Yasir Thaier Haider; Dr. Abdulrahim Thiab Humod
Volume 15, Issue 2 , August 2015, , Page 1-10
Abstract
Abstract – Artificial Neural Networks (ANN) and Neuro - Fuzzy controllers can be used as intelligent controllers to control non-li¬near dynamic systems through learning, which can easily accommodate the non-linearity’s, time dependencies, model uncertainty and external disturbances. Modern power ...
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Abstract – