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Full-Text Articles in Engineering
Series-Parallel Approaches And Clamp Methods For Extreme Dynamic Response With Advanced Digital Loads, Philip T. Krein, Jonathan W. Kimball
Series-Parallel Approaches And Clamp Methods For Extreme Dynamic Response With Advanced Digital Loads, Philip T. Krein, Jonathan W. Kimball
Electrical and Computer Engineering Faculty Research & Creative Works
The series-input parallel-output dc-dc converter combination provides inherent sharing among the converters. With conventional controls, however, this sharing is unstable. Recent literature work proposes complicated feedback loops to correct the problem, at the cost of dynamic performance. This paper shows that a simple sensorless current mode control stabilizes sharing with fast dynamics suitable for advanced digital loads. With this control in place, a "super-matched" current sharing control emerges. Sharing occurs through transients, limited only by the energy limits of the converters. The control approach has considerable promise for high-performance voltage regulator modules. For even faster response, clamping techniques are proposed.
Adaptive Load Frequency Control Of Nigerian Hydrothermal System Using Unsupervised And Supervised Learning Neural Networks, Ganesh K. Venayagamoorthy, U. O. Aliyu, S. Y. Musa
Adaptive Load Frequency Control Of Nigerian Hydrothermal System Using Unsupervised And Supervised Learning Neural Networks, Ganesh K. Venayagamoorthy, U. O. Aliyu, S. Y. Musa
Electrical and Computer Engineering Faculty Research & Creative Works
This work presents a novel load frequency control design approach for a two-area power system that relies on unsupervised and supervised learning neural network structure. Central to this approach is the prediction of the load disturbance of each area at every minute interval that is uniquely assigned to a cluster via unsupervised learning process. The controller feedback gains corresponding to each cluster center are determined using modal control technique. Thereafter, supervised learning neural network (SLNN) is employed to learn the mapping between each cluster center and its feedback gains. A real time load disturbance in either or both areas activates …