Fast voltage contingency screening using radial basis function neural network

Jain, Trapti; Srivastava, Laxmi; Singh, Shalendra

doi:10.1109/tpwrs.2003.818607

Cited by 95 publications

(58 citation statements)

References 19 publications

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“…Among all, the last issue may be the most important, because it is mainly an identification of model parameters that fits with the given data. Although the BP algorithm is commonly used in recent years to perform the training task, some drawbacks are often encountered using this gradient-based method, include: very slow training convergence speed and getting stuck in a local minimum easily [4,5]. In order to solve these drawbacks different algorithms have been proposed [6, 7 and 8].…”

Section: Introductionmentioning

confidence: 99%

Comparison Of Particle Swarm Optimization And Backpropagation Algorithms For Training Feedforward Neural Network

Mohammadi¹,

Mirabedini²

2014

J. Math. Computer Sci.

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An interesting tool for non-linear multivariable modeling is the Artificial Neural Network (ANN) which has been developed recently. The use of ANN has been proved to be a cost-effective technique. It is very important to choose a suitable algorithm for training a neural network. Generally Backpropagation (BP) algorithm is used to train the neural network. While these algorithms prove to be very effective and robust in training many types of network structures, they suffer from certain disadvantages such as easy entrapment in a local minimum and very slow convergence. In this paper, to improve the performance of ANN, the adjustment of network weights using Particle Swarm Optimization (PSO) was proposed as a mechanism and the results obtained were compared with various BP algorithms such as LevenbergMarquardt and gradient descent algorithms. Each of these networks runs and trains for different learning ratios, activation functions and numbers of neurons within their hidden layer. Among different criteria Mean Square Error (MSE) and Accuracy are the main selected criteria used for evaluating both models. Also the MSE was used as a criterion to specify optimum number of neurons in hidden layer. The results showed that PSO approach outperforms the BP for training neural network models.

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Section: Introductionmentioning

confidence: 99%

Comparison Of Particle Swarm Optimization And Backpropagation Algorithms For Training Feedforward Neural Network

Mohammadi¹,

Mirabedini²

2014

J. Math. Computer Sci.

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“…The choice of the number of hidden layers and hidden neurons are important in deciding the accuracy of the neural networks. In [1] an enhanced radial basis function neural network (RBFNN) approach is used for on-line ranking of the contingencies expected to cause steady state bus voltage and power flow violat ions. The advantage of this method is the simplicity in algorith m and accuracy in classification.…”

Section: Introductionmentioning

confidence: 99%

Power System Security Assessment and Contingency Analysis Using Supervised Learning Approach

2015

IJAERD

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“…Applications of ANNs in reactive power transfer allocation [10] and ATC estimation [11] have been reported. Stability issues like damping of oscillations [12][13][14], prediction of loadability margins [15] and voltage contingency screening [16] have been successfully addressed by ANN based solutions. ANNs have the potential of application for realtime control as reported [17].…”

Section: Introductionmentioning

confidence: 99%

Neural Network Based Identification of Multimachine Power System

Tiwari¹,

Naresh²,

Jha³

et al. 2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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This paper demonstrates an effective application of artificial neural networks for online identification of a multimachine power system. The paper presents a recurrent neural network as the identifier of the benchmark two area, four machine system. This neural identifier is trained using the static Backpropagation algorithm. The trained neural identifier is then tested using datasets generated by simulating the system under consideration at different operating points and a different loading condition. The test results clearly establish a satisfactory performance of the trained neural identifier in identification of the power system considered.

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Fast voltage contingency screening using radial basis function neural network

Cited by 95 publications

References 19 publications

Comparison Of Particle Swarm Optimization And Backpropagation Algorithms For Training Feedforward Neural Network

Comparison Of Particle Swarm Optimization And Backpropagation Algorithms For Training Feedforward Neural Network

Power System Security Assessment and Contingency Analysis Using Supervised Learning Approach

Neural Network Based Identification of Multimachine Power System

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