network structure to make a range of force selections for IP to improve the performance of IP. To prove our algorithm, the OpenAI/Gym and Keras libraries are used to develop DQN. All results showed that our proposed controller has higher performance than the original DQN and could be applied to a nonlinear system.
Hybrid Quantum Genetic Particle Swarm Optimization Algorithm For Solving Opti...paperpublications3
Abstract: This paper presents hybrid particle swarm algorithm for solving the multi-objective reactive power dispatch problem. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. Evolutionary algorithm and Swarm Intelligence algorithm (EA, SI), a part of Bio inspired optimization algorithm, have been widely used to solve numerous optimization problem in various science and engineering domains. In this paper, a framework of hybrid particle swarm optimization algorithm, called Hybrid quantum genetic particle swarm optimization (HQGPSO), is proposed by reasonably combining the Q-bit evolutionary search of quantum particle swarm optimization (QPSO) algorithm and binary bit evolutionary search of genetic particle swarm optimization (GPSO) in order to achieve better optimization performances. The proposed HQGPSO also can be viewed as a kind of hybridization of micro-space based search and macro-space based search, which enriches the searching behavior to enhance and balance the exploration and exploitation abilities in the whole searching space. In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other algorithms.
Nowadays, the location and sizing of distributed generation (DG) units in power system network are crucial to be at optimal as it will affect the power system operation in terms of stability and security. In this paper, a new technique termed as Immune Log-Normal Evolutionary Programming (ILNEP) is applied to find the optimal location and size of distributed generation units in power system network. Voltage stability is considered in solving this problem. The proposed technique has been tested on the IEEE 26 bus Reliability Test System to find the optimal location and size of distributed generation in transmission network. In order to study the performance of ILNEP technique in solving DG Installation problem, the results produced by ILNEP were compared with other meta-heuristic techniques like evolutionary programming (EP) and artificial immune system (AIS). It is found that the proposed technique gives better solution in term of lower total system loss compared to the other two techniques.
ECONOMIC LOAD DISPATCH USING PARTICLE SWARM OPTIMIZATIONMln Phaneendra
In this ppt particle swarm optimization (PSO) is applied to allot the active power among the generating stations satisfying the system constraints and minimizing the cost of power generated.The viability of the method is analyzed for its accuracy and rate of convergence. The economic load dispatch problem is solved for three and six unit system using PSO and conventional method for both cases of neglecting and including transmission losses. The results of PSO method were compared with conventional method and were found to be superior.
A Comparative Analysis of Intelligent Techniques to obtain MPPT by Metaheuris...IJMTST Journal
Main objective of this paper is to develop an intelligent and efficient Maximum Power Point Tracking (MPPT) technique. Most recently introduced of intelligent based algorithm Cuckoo search algorithm has been used in this study to develop a novel technique to track the Maximum Power Point (MPP) of a solar cell module. The performances of this algorithm has been compared with other evolutionary soft computing techniques like ABC, FA and PSO. Simulations were done in MATLAB/SIMULINK environment and simulation results show that proposed approach can obtain MPP to a good precision under different solar irradiance and environmental temperatures.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
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
An Heterogeneous Population-Based Genetic Algorithm for Data Clusteringijeei-iaes
As a primary data mining method for knowledge discovery, clustering is a technique of classifying a dataset into groups of similar objects. The most popular method for data clustering K-means suffers from the drawbacks of requiring the number of clusters and their initial centers, which should be provided by the user. In the literature, several methods have proposed in a form of k-means variants, genetic algorithms, or combinations between them for calculating the number of clusters and finding proper clusters centers. However, none of these solutions has provided satisfactory results and determining the number of clusters and the initial centers are still the main challenge in clustering processes. In this paper we present an approach to automatically generate such parameters to achieve optimal clusters using a modified genetic algorithm operating on varied individual structures and using a new crossover operator. Experimental results show that our modified genetic algorithm is a better efficient alternative to the existing approaches.
