Pub Date : 2007-11-01DOI: 10.1109/ISAP.2007.4441618
Huy Huynh Nguyen, G. Baxter, L. Reznik
This paper presents an investigation and a comparative study of four different approaches namely ANSI/IEEE standard methods, Adaptive Neuro-Fuzzy Inference System (ANFIS), Multilayer Feedforward Neural Network (MFNN) and Elman Recurrent Neural Network (ERNN) to modeling and prediction of the top-oil temperature for the 8 MVA Oil Air (OA)-cooled and 27 MVA Forced Air (FA)-cooled class of power transformers. A comparison of the proposed techniques is presented for predicting top-oil temperature based on the historical data measured over a 35 day period for the first transformer and 4.5 days for the second transformer with either a half or a quarter hour sampling time. Comparison results indicate that hybrid neuro-fuzzy network is the best candidate for the analysis and predicting of power transformer top-oil temperature. The ANFIS demonstrated the paramount performance in temperature prediction in terms of Root Mean Square Error (RMSE) and peaks of error.
{"title":"Soft Computing Techniques to Model the Top-oil Temperature of Power Transformers","authors":"Huy Huynh Nguyen, G. Baxter, L. Reznik","doi":"10.1109/ISAP.2007.4441618","DOIUrl":"https://doi.org/10.1109/ISAP.2007.4441618","url":null,"abstract":"This paper presents an investigation and a comparative study of four different approaches namely ANSI/IEEE standard methods, Adaptive Neuro-Fuzzy Inference System (ANFIS), Multilayer Feedforward Neural Network (MFNN) and Elman Recurrent Neural Network (ERNN) to modeling and prediction of the top-oil temperature for the 8 MVA Oil Air (OA)-cooled and 27 MVA Forced Air (FA)-cooled class of power transformers. A comparison of the proposed techniques is presented for predicting top-oil temperature based on the historical data measured over a 35 day period for the first transformer and 4.5 days for the second transformer with either a half or a quarter hour sampling time. Comparison results indicate that hybrid neuro-fuzzy network is the best candidate for the analysis and predicting of power transformer top-oil temperature. The ANFIS demonstrated the paramount performance in temperature prediction in terms of Root Mean Square Error (RMSE) and peaks of error.","PeriodicalId":320068,"journal":{"name":"2007 International Conference on Intelligent Systems Applications to Power Systems","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133611298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-11-01DOI: 10.1109/ISAP.2007.4441632
M. Farsangi, H. Nezamabadi-pour, K.Y. Lee
In this paper, Guaranteed Convergence Particle Swarm Optimization (GCPSO) Algorithm is used for VAr planning with the Static Var Compensators (SVC) in a large-scale power system. To enhance voltage stability, the planning problem is formulated as a multiobjective optimization problem for maximizing fuzzy performance indices. The multi-objective VAr planning problem is solved by the fuzzy GCPSO and the results are compared with those obtained by the Particle Swarm Optimization (PSO) and Genetic Algorithm
{"title":"Implementation of GCPSO for Multi-objective VAr Planning with SVC and Its Comparison with GA and PSO","authors":"M. Farsangi, H. Nezamabadi-pour, K.Y. Lee","doi":"10.1109/ISAP.2007.4441632","DOIUrl":"https://doi.org/10.1109/ISAP.2007.4441632","url":null,"abstract":"In this paper, Guaranteed Convergence Particle Swarm Optimization (GCPSO) Algorithm is used for VAr planning with the Static Var Compensators (SVC) in a large-scale power system. To enhance voltage stability, the planning problem is formulated as a multiobjective optimization problem for maximizing fuzzy performance indices. The multi-objective VAr planning problem is solved by the fuzzy GCPSO and the results are compared with those obtained by the Particle Swarm Optimization (PSO) and Genetic Algorithm","PeriodicalId":320068,"journal":{"name":"2007 International Conference on Intelligent Systems Applications to Power Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129113700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-11-01DOI: 10.1109/ISAP.2007.4441606
A. C. Tellidou, A. Bakirtzis
In this paper agent-based simulation is employed to study the energy market performance and, particularly, the exercise of monopoly power. The energy market is formulated as a stochastic game, where each stage game corresponds to an hourly energy auction. Each hourly energy auction is cleared using Locational Marginal Pricing. Generators are modeled as adaptive agents capable of learning through the interaction with their environment, following a Reinforcement Learning algorithm. The SA-Q-learning algorithm, a modified version of the popular Q-Learning, is used. Test results on a two-node power system with two competing generator-agents, demonstrate the exercise of monopoly power.
