A particle swarm optimization for reactive power and voltage control considering voltage security assessment

doi:10.1109/59.898095

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A particle swarm optimization for reactive power and voltage control considering voltage security assessment

Journal Article•DOI•

A particle swarm optimization for reactive power and voltage control considering voltage security assessment

Hirotaka Yoshida, Kenichi Kawata, Yoshikazu Fukuyama, Shinichi Takayama, Yosuke Nakanishi - Show less +1 more

01 Nov 2000-IEEE Transactions on Power Systems (IEEE TRANSACTIONS ON POWER SYSTEMS)-Vol. 15, Iss: 4, pp 1232-1239

TL;DR: In this article, a particle swarm optimization (PSO) for reactive power and voltage control (volt/VAr control: VVC) considering voltage security assessment (VSA) is presented.

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Abstract: Summary form only given, as follows. This paper presents a particle swarm optimization (PSO) for reactive power and voltage control (volt/VAr control: VVC) considering voltage security assessment (VSA). VVC can be formulated as a mixed-integer nonlinear optimization problem (MINLP). The proposed method expands the original PSO to handle a MINLP and determines an online VVC strategy with continuous and discrete control variables such as automatic voltage regulator (AVR) operating values of generators, tap positions of on-load tap changer (OLTC) of transformers, and the number of reactive power compensation equipment. The method considers voltage security using a continuation power now and a contingency analysis technique. The feasibility of the proposed method is demonstrated and compared with reactive tabu search (RTS) and the enumeration method on practical power system models with promising results.

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Journal Article•DOI•

The particle swarm optimization algorithm: convergence analysis and parameter selection

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Ioan Cristian Trelea

31 Mar 2003-Information Processing Letters

TL;DR: The particle swarm optimization algorithm is analyzed using standard results from the dynamic system theory and graphical parameter selection guidelines are derived, resulting in results superior to previously published results.

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2,554 citations

Journal Article•DOI•

Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

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Y. del Valle¹, Ganesh K. Venayagamoorthy², Salman Mohagheghi¹, J.-C. Hernandez¹, Ronald G. Harley¹ - Show less +1 more•Institutions (2)

Georgia Institute of Technology¹, Missouri University of Science and Technology²

01 Apr 2008-IEEE Transactions on Evolutionary Computation

TL;DR: This paper presents a detailed overview of the basic concepts of PSO and its variants, and provides a comprehensive survey on the power system applications that have benefited from the powerful nature ofPSO as an optimization technique.

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Abstract: Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.

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2,147 citations

Cites background or methods from "A particle swarm optimization for r..."

...This function can be the total active power losses in the network [125], [163], [165]–[167] or the total reactive power losses...
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...It has been shown that PSO-based VVC is faster than the conventional enumeration method [163], [166] converges to a solu-...
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...to keep the voltage security of the power system [163]....
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...on-load tap changers (OLTC) of the transformers, the number of the reactive power compensation equipment and the number of the wind turbine generators [163], [164]....
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...which handles mixed-integer nonlinear optimization problems (MINLP) with ease and can be an alternative solution to the above problem [163]–[167]....
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Journal Article•DOI•

Particle swarm optimization to solving the economic dispatch considering the generator constraints

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Zwe-Lee Gaing

28 Jul 2003-IEEE Transactions on Power Systems

TL;DR: In this paper, a particle swarm optimization (PSO) method for solving the economic dispatch (ED) problem in power systems is proposed, and the experimental results show that the proposed PSO method was indeed capable of obtaining higher quality solutions efficiently in ED problems.

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Abstract: This paper proposes a particle swarm optimization (PSO) method for solving the economic dispatch (ED) problem in power systems. Many nonlinear characteristics of the generator, such as ramp rate limits, prohibited operating zone, and nonsmooth cost functions are considered using the proposed method in practical generator operation. The feasibility of the proposed method is demonstrated for three different systems, and it is compared with the GA method in terms of the solution quality and computation efficiency. The experimental results show that the proposed PSO method was indeed capable of obtaining higher quality solutions efficiently in ED problems.

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1,635 citations

Cites background or methods from "A particle swarm optimization for r..."

...Although the PSO method seems to be sensitive to the tuning of some weights or parameters, according to the experiences of many experiments, the following PSO and real-coded GA parameters can be used [10], [14], [ 18 ]....
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...Example 1: Six-Unit System: The system contains six thermal units, 26 buses, and 46 transmission lines [ 18 ]....
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...The feasibility of their method is compared with the reactive tabu system (RTS) and enumeration method on practical power system, and has shown promising results [ 18 ]....
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Journal Article•DOI•

A particle swarm optimization approach for optimum design of PID controller in AVR system

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Zwe-Lee Gaing

24 May 2004-IEEE Transactions on Energy Conversion

TL;DR: The proposed PSO method was indeed more efficient and robust in improving the step response of an AVR system and had superior features, including easy implementation, stable convergence characteristic, and good computational efficiency.