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Wireless network is ready for hundreds or thousands of nodes, where each node is connected to one or sometimes more sensors. WSN sensor integrated circuits, embedded systems, networks, modems, wireless communication and dissemination of information. The sensor may be an obligation to technology and science. Recent developments underway to miniaturization and low power consumption. They act as a gateway, and prospective clients, I usually have the data on the server WSN. Other components separate routing network routers, called calculating and distributing routing tables. Discussed the routing of wireless energy balance. Optimization solutions, we have created a genetic algorithm. Before selecting an algorithm proposed for the construction of the center console. In this study, the algorithms proposed model simulated results based on "parameters depending dead nodes, the number of bits transmitted to a base station, where the number of units sent to the heads of fuel consumption compared to replay and show that the proposed algorithm has a network of a relative.
Design for Postplacement Mousing based on GSM in Long-Distanceijeei-iaes
This design for mousing is made up of power control module, infrared sensor module, signal processing module, distance information transportation based on GSM and device of power grid. The design consists of two sets of conductors, separately linked by fire wire and null line and distributing alternatively. The major innovation is infrared sensor module with Fresnel lens, and that the infrared detecting area should be spread in one direction at least. When the mouse get into the infrared detecting area, the sensor signal of infrared detecting device is sent to power control module through signal element and then starts the device of power grid to power up to make the mouse be shocked or die. GSM module is adopted to tell that the mouse is caught successfully. This design can be placed in any position that the mouse is always out and no need of baits.
Investigation of TTMC-SVPWM Strategies for Diode Clamped and Cascaded H-bridg...ijeei-iaes
This paper presents a concept of two types multilevel inverters such as diode clamped and cascaded H-bridge for harmonic reduction on high power applications. Normally, multilevel inverters can be used to reduce the harmonic problems in electrical distribution systems. This paer focused on the performance and analysis of a three phase seven level inverter including diode clamped and cascaded H-bridge based on new tripizodal triangular space vector PWM technique approaches. TTMC based modified Space vector Pulse width modulation technique so called tripizodal triangular Space vector Pulse width modulation (TTMC-SVPWM) technique. In this paper the reference sine wave generated as in case of conventional off set injected SVPWM technique. It is observed that the TTMC-Space vector pulse width modulation ensures excellent, close to optimized pulse distribution results and THD is compared to seven level, diode clamped and cascaded multi level inverters. Theoretical investigations were confirmed by the digital simulations using MATLAB/SIMULINK software.
Optimal Power Flow with Reactive Power Compensation for Cost And Loss Minimiz...ijeei-iaes
One of the concerns of power system planners is the problem of optimum cost of generation as well as loss minimization on the grid system. This issue can be addressed in a number of ways; one of such ways is the use of reactive power support (shunt capacitor compensation). This paper used the method of shunt capacitor placement for cost and transmission loss minimization on Nigerian power grid system which is a 24-bus, 330kV network interconnecting four thermal generating stations (Sapele, Delta, Afam and Egbin) and three hydro stations to various load points. Simulation in MATLAB was performed on the Nigerian 330kV transmission grid system. The technique employed was based on the optimal power flow formulations using Newton-Raphson iterative method for the load flow analysis of the grid system. The results show that when shunt capacitor was employed as the inequality constraints on the power system, there is a reduction in the total cost of generation accompanied with reduction in the total system losses with a significant improvement in the system voltage profile
Mitigation of Power Quality Problems Using Custom Power Devices: A Reviewijeei-iaes
Electrical power quality (EPQ) in distribution systems is a critical issue for commercial, industrial and residential applications. The new concept of advanced power electronic based Custom Power Devices (CPDs) mainly distributed static synchronous compensator (D-STATCOM), dynamic voltage restorer (DVR) and unified power quality conditioner (UPQC) have been developed due to lacking the performance of traditional compensating devices to minimize power quality disturbances. This paper presents a comprehensive review on D-STATCOM, DVR and UPQC to solve the electrical power quality problems of the distribution networks. This is intended to present a broad overview of the various possible DSTATCOM, DVR and UPQC configurations for single-phase (two wire) and three-phase (three-wire and four-wire) networks and control strategies for the compensation of various power quality disturbances. Apart from this, comprehensive explanation, comparison, and discussion on D-STATCOM, DVR, and UPQC are presented. This paper is aimed to explore a broad prospective on the status of D-STATCOMs, DVRs, and UPQCs to researchers, engineers and the community dealing with the power quality enhancement. A classified list of some latest research publications on the topic is also appended for a quick reference.
