Applications of Particle Swarm Optimization Algorithm to Solving the Economic Load Dispatch of Units in Power Systems with Valve-Point Effects

Shahinzadeh, Hossein; Nasr-Azadani, Sayed Mohsen; Jannesari, Nazereh

doi:10.11591/ijece.v4i6.6720

Cited by 29 publications

(25 citation statements)

References 16 publications

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“…Where the fitness function depends on the controller of the system. In this paper, a multi-objective fitness function is utilized to find the optimal solution with a minimum velocity error based on the minimum Integral of Squared Error (ISE) criterion and minimum overshoot (M o ) criterion as follow [9][10][11][12]:…”

Section: Particle Swarm Optimization Techniquementioning

confidence: 99%

Motion control of linear induction motor based on optimal recurrent wavelet neural network-PID controller

Saleh

Obaid

Obed

2018

IJET

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This paper contains a proposed controller based on optimal recurrent wavelet neural network (RWNN) with PID controller to control the velocity and hence the stator current as well as the developed thrust of three phase linear induction motor (LIM) which consider the end effects. A vector control represented by indirect field oriented control (IFOC) technique is appointed to attain velocity and flux control for different loading conditions. Moreover, a voltage source inverter based on space vector pulse width modulation (SVPWM) is utilized to give the required stator voltage of LIM. A well-known particle swarm optimization (PSO) algorithm is employed for online tuning of the proposed controller. The computer simulation results show that this controller is effective and gives preferable and rigorous performance compared with a results obtained from conventional wavelet neural network (WNN) and PID controllers.

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Section: Particle Swarm Optimization Techniquementioning

confidence: 99%

Motion control of linear induction motor based on optimal recurrent wavelet neural network-PID controller

Saleh

Obaid

Obed

2018

IJET

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“…The architecture of the proposed PSO technique is depicted in Figure 1. It uses micro generation model which consists of various sources like wind turbines, solar cells and batteries [12][13][14][15]. This combination of the different sources together leads to a new design of Hybrid Renewable Energy System (HRES) [16].…”

Section: Proposed Techniquementioning

confidence: 99%

Improving Efficiency of Power Systems by Demand Side Management Method

Symon¹,

Ragland²

2017

IJECE

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In the smart grid infrastructure based power systems, it is necessary to consider the demand side management to enhance the energy reduction and system control. In many countries the resources are very less so the available resources have to be used in an efficient manner without any loss. The total loss cannot be avoided but it can be reduced. In the proposed system, the Particle Swarm Optimization (PSO) technique is used to distribute the power in the smart grid. Here, the grids are arranged in such a way that the losses in it are reduced. The load connected to the grid is rearranged according to their use. It uses a new and stochastic scheduling technique to handle the uncertainties in the power system. Solar and wind power are taken in account for twenty four hours and the values are given to the PSO algorithm. The experiment was conducted by MATLAB and the results show that the efficiency level of wind and solar power systems was increased by an appreciable level. The proposed technique is compared with the normal system without using Demand Side Management (DSM) and it shows that the proposed system gives better results than the existing systems.

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“…Leandro dos Santos Coelho a,*, Chu-Sheng Lee b, using chaotic and Gaussian particle swarm optimization Solving economic load dispatch problem [2]. Hossein Shahinzadeh used Particle Swarm Optimization algorithm to solve the economic load dispatch of units in power systems with valve-point effects consideration [3]. PSO is used for the solution of Dynamic Economic Load Dispatch (DELD) problem with valve point loading effects and ramp rate limits in the paper writen by G.Sreenivasan [4].…”

Section: Introductionmentioning

confidence: 99%

Economic Dispatch Using Quantum Evolutionary Algorithm in Electrical Power System Involving Distributed Generators

Aryani¹,

Soeprijanto²,

Negara³

et al. 2017

IJECE

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Unpredictable increase in power demands will overload the supply subsystems and insufficiently powered systems will suffer from instabilities, in which voltages drop below acceptable levels. Additional power sources are needed to satisfy the demand. Small capacity distributed generators (DGs) serve for this purpose well. One advantage of DGs is that they can be installed close to loads, so as to minimise loses. Optimum placements and sizing of DGs are critical to increase system voltages and to reduce loses. This will finally increase the overall system efficiency. This work exploits Quantum Evolutionary Algorithm (QEA) for the placements and sizing. This optimisation targets the cheapest generation cost. Quantum Evolutionary Algorithm is an Evolutionary Algorithm running on quantum computing, which works based on qubits and states superposition of quantum mechanics. Evolutionary algorithm with qubit representation has a better characteristic of diversity than classical approaches, since it can represent superposition of states.

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Applications of Particle Swarm Optimization Algorithm to Solving the Economic Load Dispatch of Units in Power Systems with Valve-Point Effects

Cited by 29 publications

References 16 publications

Motion control of linear induction motor based on optimal recurrent wavelet neural network-PID controller

Motion control of linear induction motor based on optimal recurrent wavelet neural network-PID controller

Improving Efficiency of Power Systems by Demand Side Management Method

Economic Dispatch Using Quantum Evolutionary Algorithm in Electrical Power System Involving Distributed Generators

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