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Abstract: In this paper, a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters of an AVR system using the particle swarm optimization (PSO) algorithm is presented. This paper demonstrated in detail how to employ the PSO method to search efficiently the optimal PID controller parameters of an AVR system. The proposed approach had superior features, including easy implementation, stable convergence characteristic, and good computational efficiency. Fast tuning of optimum PID controller parameters yields high-quality solution. In order to assist estimating the performance of the proposed PSO-PID controller, a new time-domain performance criterion function was also defined. Compared with the genetic algorithm (GA), the proposed method was indeed more efficient and robust in improving the step response of an AVR system.

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1,485 citations

Cites background or methods from "A particle swarm optimization for r..."

...The reasonable transfer function of these components may be represented, respectively, as follows [ 16 ]....
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...The PSO technique can generate a high-quality solution within shorter calculation time and stable convergence characteristic than other stochastic methods [14]–[ 16 ]....
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...(1) B. Linearized Model of an AVR System [ 16 ]...
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...Their method is compared with the reactive tabu system (RTS) and enumeration method on practical power system, and has shown promising results [ 16 ]....
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Book•

Particle Swarm Optimization

[...]

Maurice Clerc

24 Feb 2006

TL;DR: This work focuses on the optimization of particle Swarm Optimization for TRIBES or co-operation of tribes with a focus on the dynamics of a swarm.

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Abstract: Foreword. Introduction. Part 1: Particle Swarm Optimization. Chapter 1. What is a difficult problem? Chapter 2. On a table corner. Chapter 3. First formulations. Chapter 4. Benchmark set. Chapter 5. Mistrusting chance. Chapter 6. First results. Chapter 7. Swarm: memory and influence graphs. Chapter 8. Distributions of proximity. Chapter 9. Optimal parameter settings. Chapter 10. Adaptations. Chapter 11. TRIBES or co-operation of tribes. Chapter 12. On the constraints. Chapter 13. Problems and applications. Chapter 14. Conclusion. Part 2: Outlines. Chapter 15. On parallelism. Chapter 16. Combinatorial problems. Chapter 17. Dynamics of a swarm. Chapter 18. Techniques and alternatives. Further Information. Bibliography. Index.

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1,293 citations

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References

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Proceedings Article•DOI•

A modified particle swarm optimizer

[...]

Yuhui Shi¹, Russell C. Eberhart¹•Institutions (1)

Indiana University – Purdue University Indianapolis¹

04 May 1998

TL;DR: A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.

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Abstract: Evolutionary computation techniques, genetic algorithms, evolutionary strategies and genetic programming are motivated by the evolution of nature. A population of individuals, which encode the problem solutions are manipulated according to the rule of survival of the fittest through "genetic" operations, such as mutation, crossover and reproduction. A best solution is evolved through the generations. In contrast to evolutionary computation techniques, Eberhart and Kennedy developed a different algorithm through simulating social behavior (R.C. Eberhart et al., 1996; R.C. Eberhart and J. Kennedy, 1996; J. Kennedy and R.C. Eberhart, 1995; J. Kennedy, 1997). As in other algorithms, a population of individuals exists. This algorithm is called particle swarm optimization (PSO) since it resembles a school of flying birds. In a particle swarm optimizer, instead of using genetic operators, these individuals are "evolved" by cooperation and competition among the individuals themselves through generations. Each particle adjusts its flying according to its own flying experience and its companions' flying experience. We introduce a new parameter, called inertia weight, into the original particle swarm optimizer. Simulations have been done to illustrate the significant and effective impact of this new parameter on the particle swarm optimizer.

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9,373 citations

Journal Article•DOI•

Generalized Benders decomposition

[...]

Arthur M. Geoffrion¹•Institutions (1)

University of California, Los Angeles¹

01 Oct 1972-Journal of Optimization Theory and Applications

TL;DR: In this paper, the extremal value of the linear program as a function of the parameterizing vector and the set of values of the parametric vector for which the program is feasible were derived using linear programming duality theory.

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Abstract: J. F. Benders devised a clever approach for exploiting the structure of mathematical programming problems withcomplicating variables (variables which, when temporarily fixed, render the remaining optimization problem considerably more tractable). For the class of problems specifically considered by Benders, fixing the values of the complicating variables reduces the given problem to an ordinary linear program, parameterized, of course, by the value of the complicating variables vector. The algorithm he proposed for finding the optimal value of this vector employs a cutting-plane approach for building up adequate representations of (i) the extremal value of the linear program as a function of the parameterizing vector and (ii) the set of values of the parameterizing vector for which the linear program is feasible. Linear programming duality theory was employed to derive the natural families ofcuts characterizing these representations, and the parameterized linear program itself is used to generate what are usuallydeepest cuts for building up the representations.