Comparison of Dynamic Stability Response of A SMIB with PI and Fuzzy Controll...ijeei-iaes
Consumer utilities are non –linear in nature. This injects increased flow of current and reduced voltage with distortions which cause adverse effect on the stability of consumer utilities. To overcome this problem we are using a modern Flexible Alternating Current Transmission System controller i.e. distributed power flow controller (DPFC). This controller is similar to UPFC, which can be installed in a transmission line between the two electrical areas. In DPFC, instead of the common Dc link capacitor three single phase converters are used. In this paper we are concentrating on system stability (oscillation damping). For analyzing the stability of a single machine infinite bus system (SMIB) we have used PI controlled Distributed Power Flow Controller (DPFC) and Fuzzy controlled DPFC. All these models are simulated using MATLAB/SIMULINK. Simulation results shows Fuzzy controlled DPFC are better than PI controlled DPFC. The significance of the results are better stability and constant power supply.
Intelligent Management on the Home Consumers with Zero Energy Consumptionijeei-iaes
The energy and environment crisis has forced modern humans to think about new and clean energy sources and in particular, renewable energy sources. With the development of home network, the residents have the opportunity to plan the home electricity usage with the goal of reducing the cost of electricity. In this regard, to improve the energy consumption efficiency in residential buildings, smart buildings with zero energy consumption were considered as a proper option. Zero-energy building is a building that has smart equipment whose integral of generated and consumed power within a year is zero. In this article, smart devices submit their power consumption with regard to the requested activity associated with the user’s time setting for run times and end times of the work to the energy management unit and ultimately the time to start work will be determined. The problem’s target function is reducing the energy cost for the consumer with taking into account the applicable limitations.
Analysing Transportation Data with Open Source Big Data Analytic Toolsijeei-iaes
Big data analytics allows a vast amount of structured and unstructured data to be effectively processed so that correlations, hidden patterns, and other useful information can be mined from the data. Several open source big data analytic tools that can perform tasks such as dimensionality reduction, feature extraction, transformation, optimization, are now available. One interesting area where such tools can provide effective solutions is transportation. Big data analytics can be used to efficiently manage transport infrastructure assets such as roads, airports, bus stations or ports. In this paper an overview of two open source big data analytic tools is first provided followed by a simple demonstration of application of these tools on transport dataset.
A Pattern Classification Based approach for Blur Classificationijeei-iaes
Blur type identification is one of the most crucial step of image restoration. In case of blind restoration of such images, it is generally assumed that the blur type is known prior to restoration of such images. However, it is not practical in real applications. So, blur type identification is extremely desirable before application of blind restoration technique to restore a blurred image. An approach to categorize blur in three classes namely motion, defocus, and combined blur is presented in this paper. Curvelet transform based energy features are utilized as features of blur patterns and a neural network is designed for classification. The simulation results show preciseness of proposed approach.
Computing Some Degree-Based Topological Indices of Grapheneijeei-iaes
Graphene is one of the most promising nanomaterial because of its unique combination of superb properties, which opens a way for its exploitation in a wide spectrum of applications ranging from electronics to optics, sensors, and bio devices. Inspired by recent work on Graphene of computing topological indices, here we compute new topological indices viz. Arithmetic-Geometric index (AG2 index), SK3 index and Sanskruti index of a molecular graph G and obtain the explicit formulae of these indices for Graphene.
A Lyapunov Based Approach to Enchance Wind Turbine Stabilityijeei-iaes
This paper introduces a nonlinear control of a wind turbine based on a Double Feed Induction Generator. The Rotor Side converter is controlled by using field oriented control and Backstepping strategy to enhance the dynamic stability response. The Grid Side converter is controlled by a sliding mode. These methods aim to increase dynamic system stability for variable wind speed. Hence, The Doubly Fed Induction Generator (DFIG) is studied in order to illustrate its behavior in case of severe disturbance, and its dynamic response in grid connected mode for variable speed wind operation. The model is presented and simulated under Matlab/ Simulink.
