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Publication Date: 2007
Scientia Iranica (23453605) 14(6)pp. 631-640
In this paper, reinforcement learning is used in order to model the reputation of buying and selling agents. Two important factors, quality and price, are considered in the proposed model. Each selling agent learns to evaluate the reputation of buying agents, based on their profits for that seller and uses this reputation to dedicate a discount for reputable buying agents. Also, selling agents learn to maximize their expected profits by using reinforcement learning to adjust the quality and price of the products, in order to satisfy the buying agents' preferences. In contrast, buying agents evaluate the reputation of selling agents based on two different factors: Reputation based on quality and price. Therefore, buying agents avoid interacting with disreputable selling agents. In addition, the fact that buying agents can have different priorities on the quality and price of their goods is taken into account. The proposed model has been implemented with Aglet and tested in a large-sized marketplace. The results show that selling/ buying agents that use the proposed algorithms in this paper obtain more satisfaction than the other selling/buying agents. © Sharif University of Technology, December 2007.
Publication Date: 2007
Journal of Theoretical and Applied Electronic Commerce Research (07181876) 2(1)pp. 1-17
In this paper, we propose a market model which Is based on reputation and reinforcement learning algorithms for buying and selling agents. Three important factors: quality, price and delivery-time are considered in the model. We take into account the fact that buying agents can have different priorities on quality, price and delivery-time of their goods and selling agents adjust their bids according to buying agents preferences. Also we have assumed that multiple selling agents may offer the same goods with different qualities, prices and delivery-times. In our model, selling agents learn to maximize their expected profits by using reinforcement learning to adjust product quality, price and delivery-time. Also each selling agent models the reputation of buying agents based on their profits for that seller and uses this reputation to consider discount for reputable buying agents. Buying agents learn to model the reputation of selling agents based on different features of goods: reputation on quality, reputation on price and reputation on delivery-time to avoid interaction with disreputable selling agents. The model has been implemented with Aglet and tested in a large-sized marketplace. The results show that selling/buying agents that model the reputation of buying/selling agents obtain more satisfaction rather than selling/buying agents who only use the reinforcement learning. © 2007 Universidad de Talca.
Jannati, M. ,
Vahidi, B. ,
Hosseinian s.h., S.H. ,
Baghaee h.r., Publication Date: 2008
pp. 203-207
Almost 70-90% of faults in extra high voltage (EHV) transmission lines in modern interconnected power systems are intrinsically transient. The necessity of rapid fault clearing results in fast developing of protection equipments. Morever, need for reliable supplying of loads, lead to improve in single phase auto-reclosing equipments. In this paper, a new method is proposed to reduce dead time of transmission lines. Leads to improve the performance and efficiency of single phase auto-reclosing. In the occurrence of permanent faults, the proposed yardstick is accurate and authentic to diagnose fault's type (transient or permanent). To validate accuracy and authenticity of proposed method, a 765 kV transmission system is simulated in Electromagnetic Transients Program software and results are presented. © 2008 IEEE.
Publication Date: 2008
pp. 307-311
A novel approach for fault detection in high voltage DC transmission systems using neural networks is presented. In the presented method, at first, Harmonics of voltage waveform in rectifier side are derived rapidly by using an adaptive linear neuron. Then, different types of faults including DC line fault, AC system Fault and Converter's faults are detected property using proposed criterion. In the under study voltage DC transmission systems system, rectifier and its controllers and required filters is modeled completely. A proposed criterion is tested on a high voltage DC system by computer simulation performed in MATLAB/Simulink environment. Simulation resultes demonstrates that the proposed approach can be used for online fault detection in high voltage DC systems. © 2008 IEEE.
Jannati, M. ,
Vahidi, B. ,
Hosseinian s.h., S.H. ,
Baghaee h.r., Publication Date: 2008
pp. 215-220
Reducing dead time of high voltage power transmission lines is one of the most important issues in power system protection. Besides, need for reducing the dead time is a matter of grave concern to increase voltage level of power transmission lines and insulation coordination. In this paper, different methods for decreasing the capacitive coupling and consequently reducing the dead time of power transmission lines are compared. This leads to faster quenching of secondary arc and limit the transient over voltage. Moreover, a novel hybrid approach is presented for reducing dead time of power transmission lines and faster quenching of secondary arc current. Simulations performed in electromagnetic transient program are performed for different cases. Simulation results show that dead time is reduced appropriately by proposed method.
