Artificial neural networks for solving the power flow problem in electric power systems
ABSTRACT In this paper, the use of artificial neural networks (ANN) is proposed for solving the well known power flow (PF) problem of electric power systems (EPS). PF evaluates the steady state of EPS and is a fundamental tool for planning, operation and control of modern power systems. The mathematical model of the PF comprises a set of non-linear algebraic equations conventionally solved with the Newton-Raphson method or its decoupled versions. In order to take advantage of the superior speed of ANN over conventional PF methods, multilayer perceptrons neural networks trained with the second order Levenberg–Marquardt method have been used for computing voltages magnitudes and angles of the PF problem. The proposed ANN methodology has been successfully tested using the IEEE-30 bus system.
Conference Paper: Power flow model based on artificial neural networks[Show abstract] [Hide abstract]
ABSTRACT: In this paper a model and a methodology for using artificial neural networks to solve the load flow problem are proposed. An evaluation of the input data required by the ANN as well as its architecture is also presented. The ANN model used in this paper is the multilayer perceptron, and the training process is based on the second order Levenberg-Marquardt method. The proposed methodology was evaluated using the Ward-Hale 6 bus, the IEEE 14 bus and the IEEE 30 bus systems, considering normal operating conditions (base case) and different contingency scenarios, including different load/generation patterns. The simulation results show the excellent performance of the ANN, proving its ability to solve the load flow problem.Power Tech, 2005 IEEE Russia; 01/2005
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ABSTRACT: This paper proposes a new dead time compensation method of independent six-phase permanent magnet synchronous motors (IS-PMSM). The current of the independent phase machines contains odd-numbered harmonics because of the dead time and the nonlinear characteristics of the switching devices. By using the d-q-n three-dimensional vector analysis, these harmonics can be extracted at the n-axis current. Thus, the current distortion can be compensated by controlling the n-axis current of the IS-PMSM to zero. The proposed method is simple and can be easily implemented without additional hardware setup. The validity of the proposed compensation method is verified with simulations and several experiments.Journal of power electronics 01/2013; 13(1). · 0.78 Impact Factor
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ABSTRACT: Artificial neural networks (ANNs) are addressed in order to estimate the electric field across medium voltage surge arresters, information which is very useful for diagnostic tests and design procedures. Actual input and output data collected from hundreds of measurements carried out in the High Voltage Laboratory of the National Technical University of Athens (NTUA) are used in the training, validation and testing process. The developed ANN method can be used by laboratories and manufacturing/retail companies dealing with medium voltage surge arresters which either face a lack of suitable measuring equipment or want to compare/verify their own measurements.15th International Conference, EANN 2014, Sofia, Bulgaria, September 5-7, 2014; 09/2014