Fuzzy Control of a Large Crane Structureijeei-iaes
The usage of tower cranes, one type of rotary cranes, is common in many industrial structures, e.g., shipyards, factories, etc. With the size of these cranes becoming larger and the motion expected to be faster and has no prescribed path, their manual operation becomes difficult and hence, automatic closed-loop control schemes are very important in the operation of rotary crane. In this paper, the plant of concern is a tower crane consists of a rotatable jib that carries a trolley which is capable of traveling over the length of the jib. There is a pendulum-like end line attached to the trolley through a cable of variable length. A fuzzy logic controller with various types of membership functions is implemented for controlling the position of the trolley and damping the load oscillations. It consists of two main types of controllers radial and rotational each of two fuzzy inference engines (FIEs). The radial controller is used to control the trolley position and the rotational is used for damping the load oscillations. Computer simulations are used to verify the performance of the controller. The results from the simulations show the effectiveness of the method in the control of tower crane keeping load swings small at the end of motion.
Site Diversity Technique Application on Rain Attenuation for Lagosijeei-iaes
This paper studied the impact of site diversity (SD) as a fade mitigation technique on rain attenuation at 12 GHz for Lagos. SD is one of the most effective methods to overcome such large fades due to rain attenuation that takes advantage of the usually localized nature of intense rainfall by receiving the satellite downlink signal at two or more earth stations to minimize the prospect of potential diversity stations being simultaneously subjected to significant rain attenuation. One year (January to December 2011) hourly rain gauge data was sourced from the Nigerian Meteorological Agency (NIMET) for three sites (Ikeja, Ikorodu and Marina) in Lagos, Nigeria. Significant improvement in both performance and availability was observed with the application of SD technique; again, separation distance was seen to be responsible for this observed performance improvements.
Impact of Next Generation Cognitive Radio Network on the Wireless Green Eco s...ijeei-iaes
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Music Recommendation System with User-based and Item-based Collaborative Filt...ijeei-iaes
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CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERS
Modified Monkey Optimization Algorithm for Solving Optimal Reactive Power Dispatch Problem
1. Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Vol. 3, No. 2, June 2015, pp. 55~62
ISSN: 2089-3272 55
Received November 18, 2014; Revised March 2, 2015; Accepted March 18, 2015
Modified Monkey Optimization Algorithm for Solving
Optimal Reactive Power Dispatch Problem
K. Lenin, B. Ravindhranath Reddy, M. Suryakalavathi
Jawaharlal Nehru Technological University Kukatpally, Hyderabad 500 085, India.
e-mail: gklenin@gmail.com
Abstract
In this paper, a novel approach Modified Monkey optimization (MMO) algorithm for solving
optimal reactive power dispatch problem has been presented. MMO is a population based stochastic
meta-heuristic algorithm and it is inspired by intelligent foraging behaviour of monkeys. This paper
improves both local leader and global leader phases. The proposed (MMO) algorithm has been tested in
standard IEEE 30 bus test system and simulation results show the worthy performance of the proposed
algorithm in reducing the real power loss.
Keywords: optimal reactive power, transmission loss, modified monkey optimization, bio-inspired
algorithm
1. Introduction
Reactive power optimization plays a key role in optimal operation of power systems.
Many numerical methods [1-7] have been applied to solve the optimal reactive power dispatch
problem. The problem of voltage stability plays a strategic role in power system planning and
operation [8]. So many Evolutionary algorithms have been already proposed to solve the
reactive power flow problem [9-11]. In [12, 13], Hybrid differential evolution algorithm and
Biogeography Based algorithm has been projected to solve the reactive power dispatch
problem. In [14, 15], a fuzzy based technique and improved evolutionary programming has
been applied to solve the optimal reactive power dispatch problem. In [16, 17] nonlinear interior
point method and pattern based algorithm has been used to solve the reactive power problem.