Publication Date: 2008
Simulation (17413133) 84(12)pp. 601-610
In modern interconnected power systems, almost 70-90% of faults in high voltage Power Transmission Lines (PTLs) are intrinsically transient. The necessity of rapid fault clearing results in fast developing of protection equipments. Moreover, need for reliable supplying of loads, lead to improvements in single-phase autoreclosure (SPAR) equipments. An ADAptive LInear NEuron (ADALINE) is suitable for important applications such as protection of power systems and digital relays. In this paper, a novel simple adaptive SPAR algorithm is introduced. This algorithm is based on learning error function of an ADALINE. It can be distinguished by fault type (transient fault or a permanent fault), and if the fault is permanent, autoreclosure should be blocked. This leads to improve the performance and efficiency of SPAR. Electromagnetic transients program-based simulation results show that the autoreclosure scheme based on learning error function of ADALINE on a typical 400 kV circuit for various system and fault conditions improves the reliability of fault discrimination.© 2008 The Society for Modeling and Simulation International.
Baghaee h.r., ,
Jannati, M. ,
Vahidi, B. ,
Hosseinian s.h., S.H. ,
Rastegar h., Publication Date: 2008
pp. 209-214
Modern power systems are prone to widespread failures. With the increase in power demand, operation and planning of large interconnected power system are becoming more and more complex, so power system will become less secure. Operating environment, conventional planning and operating methods can leave power system exposed to instabilities. Voltage instability is one of the phenomena which have result in a major blackout. Moreover, with the fast development of restructuring, the problem of voltage stability has become a major concern in deregulated power systems. To maintain security of such systems, it is desirable to plan suitable measures to improve power system security and increase voltage stability margins. FACTS devices can regulate the active and reactive power control as well as adaptive to voltage-magnitude control simultaneously because of their flexibility and fast control characteristics. Placement of these devices in suitable location can lead to control in line flow and maintain bus voltages in desired level and so improve voltage stability margins. This paper presents a Genetic Algorithm (GA) based allocation algorithm for FACTS devices considering Cost function of FACTS devices and power system losses. Proposed algorithm is tested on IEEE 30 bus power system for optimal allocation of multi-type FACTS devices and results are presented.
Baghaee h.r., ,
Jannati, M. ,
Vahidi, B. ,
Hosseinian s.h., S.H. ,
Jazebi s., Publication Date: 2008
pp. 162-166
as power transfer increases, operation of power system become gradually more complex. Short circuit level increases and so power system will become less secure. Moreover, the problem of power system, security has become a mater of grave concern in the deregulated power industry. FACTS devices can control power flow because of their flexibility and fast control characteristics. Placement of these devices in suitable location can lead to control in line flow and maintain bus voltages in desired level and so improve power system security. This paper presents a novel algorithm for allocation of FACTS devices based on Genetic Algorithm (GA). Cost function of FACTS devices and power system losses are considered in this algorithm. Proposed algorithm is tested on IEEE 30 bus power system for optimal allocation of multi-type FACTS devices and results are presented © 2008 IEEE.
Publication Date: 2008
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (03029743) 5097pp. 681-692
In this paper, we propose a flexible parallel ant colony algorithm for classification rule discovery in the large databases. We call this algorithm Parallel Ant-Miner2. This model relies on the extension of real behavior of ants and data mining concepts. The artificial ants are firstly generated and separated into several groups. Each group is assigned a class label which is the consequent parts of the rules it should discover. Ants try to discover rules in parallel and then communicate with each other to update the pheromones in different paths. The communication methods help ants not to gather irrelevant terms of the rule. The parallel executions of ants reduce the speed of convergence and consequently make it possible to extract more new high quality rules by exploring all search space. Our experimental results show that the proposed model is more accurate than the other versions of Ant-Miner. © 2008 Springer-Verlag Berlin Heidelberg.
Publication Date: 2008
Energy Conversion and Management (01968904) 49(10)pp. 2629-2641
The introduction of liberalized electricity markets in many countries has resulted in more highly stressed power systems. On the other hand, operating points of a power system are acceptable in the feasible region, which is surrounded by the borders of different stabilities. Power system instability is critical for all participants of the electricity market. Determination of different stability margins can result in the optimum utilization of power system with minimum risk. This paper focuses on the small disturbance voltage stability, which is an important subset of the power system global stability. This kind of voltage stability is usually evaluated by static analysis tools such as continuation power flow, while it essentially has dynamic nature. Besides, a combination of linear and nonlinear analysis tools is required to correctly analyze it. In this paper, a hybrid evaluation method composed of static, dynamic, linear, and nonlinear analysis tools is proposed for this purpose. Effect of load scenario, generation pattern, branch and generator contingency on the small disturbance voltage stability are evaluated by the hybrid method. The test results are given for New England and IEEE68 bus test systems. © 2008 Elsevier Ltd. All rights reserved.
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