This paper focuses on the engineering and computer science aspects of developments, applications, and resources related to particle swarm optimization. Developments in the particle swarm algorithm since its origin in 1995 are reviewed. Included are brief discussions of constriction factors, inertia weights, and tracking dynamic systems. Applications, both those already developed, and promising future application areas, are reviewed. Finally, resources related to particle swarm optimization are listed, including books, Web sites, and software. A particle swarm optimization bibliography is at the end of the paper.

Particle swarm optimization: developments, applications and resources

This paper introduces a novel parameter automation strategy for the particle swarm algorithm and two further extensions to improve its performance after a predefined number of generations. Initially, to efficiently control the local search and convergence to the global optimum solution, time-varying acceleration coefficients (TVAC) are introduced in addition to the time-varying inertia weight factor in particle swarm optimization (PSO). From the basis of TVAC, two new strategies are discussed to improve the performance of the PSO. First, the concept of "mutation" is introduced to the particle swarm optimization along with TVAC (MPSO-TVAC), by adding a small perturbation to a randomly selected modulus of the velocity vector of a random particle by predefined probability. Second, we introduce a novel particle swarm concept "self-organizing hierarchical particle swarm optimizer with TVAC (HPSO-TVAC)". Under this method, only the "social" part and the "cognitive" part of the particle swarm strategy are considered to estimate the new velocity of each particle and particles are reinitialized whenever they are stagnated in the search space. In addition, to overcome the difficulties of selecting an appropriate mutation step size for different problems, a time-varying mutation step size was introduced. Further, for most of the benchmarks, mutation probability is found to be insensitive to the performance of MPSO-TVAC method. On the other hand, the effect of reinitialization velocity on the performance of HPSO-TVAC method is also observed. Time-varying reinitialization step size is found to be an efficient parameter optimization strategy for HPSO-TVAC method. The HPSO-TVAC strategy outperformed all the methods considered in this investigation for most of the functions. Furthermore, it has also been observed that both the MPSO and HPSO strategies perform poorly when the acceleration coefficients are fixed at two.

Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients

The particle swarm optimization algorithm: convergence analysis and parameter selection

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

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.

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

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.

/pdf/a-particle-swarm-optimization-for-reactive-power-and-voltage-1g427relsp.pdf

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

This paper presents a particle swarm optimization for reactive power and voltage control considering voltage stability. The proposed method determines a control strategy with continuous and discrete control variables such as AVR operating values, OLTC tap positions, and the amount of reactive power compensation equipment. The method also considers voltage stability using a continuation power flow technique. The feasibility of the proposed method is demonstrated on model power systems with promising results.

A particle swarm optimization for reactive power and voltage control considering voltage stability

A Particle Swarm Optimization for Reactive Power and Voltage Control Considering Voltage Security Assessment

Prony analysis method can detect oscillation frequency, damping, phase and amplitude directly from time series data. Several applications based on the method have been developed in power systems, but it is said that it gives less accurate results for noisy data. In this paper, a power swing mode detection method is presented which is suitable for noisy measured data in power systems. First, the analysis parameters such as sampling interval, number of data and number of orders are investigated. For detection of low frequency inter-area modes, longer sampling interval (0.2 to 0.4s) is preferable. The importance of each detected mode can be evaluated by an index based on integration of each waveform. And for data containing large noise, the accuracy of analysis can be improved by applying a low-pass filter. By modifying amplitudes and phases based on the filter characteristics, the original waveform eliminating noise can be estimated precisely. Effectiveness of the method were verified through examinations using active power and bus voltage data actually measured in a power system.

Kenichi Kawata

Papers

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

A particle swarm optimization for reactive power and voltage control considering voltage stability

A Particle Swarm Optimization for Reactive Power and Voltage Control Considering Voltage Security Assessment

Power Swing Mode Detection from Power System Measured Data Based on Prony Analysis

Combined Cycle Plant Model for Power System Dynamic Simulation Study