{"title":"Agent-Based Analysis of Monopoly Power in Electricity Markets","authors":"A. C. Tellidou, A. Bakirtzis","doi":"10.1109/ISAP.2007.4441606","DOIUrl":"https://doi.org/10.1109/ISAP.2007.4441606","url":null,"abstract":"In this paper agent-based simulation is employed to study the energy market performance and, particularly, the exercise of monopoly power. The energy market is formulated as a stochastic game, where each stage game corresponds to an hourly energy auction. Each hourly energy auction is cleared using Locational Marginal Pricing. Generators are modeled as adaptive agents capable of learning through the interaction with their environment, following a Reinforcement Learning algorithm. The SA-Q-learning algorithm, a modified version of the popular Q-Learning, is used. Test results on a two-node power system with two competing generator-agents, demonstrate the exercise of monopoly power.","PeriodicalId":320068,"journal":{"name":"2007 International Conference on Intelligent Systems Applications to Power Systems","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123912543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-11-01DOI: 10.1109/ISAP.2007.4441587
Liang Min, Pei Zhang
Our past research proposed to apply Comulants and Gram-Charlier expansion method to perform probabilistic load flow studies with consideration of generation and load uncertainties. This paper proposed a new method to improve the previous PLF computation method in order to model the network topology uncertainties. This innovative method uses distribution factor concept to model the impact of network uncertainties as a linear function of power injections. Maintaining the linear relationship between line flows and power injections enables applying Cumulants and Gram-Charlier expansion method to compute probabilistic distribution functions of transmission line flows. The proposed method is examined using IEEE 30-bus test system. Numerical comparison with Monte Carlo simulation method is also presented in this paper. Study results indicate that the proposed method has significantly reduced the computational efforts while maintaining a high degree of accuracy.
{"title":"A Probabilistic Load Flow with Consideration of Network Topology Uncertainties","authors":"Liang Min, Pei Zhang","doi":"10.1109/ISAP.2007.4441587","DOIUrl":"https://doi.org/10.1109/ISAP.2007.4441587","url":null,"abstract":"Our past research proposed to apply Comulants and Gram-Charlier expansion method to perform probabilistic load flow studies with consideration of generation and load uncertainties. This paper proposed a new method to improve the previous PLF computation method in order to model the network topology uncertainties. This innovative method uses distribution factor concept to model the impact of network uncertainties as a linear function of power injections. Maintaining the linear relationship between line flows and power injections enables applying Cumulants and Gram-Charlier expansion method to compute probabilistic distribution functions of transmission line flows. The proposed method is examined using IEEE 30-bus test system. Numerical comparison with Monte Carlo simulation method is also presented in this paper. Study results indicate that the proposed method has significantly reduced the computational efforts while maintaining a high degree of accuracy.","PeriodicalId":320068,"journal":{"name":"2007 International Conference on Intelligent Systems Applications to Power Systems","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117350955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-11-01DOI: 10.1109/ISAP.2007.4441671
A. Dimeas, N. Hatziargyriou
The transition of traditional power systems into the flexible smart grids is under way. This paper presents a new interesting concept where Microgrids and other production or consumption units form a Virtual Power Plant. The main goal is to present the advantages of using agents for Virtual Power Plant control. More specifically this paper through examples and case studies presents how the local intelligence and the social ability of the agents may provide solutions in the optimal and effective control of a Virtual Power Plant.