In [18-20], various types of probabilistic algorithms utilized to solve optimal reactive power
problem. This paper introduces a novel Modified Monkey optimization for solving optimal
reactive power dispatch power problem. Monkey Optimization algorithm [21] is fresh entry in
class of swarm intelligence. This Monkey Optimization algorithm is enthused by fission fusion
social structure based on foraging behaviour of monkeys when searching for quality food source
and for mating. Alike to any other population based optimization techniques, artificial bee colony
(ABC) consists of a population of intrinsic solutions. The intrinsic solutions are food sources of
honey bees. The fitness is decided in terms of the quality of the food source that is nectar
amount. Artificial bee colony is comparatively a direct, quick and population based stochastic
exploration technique in the field of nature inspired algorithms. Monkey Optimization algorithm is
also alike to ABC in nature. There are two fundamental processes which drive the swarm to
modernize in ABC: the deviation process, which empowers exploring different fields of the
exploration space, and the selection process, which guarantees the exploitation of the
preceding experience. However, it has been shown that the ABC may infrequently stop moving
toward the global optimum even though the population has not meeting to a local optimum [22].
It can be observed that the solution search equation of ABC algorithm is good at exploration but
poor at exploitation [23]. Therefore, to uphold the good equilibrium between exploration and
exploitation behaviour of ABC, it is extremely expected to develop a local exploration method in
the basic ABC to strengthen the exploration region. The proposed MMO algorithm has been
evaluated in standard IEEE 30 bus test system & the simulation results show that our proposed
approach outperforms all reported algorithms in minimization of real power loss.
2. ISSN: 2089-3272
IJEEI Vol. 3, No. 2, June 2015 : 55 – 62
56
2. Problem Formulation
2.1. Active Power Loss
The objective of the reactive power dispatch is to minimize the active power loss in the
transmission network, which can be described as follows:
F PL ∑ g∈ V V 2V V cosθ (1)
Or,
F PL ∑ P P P ∑ P P∈ (2)
Where gk : is the conductance of branch between nodes i and j, Nbr: is the total number of
transmission lines in power systems. Pd: is the total active power demand, Pgi: is the generator
active power of unit i, and Pgsalck: is the generator active power of slack bus.
2.2. Voltage Profile Improvement
For minimizing the voltage deviation in PQ buses, the objective function becomes:
F PL ω VD (3)
Where ωv: is a weighting factor of voltage deviation.
VD is the voltage deviation given by:
VD ∑ |V 1| (4)
2.3. Equality Constraint
The equality constraint of the optimal reactive power dispatch power (ORPD) problem is
represented by the power balance equation, where the total power generation must cover the
total power demand and the power losses:
P P P (5)
This equation is solved by running Newton Raphson load flow method, by calculating
the active power of slack bus to determine active power loss.
2.4. Inequality Constraints
The inequality constraints reflect the limits on components in the power system as well
as the limits created to ensure system security. Upper and lower bounds on the active power of
slack bus, and reactive power of generators:
P P P (6)
Q Q Q , i ∈ N (7)
Upper and lower bounds on the bus voltage magnitudes:
V V V , i ∈ N (8)
Upper and lower bounds on the transformers tap ratios:
T T T , i ∈ N (9)
Upper and lower bounds on the compensators reactive powers:
Q Q Q , i ∈ N (10)
3. IJEEI ISSN: 2089-3272
Modified Monkey Optimization Algorithm for Solving Optimal Reactive Power… (K. Lenin)
57
Where N is the total number of buses, NT is the total number of Transformers; Nc is the total
number of shunt reactive compensators.
3. Monkey Optimization Algorithm
JC Bansal et al. [21] used Social activities of monkeys to develop a stochastic
optimization algorithm that impersonate fission-fusion social structure based intelligent foraging
behaviour of spider monkeys. JC Bansal et al. [21] identified four key features.
Step 1. The group starts food foraging and evaluates their distance from the food.
Step 2. Group members update their positions based on the distance from the foods
source and all over again evaluate distance from the food sources.
Step 3. Moreover, in this step, the local leader modernizes its best location within the
group and if the location is not rationalized for a predefined number of times then all members of
that group start searching of the food sources in different directions.
Step 4. Consequently, in the last step, the global leader keep informed its eternally best
position and in case of inactivity, it divides the group into smaller size subgroups.
