Transient stability assessment using artificial neural networks

Krishna, S.; Padiyar, К.R.

doi:10.1109/icit.2000.854241

Cited by 29 publications

(13 citation statements)

References 12 publications

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“…Recent applications of ANN have shown that they have considerable potential in overcoming the difficult tasks of data processing and interpretation. Four major steps are necessary in ANN applications [26][27][28]:…”

Section: Modeling Of Photovoltaic Cellmentioning

confidence: 99%

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A new neural networks MPPT controller for PV systems

Messalti

Harrag

Loukriz

2015

IREC2015 the Sixth International Renewable Energy Congress

109

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In this paper, an Artificial Neural Network (ANN) MPPT controller has been proposed. The data required to generate the ANN model are obtained from the principle of Perturbation and Observation (P&O) method. The neural network MPPT controller is developed in two modes: the offline mode required for testing different set of neural network parameters to find the optimal neural network controller (structure, activation function, and training algorithm) and the online mode which the optimal ANN MPPT controller is used in PV system. The inputs variables for ANN are the output power derivate (dP) and voltage derivate (dV) corresponding to a given insolation and operating cell temperature conditions, which they have significant influence on the ANN response ; the output variable of ANN is the corresponding normalized increasing or decreasing duty cycle (+1 or -1). The proposed neural network MPPT is tested and validated using Matlab/Simulink model for different atmospheric conditions. Results and analysis are presented, many contribution have been demonstrated (response time, MPPT tracking, Overshoot).

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Section: Modeling Of Photovoltaic Cellmentioning

confidence: 99%

“…The training algorithm is used to find the weights that minimize some overall error measure such as the sum of squared errors (SSE) or mean squared errors (MSE) [27].…”

Section: -Training the Ann And Testingmentioning

confidence: 99%

A new neural networks MPPT controller for PV systems

Messalti

Harrag

Loukriz

2015

IREC2015 the Sixth International Renewable Energy Congress

109

View full text Add to dashboard Cite

show abstract

“…There are two ways in using ANN for power system transient stability assessment, one way is using the regression function of ANN to predict transient stability degree [12][13][14][15], such as critical clearing time and system stability margin; another way is using the classification function of ANN to directly classify the system into either stable or unstable states [16][17][18]. Although ANN is the most popular machine learning method to classify patterns, it requires an extensive training process and a complicated design procedure.…”

Section: Introductionmentioning

confidence: 99%

Transient stability assessment of power system using support vector machine with generator combinatorial trajectories inputs

You

Wang

et al. 2013

International Journal of Electrical Power & Energy Systems

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“…Some works have been carried out using the feed forward multilayer perceptron (MLP) with back propagation learning algorithm to determine the CCT of power systems [10,11]. Radial basis function networks proposed to estimate the CCT [12]. Another method to assess power system transient stability using ANN is by means of classifying the system into either stable or unstable states for several contingencies applied to the system [4,12,13].…”

Section: Introductionmentioning

confidence: 99%

“…Radial basis function networks proposed to estimate the CCT [12]. Another method to assess power system transient stability using ANN is by means of classifying the system into either stable or unstable states for several contingencies applied to the system [4,12,13]. ANN method based on fuzzy ARTMAP architecture is also used to analyze TSA of a power system [14].…”

Section: Introductionmentioning

confidence: 99%