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Full-Text Articles in Engineering
A Comparison Of Pso And Backpropagation For Training Rbf Neural Networks For Identification Of A Power System With Statcom, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Yamille Del Valle, Ronald G. Harley
A Comparison Of Pso And Backpropagation For Training Rbf Neural Networks For Identification Of A Power System With Statcom, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Yamille Del Valle, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
Backpropagation algorithm is the most commonly used algorithm for training artificial neural networks. While being a straightforward procedure, it suffers from extensive computations, relatively slow convergence speed and possible divergence for certain conditions. The efficiency of this method as the training algorithm of a radial basis function neural network (RBFN) is compared with that of particle swarm optimization, for neural network based identification of a small power system with a static compensator. The comparison of the two methods is based on the convergence speed and robustness of each method.
A Dynamic Recurrent Neural Network For Wide Area Identification Of A Multimachine Power System With A Facts Device, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Ronald G. Harley
A Dynamic Recurrent Neural Network For Wide Area Identification Of A Multimachine Power System With A Facts Device, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
Multilayer perceptron and radial basis function neural networks have been traditionally used for plant identification in power systems applications of neural networks. While being efficient in tracking the plant dynamics in a relatively small system, their performance degrades as the dimensions of the plant to be identified are increased, for example in supervisory level identification of a multimachine power system for wide area control purposes. Recurrent neural networks can deal with such a problem by modeling the system as a set of differential equations and with less order of complexity. Such a recurrent neural network identifier is designed and implemented …
An Adaptive Mamdani Fuzzy Logic Based Controller For A Static Compensator In A Multimachine Power System, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Ronald G. Harley
An Adaptive Mamdani Fuzzy Logic Based Controller For A Static Compensator In A Multimachine Power System, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
An adaptive Mamdani based fuzzy logic controller has been designed for controlling a static compensator (STATCOM) in a multimachine power system. Such a controller does not need any prior knowledge of the plant to be controlled and can efficiently control a STATCOM during different disturbances in the network. A model free approach using the controller output error is applied for training purposes that adaptively changes the controller output parameters based on a gradient descent method. Moreover, shrinking span membership functions are used for a more stable and accurate control performance. Simulation results show that the proposed controller outperforms the conventional …
Hardware Implementation Of A Mamdani Fuzzy Logic Controller For A Static Compensator In A Multimachine Power System, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Satish Rajagopalan, Ronald G. Harley
Hardware Implementation Of A Mamdani Fuzzy Logic Controller For A Static Compensator In A Multimachine Power System, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Satish Rajagopalan, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
A Mamdani based fuzzy logic controller is designed and implemented for controlling a STATCOM, which is connected to a 10 bus multimachine power system. Such a controller does not need any prior knowledge of the plant to be controlled and can efficiently provide control signals for the STATCOM during different disturbances in the network The proposed controller is implemented using the M67 DSP board and is interfaced to the multimachine power system simulated on a real-time digital simulator (RTDS). Experimental results are provided, showing that the proposed controller provides more effective damping than the conventional PI controller in a typical …