Artificial neural networks for solving the power flow problem in electric power systems

Paucar, V. Leonardo; Rider, Marcos J.

doi:10.1016/s0378-7796(02)00030-5

Cited by 54 publications

(21 citation statements)

References 11 publications

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“…A MLP trained with the back-propagation algorithm may be viewed as a practical vehicle for performing a nonlinear input/output mapping of a general nature. A simple MLP neural network composed of single hidden layer and output layer is capable of solving difficult and complex problems [9]. The schematic diagram of the proposed MLP neural networks architecture is shown in Figure 2.…”

Section: Ann Design Training and Testingmentioning

confidence: 99%

Selection of station surge arresters based on the evaluation of failure probability using artificial neural networks

Shariatinasab

Vahidi

Hosseinian

et al. 2007

2007 42nd International Universities Power Engineering Conference

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Considering the lightning strikes, failure probability of arresters due to the highly nonlinear voltage stresses and current characteristics (v-i) of the arresters is not obvious. It means that the evaluation of failure probability and the energy capacity can be a difficult and long task. This paper presents an artificial neural network (ANN) based approach to estimate directly the failure probability of an arrester and then indirectly select the proper energy capacity to fulfill the adopted failure rate. The application of ANN is applied to a group of arresters and the results of the ANN test coincide with the analytical ones.

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Section: Ann Design Training and Testingmentioning

confidence: 99%

Selection of station surge arresters based on the evaluation of failure probability using artificial neural networks

Shariatinasab

Vahidi

Hosseinian

et al. 2007

2007 42nd International Universities Power Engineering Conference

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“…Modeling electric devices take place to define how can these devices be modified. Different techniques have been used for modeling many electric devices [7][8][9][10][11]. The ATP-EMTP software is used for modeling the synchronous generator excitation and governor on an interconnected power system [12].…”

Section: Introductionmentioning

confidence: 99%

“…The ATP-EMTP software is used for modeling the synchronous generator excitation and governor on an interconnected power system [12]. The artificial neural network is applied successfully for solving the steady state power flow of electric power systems [10]. The use of ANN in solving the steady state power flow is superior over the conventional Newton-Raphson method.…”

Section: Introductionmentioning

confidence: 99%

Modeling Static VAR Compensators Using Adaptive Neural Network

Alkhalaf

Alfarraj

Hemeida³

2015

May 13-14, 2015 Kuala Lumpur, Malaysia

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“…Such centers perform automatic generation control, state estimation and topological analysis [1]. Most of the functions performed depend on the execution of several power flow studies, which determine the Power System operation point while in steady-state [2].…”

Section: Introductionmentioning

confidence: 99%

Load flow computation via Particle Swarm Optimization

Salomon

Lambert‐Torres

Martins

et al. 2010

2010 9th IEEE/IAS International Conference on Industry Applications - INDUSCON 2010

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This paper proposes the application of a Particle Swarm Optimization (PSO) algorithm to the Load Flow calculation in Power Systems. The proposed methodology is based on the minimization of power mismatches in the system buses. The power flow study provides the system status in the steady-state and it is fundamental to the power system operation, planning and control. PSO is applied in a new computational model for the system power flow obtainment. This model searches for a better convergence, as well as a wider application in comparison with traditional methods as the Newton-Raphson method. This methodology was tested with numerical experiments accomplished in an IEEE 6-Bus System.

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Artificial neural networks for solving the power flow problem in electric power systems

Cited by 54 publications

References 11 publications

Selection of station surge arresters based on the evaluation of failure probability using artificial neural networks

Selection of station surge arresters based on the evaluation of failure probability using artificial neural networks

Modeling Static VAR Compensators Using Adaptive Neural Network

Load flow computation via Particle Swarm Optimization

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