{"title":"Agent based control of Virtual Power Plants","authors":"A. Dimeas, N. Hatziargyriou","doi":"10.1109/ISAP.2007.4441671","DOIUrl":"https://doi.org/10.1109/ISAP.2007.4441671","url":null,"abstract":"The transition of traditional power systems into the flexible smart grids is under way. This paper presents a new interesting concept where Microgrids and other production or consumption units form a Virtual Power Plant. The main goal is to present the advantages of using agents for Virtual Power Plant control. More specifically this paper through examples and case studies presents how the local intelligence and the social ability of the agents may provide solutions in the optimal and effective control of a Virtual Power Plant.","PeriodicalId":320068,"journal":{"name":"2007 International Conference on Intelligent Systems Applications to Power Systems","volume":"10 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129748131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-11-01DOI: 10.1109/ISAP.2007.4441621
Lingxue Lin, Yao Zhang, Q. Zhong, F. Wen
The behaviors of transient phenomena in HVDC systems, including DC line faults, commutation failures caused by AC faults and missing firing are difficult to be identified automatically by the control systems, while the effective protection control for commutation failures depends on the rapid and correct identification of such faults. This paper proposes a method to identify different causes leading to commutation failures based on the wavelet transform. By using the technique of wavelet multi-resolution analysis (MRA), the transient signals generated by the faults are decomposed into different resolution levels. The features of each fault are extracted. Two auxiliary parameters are defined as the criteria for the identification, based on which four thresholds are set to distinguish different faults. Simulation results indicate that the proposed approach could make a definite identification of commutation failures from DC line faults and normal operations. Furthermore, the discriminations between AC short circuit faults and missing firing faults, which cause commutation failures, also could be obtained.
{"title":"Identification of Commutation Failures in HVDC Systems Based on Wavelet Transform","authors":"Lingxue Lin, Yao Zhang, Q. Zhong, F. Wen","doi":"10.1109/ISAP.2007.4441621","DOIUrl":"https://doi.org/10.1109/ISAP.2007.4441621","url":null,"abstract":"The behaviors of transient phenomena in HVDC systems, including DC line faults, commutation failures caused by AC faults and missing firing are difficult to be identified automatically by the control systems, while the effective protection control for commutation failures depends on the rapid and correct identification of such faults. This paper proposes a method to identify different causes leading to commutation failures based on the wavelet transform. By using the technique of wavelet multi-resolution analysis (MRA), the transient signals generated by the faults are decomposed into different resolution levels. The features of each fault are extracted. Two auxiliary parameters are defined as the criteria for the identification, based on which four thresholds are set to distinguish different faults. Simulation results indicate that the proposed approach could make a definite identification of commutation failures from DC line faults and normal operations. Furthermore, the discriminations between AC short circuit faults and missing firing faults, which cause commutation failures, also could be obtained.","PeriodicalId":320068,"journal":{"name":"2007 International Conference on Intelligent Systems Applications to Power Systems","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123116637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-11-01DOI: 10.1109/ISAP.2007.4441614
H. Mori, Y. Iimura
This paper proposes a hybrid meta-heuristic method of parallel tabu search (PTS) and ordinal optimization (OO) for transmission network expansion planning in power systems. It determines the optimal structure that keeps the balance between generations and loads. The formulation is expressed as a combinatorial optimization problem that is very hard to solve. PTS is one of meta-heuristics that is useful for solving a combinatorial optimization. To speed up computational time of PTS, OO is used to reduce the number of solution candidates in a probabilistic way. The proposed method with OO-TS is successfully applied to a sample system.