It is witnessed that in SMO algorithm [21] there are two most significant control
parameters are Global Leader Limit (GLlimit) and Local Leader Limit (LLlimit) which provide
suitable direction to global and local leaders respectively. In SMO immobility can be evaded by
using LLlimit. If a local group leader does not keep informed her-self after a predefined number
of times then that group is re-directed to another direction for in order to quest for food. Here,
the term predefined number of times is referred as LLlimit. An additional control parameter, that
is to say Global Leader Limit (GLlimit) is used for the identical intention by global leader. The
global leader divides the group into smaller sub-groups if she does not modernize in a
predefined number of times that is GLlimit. Similar to the other population-based algorithms,
SMO is also a hit and trial based mutual iterative approach. The SMO development consists of
seven major phases. The detailed description of each step of SMO achievement is delineated
below:
a) Initialization of the Population
At the outset, SMO [21] produces an consistently dispersed primary population of N
spider monkeys where each monkey SMi (i = 1, 2, ..., N) is a vector of dimension D. At this point
D is the number of variables in the optimization problem and SMi represent the position of ith
Spider Monkey (SM) in the population. Each spider monkey SM corresponds to the potential
solution of the problem under consideration. Each SMi is initialized as follows:
SM SM ∅ SM SM (11)
b) Local Leader Phase (LLP)
The second phase in SMO is Local Leader phase. In this phase SM update its existing
location based on the information from the local leader understanding as well as local group
members understanding. The fitness value of so obtained new location is calculated. If the
fitness value of the new location is higher than that of the previous location, subsequently the
SM updates his location with the new one. The location modernizes equation for ith SM (which
is a member of kth local group) in this phase is as follow:
SM SM ∅ LL SM ∅ SM SM (12)
where ∅ ∈ 0,1 and ∅ ∈ 1,1
Where SMij is the jth dimension of the ith SM, LLkj represents the jth dimension of the kth local
group leader position. SMrj is the jth dimension of the rth SM which is chosen randomly within
kth group such that r≠ i.
c) Global Leader Phase (GLP)
After achievement of the Local Leader phase next phase is Global Leader phase (GLP).
During GLP phase, all the SM’s bring up to date their location by means of understanding of
Global Leader and local group member’s understanding. The location modernizes equation for
this phase is as follows:
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SM SM ∅ GL SM ∅ SM SM (13)
where ∅ ∈ 0,1 and ∅ ∈ 1,1 .
Where GLj stands for the jth dimension of the global leader location and j ∈{1, 2, ...,D} is the
haphazardly preferred index.
In GLP phase, the locations of spider monkeys (SMi) are modernized based on
probabilities pi’s which are considered using their fitness. In this way a better candidate will
have more chance to make itself better. The probability pi may be calculated using following
expression:
p 0.9 0.1 (14)
Here fitnessi is the fitness value of the ith SM and fitnessmax is the maximum fitness in
the group. Further, the fitness of the newly produced position of the SM’s is calculated and
compared with the old one and adopted the better position.
d) Global Leader Learning (GLL) phase
In GLL phase, the location of the global leader is restructured by applying the rapacious
selection approach in the population i.e., the location of the SM having most outstanding fitness
in the population is selected as the modernized location of the global leader. Further, it is
checked that the location of global leader is modernizing or not and if not then the Global Limit
Count is incremented by 1.
e) Local Leader Learning (LLL) phase
In LLL phase, the position of the local leader is modernized by applying the greedy
selection in that group i.e., the location of the SM having unmatched fitness in that group is
preferred as the updated location of the local leader. Next, the updated location of the local
leader is compared with the older one and if the local leader is not updated then the Local Limit
Count is incremented by 1.
f) Local Leader Decision (LLD) phase
If any Local Leader position is not modernized up to a predefined threshold called Local
Leader Limit (LLLimit), then all the members of that group update their locations either by
arbitrary initialization or by using mutual information from Global Leader and Local Leader
through equation (15), based on the pr (perturbation rate).
