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Articles 1 - 4 of 4
Full-Text Articles in Electrical and Computer Engineering
The Multirate Simulation Of Facts Devices In Power System Dynamics, J. G. Chen, Mariesa Crow
The Multirate Simulation Of Facts Devices In Power System Dynamics, J. G. Chen, Mariesa Crow
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, the multirate method is applied to the problem of simulating the dynamics of a power system which contains fast components such as induction machine loads and FACTS devices. Results concerning the numerical stability and accuracy of the multirate method are presented. Implementation concerns are also addressed by studying an example power system which contains a wide range of time response behavior
Security-Constrained Optimal Rescheduling Of Real Power Using Hopfield Neural Network, S. Ghosh, Badrul H. Chowdhury
Security-Constrained Optimal Rescheduling Of Real Power Using Hopfield Neural Network, S. Ghosh, Badrul H. Chowdhury
Electrical and Computer Engineering Faculty Research & Creative Works
A new method for security-constrained corrective rescheduling of real power using the Hopfield neural network is presented. The proposed method is based on solution of a set of differential equations obtained from transformation of an energy function. Results from this work are compared with the results from a method based on dual linear programming formulation of the optimal corrective rescheduling. The minimum deviations in real power generations and loads at buses are combined to form the objective function for optimization. Inclusion of inequality constraints on active line flow limits and equality constraint on real power generation load balance assures a …
Fault Classification Using Kohonen Feature Mapping, Badrul H. Chowdhury, Kunyu Wang
Fault Classification Using Kohonen Feature Mapping, Badrul H. Chowdhury, Kunyu Wang
Electrical and Computer Engineering Faculty Research & Creative Works
Applications of neural networks to power system fault diagnosis have provided positive results and shown advantages in process speed over conventional approaches. This paper describes the application of a Kohonen neural network to fault detection and classification using the fundamental components of currents and voltages. The Electromagnetic Transients Program is used to obtain fault patterns for the training and testing of neural networks. Accurate classifications are obtained for all types of possible short circuit faults on test systems representing high voltage transmission lines. Short training time makes the Kohonen network suitable for on-line power system fault diagnosis. The method introduced …
Input Dimension Reduction In Neural Network Training-Case Study In Transient Stability Assessment Of Large Systems, S. Muknahallipatna, Badrul H. Chowdhury
Input Dimension Reduction In Neural Network Training-Case Study In Transient Stability Assessment Of Large Systems, S. Muknahallipatna, Badrul H. Chowdhury
Electrical and Computer Engineering Faculty Research & Creative Works
The problem in modeling large systems by artificial neural networks (ANN) is that the size of the input vector can become excessively large. This condition can potentially increase the likelihood of convergence problems for the training algorithm adopted. Besides, the memory requirement and the processing time also increase. This paper addresses the issue of ANN input dimension reduction. Two different methods are discussed and compared for efficiency and accuracy when applied to transient stability assessment.