A Multi-Objective HPSO Algorithm Approach for Optimally Location of UPFC in Deregulated Power Systems

Taher, Seyed Abbas; Tabei, Seyed Mohammad Hadi

doi:10.3844/ajassp.2008.835.843

Cited by 6 publications

(7 citation statements)

References 21 publications

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“…Also, on placing of UPFC between buses 5 and 6, the line losses get decreased from 60.21 MW to 58.02 MW for the critical case. With regard to IEEE 14 bus system, BF obtains 2.33 % loss reduction compared to the value reported in [23] for the same test system. This shows the effectiveness of the proposed approach in minimizing the transmission line losses and voltage profile improvement simultaneously.…”

Section: Resultscontrasting

confidence: 47%

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Application of UPFC for enhancement of voltage profile and minimization of losses using Fast Voltage Stability Index (FVSI)

Kumar¹,

Renuga²

2012

Archives of Electrical Engineering

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Transmission line loss minimization in a power system is an important research issue and it can be achieved by means of reactive power compensation. The unscheduled increment of load in a power system has driven the system to experience stressed conditions. This phenomenon has also led to voltage profile depreciation below the acceptable secure limit. The significance and use of Flexible AC Transmission System (FACTS) devices and capacitor placement is in order to alleviate the voltage profile decay problem. The optimal value of compensating devices requires proper optimization technique, able to search the optimal solution with less computational burden. This paper presents a technique to provide simultaneous or individual controls of basic system parameter like transmission voltage, impedance and phase angle, thereby controlling the transmitted power using Unified Power Flow Controller (UPFC) based on Bacterial Foraging (BF) algorithm. Voltage stability level of the system is defined on the Fast Voltage Stability Index (FVSI) of the lines. The IEEE 14-bus system is used as the test system to demonstrate the applicability and efficiency of the proposed system. The test result showed that the location of UPFC improves the voltage profile and also minimize the real power loss.

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Section: Resultscontrasting

confidence: 47%

“…al [22] proposed a technique to voltage profile improvement based on Artificial Immune System (AIS) using UPFC. Seyed Abbas Taher and Seyed Mohammad Hadi Tabi [23] proposed an application of HPSO to solve the optimal location of UPFC problems in restructured power systems for considering system loadability and the overall cost function.…”

Section: Introductionmentioning

confidence: 99%

Application of UPFC for enhancement of voltage profile and minimization of losses using Fast Voltage Stability Index (FVSI)

Kumar¹,

Renuga²

2012

Archives of Electrical Engineering

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“…Among the three evolutionary operators, the mutation operators are the most commonly applied evolutionary operators in PSO. The purpose of applying mutation to PSO is to increase the diversity of the population and the ability to have the PSO to escape the local minima [19][20][21][22][23][24][25][26][27][28] . HPSO uses the mechanism of PSO and a natural selection mechanism utilizing genetic algorithm.…”

Section: Pso and Hpso Algorithm Definitionmentioning

confidence: 99%

“…The structure of system with PI controller is shown in Fig. 2 [26,29] . The area control error (ACE) for the i th area is defined as: …”

Section: Controller Design Using Hpso Algorithmmentioning

confidence: 99%

Optimal Decentralized Load Frequency Control Using HPSO Algorithms in Deregulated Power Systems

Taher¹,

Hematti²,

Abdolalipour³

et al. 2008

American J. of Applied Sciences

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Load Frequency Control (LFC) is one the most important issues in electrical power system design/operation and is becoming much more significant recently with increasing size, changing structure and complexity in interconnected power systems. In practice LFC systems use simple Proportional-Integral (PI) or Integral (I) controllers. However, since the PI or I control parameters are usually tuned based on classical or trial-and-error approaches, they are incapable of obtaining good dynamic performance for various load changes scenarios in multi-area power system. For this reason, in this study the PI and I control parameters are tuned based on Hybrid Particle Swarm Optimization (HPSO) algorithm method for LFC control in two-area power system. Because HPSO is an optimization method, therefore, in the uncertainty area of controller parameters, finds the best parameters for controller and obtained controller is an optimal controller. A two-area power system example is given to illustrate proposed methods. To show effectiveness of proposed method and compare the performance of optimized PI and I type controllers, several time-domain simulation for various load changes scenarios are presented. Simulation results emphasis on the better performance of optimized PI controller in compared to optimized I controller in LFC.

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“…The reason for the optimality of many solutions is that no one can be considered to be better than any other with respect to all objective functions. These optimal solutions are known as Pareto-optimal solutions (Abido, 2003;2006;Bueno and Oliveira, 2010;Mendoza et al, 2006;Taher, and Tabei, 2008). A general multi-objective optimization problem consists of a number of objectives to be optimized simultaneously and is associated with a number of equality and inequality constraints.…”

Section: Concepts Of Multi-objective Optimizationmentioning

confidence: 99%

Strength Pareto Evolutionary Algorithm based Multi-Objective Optimization for Shortest Path Routing Problem in Computer Networks

Potti¹

2011

Journal of Computer Science

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Problem statement:A new multi-objective approach, Strength Pareto Evolutionary Algorithm (SPEA), is presented in this paper to solve the shortest path routing problem. The routing problem is formulated as a multi-objective mathematical programming problem which attempts to minimize both cost and delay objectives simultaneously. Approach: SPEA handles the shortest path routing problem as a true multi-objective optimization problem with competing and noncommensurable objectives. Results: SPEA combines several features of previous multi-objective evolutionary algorithms in a unique manner. SPEA stores nondominated solutions externally in another continuously-updated population and uses a hierarchical clustering algorithm to provide the decision maker with a manageable pareto-optimal set. SPEA is applied to a 20 node network as well as to large size networks ranging from 50-200 nodes. Conclusion: The results demonstrate the capabilities of the proposed approach to generate true and well distributed pareto-optimal nondominated solutions.

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A Multi-Objective HPSO Algorithm Approach for Optimally Location of UPFC in Deregulated Power Systems

Cited by 6 publications

References 21 publications

Application of UPFC for enhancement of voltage profile and minimization of losses using Fast Voltage Stability Index (FVSI)

Application of UPFC for enhancement of voltage profile and minimization of losses using Fast Voltage Stability Index (FVSI)

Optimal Decentralized Load Frequency Control Using HPSO Algorithms in Deregulated Power Systems

Strength Pareto Evolutionary Algorithm based Multi-Objective Optimization for Shortest Path Routing Problem in Computer Networks

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