SM SM ∅ GL SM ∅ SM LL (15)
where ∅ ∈ 0,1
It is comprehensible from the equation (15) that the modernized measurement of this
SM is attracted towards global leader and fends off from the local leader.
g) Global Leader Decision (GLD) phase
In GLD phase, the position of global leader is observed and if it is not updated up to a
predefined number of iterations is known as Global Leader Limit (GLlimit), then the global leader
divides the population into smaller groups. Firstly, the population is divided into two groups and
then three groups and so on till the maximum number of groups (MNG) are formed. Each time
in GLD phase, LLL procedure is began to decide on the local leader in the recently fashioned
groups. The case in which maximum number of groups is formed and even then the position of
global leader is not modernized then the global leader pools all the groups to form a single
group. As a consequence the predicted algorithm mimics fusion-fission structure of SMs. The
complete pseudo-code of the SMO algorithm is outlined as follow [21]:
Spider Monkey Optimization (SMO) Algorithm:
Step 1. Set Population, Local Leader Limit (LLlimit), Global Leader Limit (GLlimit) and
Perturbation rate (pr).
Step 2. Calculate fitness (The distance of each individual from corresponding food
sources).
Step 3. Select leaders (global and local both) by smearing greedy selection.
Step 4. while (Annihilation criteria is not fulfilled) do
Step 5. Produce the new locations for all the group members by using self-experience,
local leader experience and group member’s experience. Using Equation (12)
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59
Step 6. Apply the gluttonous selection process between existing location and newly
generated location, based on fitness and select the enhanced one.
Step 7. Calculate the probability pi for all the group members using Equation (14).
Step 8. Produce new locations for the all the group members, selected by pi, by using
self-experience, global leader experience and group members experiences Using Equation (13)
Step 9. Modernize the position of local and global leaders, by applying the greedy
selection process on all the groups.
Step 10. If any Local group leader is not updating her position after a specified number
of times (LLLimit) then re-direct all members of that particular group for foraging by algorithm
using Equation (15).
Step 11. If Global Leader is not modernizing her position for a specified number of times
(GLLimit) then she divides the group into smaller groups by following steps.
Step 12. End While
4. Modified Monkey Optimization (MMO) Algorithm
Exploration of the whole search space and exploitation of the best solutions found in its
proximity may be balanced by maintaining the diversity in local leader and global leader phase
of SMO. In order to balance exploration and exploitation of local search space the proposed
algorithm alter both local leader phase and global leader phase using modified golden section
search (GSS) [24]. Original GSS method does not use any gradient information of the function
to finds the optima of a uni-modal continuous function. GSS processes the interval [a = −1.2, b
= 1.2] and initiates two intermediate points:
F1 = b − (b − a) × ψ, (16)
F2 = a + (b − a) × ψ, (17)
Here ψ = 0.618 is the golden ratio.
The detailed GSS process [25] is described as follows:
Golden Section Search procedure
Input: Optimization function
Min f(x) s.t. a ≤ x ≤ b and termination criteria
Repeat while termination criteria fulfill
Calculate F1and F2 as follow
F1=b-(b-a)*Ѱ and F2=a+(b-a)*Ѱ here a = −1.2, b = 1.2 and Ѱ=0.618(Golden ratio)
Compute f(F1) and f(F2)
If f(F1)< f(F2) then
b = F2 and the solution fall in range [a,b]
else
a = F1 and the solution fall in range [a,b]
end if
end while
The proposed strategy modify Equation (12) and (13) in the following manner. Here f is
determined by GSS process as outlined in above algorithm. Position update in local leader
phase is done using Equation (18).
SM SM ∅ LL SM ∅ SM SM f SM SM (18)
here ∅ ∈ 0,1 and ∅ ∈ 1,1 f is decided by GSS.
Position update in local leader phase is done using Equation (19).
SM SM ∅ GL SM ∅ SM SM f SM SM (19)
here ∅ ∈ 0,1 and ∅ ∈ 1,1 f is decided by GSS.
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MMO Algorithm:
Step 1. Initialize Population, Local Leader Limit (LLlimit), Global Leader Limit (GLlimit)
and Perturbation rate (pr).
Step 2. Compute fitness (The distance of each individual from corresponding food
sources).
Step 3. Select leaders (global and local both) by applying greedy selection.
Step 4. while (extermination criteria is not fulfilled) do
Step 5. For finding the objective (Food Source), generate the new locations for all the
group members by using self-experience, local leader experience and group member’s
experience.