{"title":"Transmission Network Expansion Planning with a Hybrid Meta-heuristic Method of Parallel Tabu Search and Ordinal Optimization","authors":"H. Mori, Y. Iimura","doi":"10.1109/ISAP.2007.4441614","DOIUrl":"https://doi.org/10.1109/ISAP.2007.4441614","url":null,"abstract":"This paper proposes a hybrid meta-heuristic method of parallel tabu search (PTS) and ordinal optimization (OO) for transmission network expansion planning in power systems. It determines the optimal structure that keeps the balance between generations and loads. The formulation is expressed as a combinatorial optimization problem that is very hard to solve. PTS is one of meta-heuristics that is useful for solving a combinatorial optimization. To speed up computational time of PTS, OO is used to reduce the number of solution candidates in a probabilistic way. The proposed method with OO-TS is successfully applied to a sample system.","PeriodicalId":320068,"journal":{"name":"2007 International Conference on Intelligent Systems Applications to Power Systems","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132834784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-11-01DOI: 10.1109/ISAP.2007.4441660
D. Srinivasan, F. Yong, A. Liew
Evolutionary techniques have capabilities of efficient search space exploration with population models corresponding to the problem. Their ability to capture the non linear dependencies among the system variables has invited economic analysts towards their use in the field of financial time series prediction. Although simple neural networks with sufficient number neuron units in the hidden layer are capable of following dynamics of any deterministic system, the weight search space becomes too complex to be searched using a simple back propagation based training algorithm. This paper presents and evaluates two alternative methods for finding the optimum weights of a neural network to capture the chaotic dynamics of electricity price data. The first method uses evolutionary algorithm to evolve a neural network, and the second method uses particle swarm optimization for NN training. The global search capabilities of these evolutionary methods is used for finding the optimum neural network for forecasting electricity price from the California Power Exchange.
{"title":"Electricity Price Forecasting Using Evolved Neural Networks","authors":"D. Srinivasan, F. Yong, A. Liew","doi":"10.1109/ISAP.2007.4441660","DOIUrl":"https://doi.org/10.1109/ISAP.2007.4441660","url":null,"abstract":"Evolutionary techniques have capabilities of efficient search space exploration with population models corresponding to the problem. Their ability to capture the non linear dependencies among the system variables has invited economic analysts towards their use in the field of financial time series prediction. Although simple neural networks with sufficient number neuron units in the hidden layer are capable of following dynamics of any deterministic system, the weight search space becomes too complex to be searched using a simple back propagation based training algorithm. This paper presents and evaluates two alternative methods for finding the optimum weights of a neural network to capture the chaotic dynamics of electricity price data. The first method uses evolutionary algorithm to evolve a neural network, and the second method uses particle swarm optimization for NN training. The global search capabilities of these evolutionary methods is used for finding the optimum neural network for forecasting electricity price from the California Power Exchange.","PeriodicalId":320068,"journal":{"name":"2007 International Conference on Intelligent Systems Applications to Power Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130487303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-11-01DOI: 10.1109/ISAP.2007.4441588
B. Fardanesh, Pei Zhang, Liang Min, L. Hopkins, B. Fardanesh
EPRI has developed a probabilistic risk/reliability assessment (PRA) method under power delivery reliability initiative, which has been successfully implemented by various energy companies in planning studies of growing complexity. Unlike the traditional deterministic contingency analysis, PRA combines a probabilistic measure of the likelihood of undesirable events with a measure of the consequence of the events (that is, the impact) into a single reliability index -probabilistic reliability index (PRI). EPRI internally developed the PRI program that uses contingency analysis results as well as the transmission facility outage information as input to compute and graphically display the reliability indices. This paper presents an application of PRI program to study the transmission network of New York Power Authority (NYPA). This work has demonstrated that the PRA method significantly improves the ability of conducting effective transmission operational planning. The paper represents the collaborative effort between EPRI and NYPA