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2,133 citations

Book•

Voltage Stability of Electric Power Systems

[...]

Thierry Van Cutsem¹, Costas Vournas•Institutions (1)

University of Liège¹

31 Mar 1998

TL;DR: In this paper, the authors present a model for voltage security assessment based on loadability, sensitivity, and Bifurcation analysis, and present a set of criteria and methods for Voltage Security Assessment.

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Abstract: Foreword. Preface. Part I: Components and Phenomena. 1. Introduction. 2. Transmission System Aspects. 3. Generation Aspects. 4. Load Aspects. Part II: Instability Mechanisms and Analysis Methods. 5. Mathematical Background. 6. Modelling: System Perspective. 7. Loadability, Sensitivity and Bifurcation Analysis. 8. Instability Mechanisms and Countermeasures. 9. Criteria and Methods for Voltage Security Assessment. References. Index.

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1,350 citations

"A particle swarm optimization for r..." refers background or methods in this paper

...The calculation is called voltage contingency analysis [ 1 ]....
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...If the dynamic VSA is still required, the VSA used in the proposed method can be replaced with a dynamic VSA tool such as QSS described in [ 1 ]....
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...Since many voltage collapse accidents have been occurred over the last three decades [ 1 ], voltage security problems have been dominated and the consideration of the problem has been required in VVC problem [2], [3]....
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Proceedings Article•DOI•

Using selection to improve particle swarm optimization

[...]

P.J. Angeline

04 May 1998

TL;DR: A hybrid based on the particle swarm algorithm but with the addition of a standard selection mechanism from evolutionary computations is described that shows selection to provide an advantage for some (but not all) complex functions.

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Abstract: This paper describes a evolutionary optimization algorithm that is a hybrid based on the particle swarm algorithm but with the addition of a standard selection mechanism from evolutionary computations. A comparison is performed between the hybrid swarm and the ordinary particle swarm that shows selection to provide an advantage for some (but not all) complex functions.

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897 citations

Additional excerpts

...VQC問題の定式化 <2・1>定式化ここでは,給電所などにおける集中制御によって実現する平鴬時のVQCを対象として,以下のように定式化する。目的関数:対象系統の全系ロス最小化制約条件:① 母線電圧上下限制約 ②線路潮流上限制約 ③ 電圧信頼度なお,ここでは平常時のVQCを対象としているため,電圧信頼度はあくまでも制約として扱っており電圧信頼度を向上する様な制御としていない。電圧信頼度を保てない場合には電圧信頼度向上も目的関数に含める様な定式化が必…...
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Journal Article•DOI•

The Reactive Tabu Search

[...]

Roberto Battiti, Giampietro Tecchiolli

01 May 1994-Informs Journal on Computing

TL;DR: An algorithm for combinatorial optimization where an explicit check for the repetition of configurations is added to the basic scheme of Tabu search, and it is shown that the Hashing or Digital Tree techniques can be used in order to search for repetitions in a time that is approximately constant.

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Abstract: We propose an algorithm for combinatorial optimization where an explicit check for the repetition of configurations is added to the basic scheme of Tabu search. In our Tabu scheme the appropriate size of the list is learned in an automated way by reacting to the occurrence of cycles. In addition, if the search appears to be repeating an excessive number of solutions excessively often, then the search is diversified by making a number of random moves proportional to a moving average of the cycle length. The reactive scheme is compared to a “strict” Tabu scheme that forbids the repetition of configurations and to schemes with a fixed or randomly varying list size. From the implementation point of view we show that the Hashing or Digital Tree techniques can be used in order to search for repetitions in a time that is approximately constant. We present the results obtained for a series of computational tests on a benchmark function, on the 0-1 Knapsack Problem, and on the Quadratic Assignment Problem. INFORMS...

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865 citations

Additional excerpts

...…は,重要な特徴となる。従来,連続・離散型の混合変数のMINLPに対する効果的な手法は開発されていない。上記特徴にあるように,PSOはこの様なMINLPを容易に扱う事が可能であり,かつ計算時間が短い。このような特徴をいかし,今回,PSOのVQCへの適用を検討した。鳥の群れに対する従来からの研究により,鳥の群れ全体でえさをみつける行動を通して,情報を群れの間で共有しているという仮定が導かれた。この仮定がPSOの開発の基本となっている。また,人…...
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