SM SM ∅ LL SM ∅ SM SM f SM SM
where ∅ ∈ 0,1 and ∅ ∈ 1,1 f is decided by GSS.
Step 6. Apply the gluttonous selection process between existing location and newly
generated location, based on fitness and select the better one;
Step 7. Calculate the probability pi for all the group members using,
p 0.9 0.1
Step 8. Produce new locations for the all the group members, selected by pi, by using
self-experience, global leader experience and group member’s experiences.
SM SM ∅ GL SM ∅ SM SM f SM SM
where ∅ ∈ 0,1 and ∅ ∈ 1,1 f is decided by GSS.
Step 9. Modernize the position of local and global leaders, by applying the greedy
selection process on all the groups.
Step 10. If any Local group leader is not updating her position after a specified number
of times (LLLimit) then re-direct all members of that particular group for foraging by algorithm.
if U(0,1) ≥ pr
SM SM ∅ SM SM
Else
SM SM ∅ GL SM ∅ SM LL
where ∅ ∈ 0,1
Step 11. If Global Leader is not updating her position for a specified number of times
(GLLimit) then she divides the group into smaller groups by following steps.
if Global Limit Count > GLLimit then set Global Limit Count = 0
if Number of groups < MNG then
Divide the population into groups.
else
Pool all the groups to make a single group.
Modernize Local Leaders position.
5. Simulation Results
MMO algorithm has been tested on the IEEE 30-bus, 41 branch system. It has a total of
13 control variables as follows: 6 generator-bus voltage magnitudes, 4 transformer-tap settings,
and 2 bus shunt reactive compensators. Bus 1 is the slack bus, 2, 5, 8, 11 and 13 are taken as
PV generator buses and the rest are PQ load buses. The measured security constraints are the
voltage magnitudes of all buses, the reactive power limits of the shunt VAR compensators and
the transformers tap settings limits. The variables limits are listed in Table 1.
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61
The transformer taps and the reactive power source installation are discrete with the
changes step of 0.01. The power limits generators buses are represented in Table 2.
Generators buses are: PV buses 2, 5, 8, 11, 13 and slack bus is 1. The others are PQ-buses.
Table 1. Initial Variables Limits (PU)
Control
variables
Min.value Max.value Type
Generator: Vg 0.92 1.08 Continuous
Load Bus: VL 0.90 1.01 Continuous
T 0.90 1.40 Discrete
Qc -0.11 0.30 Discrete
Table 2. Generators Power Limits in MW and
MVAR
Bus n° Pg Pgmin Pgmax Qgmin
1 97.00 50 200 -20
2 80.00 20 80 -20
5 52.00 15 55 -13
8 20.00 10 31 -13
11 20.00 10 25 -10
13 20.00 11 40 -13
Table 3 show that the proposed approach succeeds in keeping the dependent variables
within their limits. Table 4 summarizes the results of the optimal solution by different methods. It
reveals the reduction of real power loss after optimization.
Table 3. Values of Control Variables after
Optimization and Active Power Loss
Control Variables (p.u) MMO
V1 1.0308
V2 1.0379
V5 1.0190
V8 1.0289
V11 1.0619
V13 1.0428
T4,12 0.00
T6,9 0.01
T6,10 0.91
T28,27 0.90
Q10 0.11
Q24 0.10
PLOSS 4.5328
VD 0.9081
Table 4. Comparison Results of Different
Methods
Methods Ploss (MW)
SGA (26) 4.98
PSO (27) 4.9262
LP (28) 5.988
EP (28) 4.963
CGA (28) 4.980
AGA (28) 4.926
CLPSO (28) 4.7208
HSA (29) 4.7624
BB-BC (30) 4.690
MMO 4.5328
6. Conclusion
In this paper, the MMO has been successfully implemented to solve Optimal Reactive
Power Dispatch problem. The main advantages of the MMO are easily handling of non-linear
constraints. The proposed algorithm has been tested on the IEEE 30-bus system to minimize
the active power loss. The optimal setting of control variables are well within the limits. The
results were compared with the other heuristic methods and proposed MMO demonstrated its
effectiveness and robustness in minimizing the real power loss.
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