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.
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- "Pada referensi , diusulkan suatu metoda aliran daya menggunakan JST dengan minimalisasi model. Model MLP terpisah menggunakan metoda pelatihan Lavenberg-Marquadrt tingkat kedua sudah dipakai untuk menghitung magnitudo dan sudut tegangan masing-masing bus pada sistem tenaga . Bilangan JST yang diperlukan untuk memecahkan persoalan aliran daya adalah besar, dan tidak mungkin dapat diaplikasikan pada sistem tenaga praktis dengan jumlah bus yang sangat besar. "
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- "ANN are widely used in short term load forecasting , in fault classification and fault location in transmission lines    , in voltage stability analysis , in power system economic dispatch solution problems and in power system stabilizer design . Furthermore the ANNs present to have applications in the solution of the power flow problem , to the effective distance protection of the transmission lines  , to the prediction of high voltage insulators' flashover  and to the calculation of insulators' surface contamination under various meteorological conditions . Finally studies, which are using ANNs, have been presented for the evaluation of lightning overvoltages in distributions lines  and for the protection of high voltage transmission lines . "
ABSTRACT: Feed-forward (FF) artificial neural networks (ANN) and radial basis function (RBF) ANN methods were addressed for evaluating the lightning performance of high voltage transmission lines. Several structures, learning algorithms and transfer functions were tested in order to produce a model with the best generalizing ability. Actual input and output data, collected from operating Hellenic high voltage transmission lines, as well as simulated output data were used in the training, validation and testing process. The aims of the paper are to describe in detail and compare the proposed FF and RBF ANN models, to state their advantages and disadvantages and to present results obtained by their application on operating Hellenic transmission lines of 150 kV and 400 kV. The ANN results are also compared with results obtained using conventional methods and real records of outage rate showing a quite satisfactory agreement. The proposed ANN methods can be used by electric power utilities as useful tools for the design of electric power systems, alternative to the conventional analytical methods.Electric Power Systems Research 01/2007; 77(1):55-63. DOI:10.1016/j.epsr.2006.01.005 · 1.60 Impact Factor
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- "In reference , a neural network load flow using an ANN-based minimisation model is proposed. A separate MLP model based on Levenberg-Marquardt second order training method has been used for computation for bus voltage magnitude and for angle at each bus of power system in reference . As the number of neural networks required to solve load flow problem are large, it may not be applicable to a practical power system having huge number of buses. "
ABSTRACT: Load flow (LF) study, which is performed to determine the power system static states (voltage magnitudes and voltage angles) at each bus to find the steady state operating condition of a system, is very important and is the most frequently carried out study by power utilities for power system planning, operation and control. In this paper, a radial basis function neural network (RBFN) is proposed to solve load flow problem under different loading/contingency conditions for computing bus voltage magnitudes and angles of the power system. The RBFN has many advantageous features such as optimised system complexity, minimized learning and recall times as compared to multi-layer perceptron model. The composition of the input variables for the proposed neural network has been selected to emulate the solution process of a conventional load flow program. The effectiveness of the proposed RBFN based approach for on-line application is demonstrated by computation of bus voltage magnitudes and voltage angles for different loading conditions and single line-outage contingencies in IEEE 14-bus system.