Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm

This paper addresses the attuned use of multiconverter flexible alternative current transmission systems (M-FACTS) devices and demand response (DR) to perform congestion management (CM) in the deregulated environment. The strong control capability of the M-FACTS offers a great potential in solving many of the problems facing electric utilities. Besides, DR is a novel procedure that can be an effective tool for reduction of congestion. A market clearing procedure is conducted based on maximizing social welfare (SW) and congestion as network constraint is paid by using concurrently the DR and M-FACTS. A multi-objective problem (MOP) based on the sum of the payments received by the generators for changing their output, the total payment received by DR participants to reduce their load and M-FACTS cost is systematized. For the solution of this problem a nonlinear time-varying evolution (NTVE) based multi-objective particle swarm optimization (MOPSO) style is formed. Fuzzy decision-making (FDM) and technique for order preference by similarity to ideal solution (TOPSIS) approaches are employed for finding the best compromise solution from the set of Pareto-solutions obtained through multi-objective particle swarm optimization-nonlinear time-varying evolution (MOPSO-NTVE). In a real power system, Azarbaijan regional power system of Iran, comparative analysis of the results obtained from the application of the DR & unified power flow controller (UPFC) and the DR & M-FACTS are presented.Keywords multi-converter flexible alternative current transmission systems (M-FACTS), demand response, fuzzy decision making, multi-objective particle swarm optimization-nonlinear time-varying evolution (MOPSO-NTVE)

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“…Besides, the social parameter c 2 starts with a low value c 2min and the nonlinearity increases to c 2max using Eqs. (20) to (22) [25,26].…”

Section: Mopso-ntve Implementation To Solve Optimization Proceduresmentioning

confidence: 99%

Attuned design of demand response program and M-FACTS for relieving congestion in a restructured market environment

Hashemi

Shayeghi

Hashemi³

2015

Front. Energy

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“…The fitness of the global best particle is re-evaluated based on the cost function either in (5) or in (6), before it is updated. If the fitness of the global best particle changes significantly, this indicates that a significant environmental change occurs.…”

Section: Change Detection Algorithmmentioning

confidence: 99%

“…Also, Chan et al [6] show that it can effectively adapt optimal structures and parameters of time-varying systems, where data is newly captured. Further to enhance the adaptive capability, the mechanism of sub-swarms [36] is employed.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Short-Term Traffic Variables Using Intelligent Swarm-Based Neural Networks

Chan

Dillon

Singh

2013

IEEE Trans. Contr. Syst. Technol.

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This brief presents an innovative algorithm integrated with particle swarm optimization and artificial neural networks to develop short-term traffic flow predictors, which are intended to provide traffic flow forecasting information for traffic management in order to reduce traffic congestion and improve mobility of transportation. The proposed algorithm aims to address the issues of development of short-term traffic flow predictors which have not been addressed fully in the current literature namely that: 1) strongly non-linear characteristics are unavoidable in traffic flow data; 2) memory space for implementation of short-term traffic flow predictors is limited; 3) specification of model structures for short-term traffic flow predictors which do not involve trial and error methods based on human expertise; and 4) adaptation to newly-captured, traffic flow data is required. The proposed algorithm was applied to forecast traffic flow conditions on a section of freeway in Western Australia, whose traffic flow information is newly-captured. These results clearly demonstrate the effectiveness of using the proposed algorithm for real-time traffic flow forecasting.Index Terms-Adaptive neural network (NN), evolutionary algorithm, particle swarm optimization, time-varying modeling, traffic flow forecasting, traffic management.

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“…The algorithm imitates the social behavior of bird flocking or fish schooling to find the global best solution. Due to the simple concept, having a few parameters and being easy to implement, PSO has received much more attention to solve real-world optimization problems [2][3][4][5][6] in recent years. Nevertheless, PSO may easily get trapped in local optima when solving complex multimodal problems [7].…”

Section: Introductionmentioning

confidence: 99%

Fusion Global-Local-Topology Particle Swarm Optimization for Global Optimization Problems

Beheshti

Shamsuddin

Sulaiman

2014

Mathematical Problems in Engineering

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In recent years, particle swarm optimization (PSO) has been extensively applied in various optimization problems because of its structural and implementation simplicity. However, the PSO can sometimes find local optima or exhibit slow convergence speed when solving complex multimodal problems. To address these issues, an improved PSO scheme called fusion global-local-topology particle swarm optimization (FGLT-PSO) is proposed in this study. The algorithm employs both global and local topologies in PSO to jump out of the local optima. FGLT-PSO is evaluated using twenty (20) unimodal and multimodal nonlinear benchmark functions and its performance is compared with several well-known PSO algorithms. The experimental results showed that the proposed method improves the performance of PSO algorithm in terms of solution accuracy and convergence speed.

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Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm

Cited by 16 publications

References 52 publications

Attuned design of demand response program and M-FACTS for relieving congestion in a restructured market environment

Attuned design of demand response program and M-FACTS for relieving congestion in a restructured market environment

Prediction of Short-Term Traffic Variables Using Intelligent Swarm-Based Neural Networks

Fusion Global-Local-Topology Particle Swarm Optimization for Global Optimization Problems

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