{"title":"Utility Experience Performing Probabilistic Risk Assessment for Operational Planning","authors":"B. Fardanesh, Pei Zhang, Liang Min, L. Hopkins, B. Fardanesh","doi":"10.1109/ISAP.2007.4441588","DOIUrl":"https://doi.org/10.1109/ISAP.2007.4441588","url":null,"abstract":"EPRI has developed a probabilistic risk/reliability assessment (PRA) method under power delivery reliability initiative, which has been successfully implemented by various energy companies in planning studies of growing complexity. Unlike the traditional deterministic contingency analysis, PRA combines a probabilistic measure of the likelihood of undesirable events with a measure of the consequence of the events (that is, the impact) into a single reliability index -probabilistic reliability index (PRI). EPRI internally developed the PRI program that uses contingency analysis results as well as the transmission facility outage information as input to compute and graphically display the reliability indices. This paper presents an application of PRI program to study the transmission network of New York Power Authority (NYPA). This work has demonstrated that the PRA method significantly improves the ability of conducting effective transmission operational planning. The paper represents the collaborative effort between EPRI and NYPA","PeriodicalId":320068,"journal":{"name":"2007 International Conference on Intelligent Systems Applications to Power Systems","volume":"611 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133601147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-11-01DOI: 10.1109/ISAP.2007.4441653
Jong-Yul Kim, hee-myung jeong, Hwa-Seok Lee, Juneho Park
The optimal power flow (OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, the OPF problem has been intensively studied and widely used in power system operation and planning. To solve OPF problem, a number of conventional optimization techniques have been applied. In the past few decades, many heuristic optimization methods have been developed, such as genetic algorithm (GA), evolutionary programming (EP), evolution strategies (ES), and particle swarm optimization(PSO). Especially, PSO algorithm is a newly proposed population based heuristic optimization algorithm which was inspired by the social behaviors of animals. However, population based heuristic optimization methods require higher computing time to find optimal point. This shortcoming is overcome by a straightforward parallelization of PSO algorithm. The developed parallel PSO algorithm is implemented on a PC- cluster system with 6 Intel Pentium IV 2GHz processors. The proposed approach has been tested on the IEEE 30-bus system. The results showed that computing time of parallelized PSO algorithm can be reduced by parallel processing without losing the quality of solution.
最优潮流(OPF)问题是由Carpentier于1962年提出的一个网络约束经济调度问题。自此，OPF问题在电力系统运行规划中得到了广泛的研究和应用。为了解决OPF问题，采用了许多传统的优化技术。在过去的几十年里，许多启发式优化方法被开发出来，如遗传算法(GA)、进化规划(EP)、进化策略(ES)和粒子群优化(PSO)。其中，粒子群优化算法是受动物社会行为启发而提出的一种基于种群的启发式优化算法。然而，基于种群的启发式优化方法需要较高的计算时间来寻找最优点。这种缺点被一种直接并行化的粒子群算法所克服。所开发的并行粒子群算法在6个Intel Pentium IV 2GHz处理器的PC集群系统上实现。该方法已在IEEE 30总线系统上进行了测试。结果表明，并行PSO算法通过并行处理可以在不影响解质量的前提下减少计算时间。
{"title":"PC Cluster based Parallel PSO Algorithm for Optimal Power Flow","authors":"Jong-Yul Kim, hee-myung jeong, Hwa-Seok Lee, Juneho Park","doi":"10.1109/ISAP.2007.4441653","DOIUrl":"https://doi.org/10.1109/ISAP.2007.4441653","url":null,"abstract":"The optimal power flow (OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, the OPF problem has been intensively studied and widely used in power system operation and planning. To solve OPF problem, a number of conventional optimization techniques have been applied. In the past few decades, many heuristic optimization methods have been developed, such as genetic algorithm (GA), evolutionary programming (EP), evolution strategies (ES), and particle swarm optimization(PSO). Especially, PSO algorithm is a newly proposed population based heuristic optimization algorithm which was inspired by the social behaviors of animals. However, population based heuristic optimization methods require higher computing time to find optimal point. This shortcoming is overcome by a straightforward parallelization of PSO algorithm. The developed parallel PSO algorithm is implemented on a PC- cluster system with 6 Intel Pentium IV 2GHz processors. The proposed approach has been tested on the IEEE 30-bus system. The results showed that computing time of parallelized PSO algorithm can be reduced by parallel processing without losing the quality of solution.","PeriodicalId":320068,"journal":{"name":"2007 International Conference on Intelligent Systems Applications to Power Systems","volume":"69 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114131097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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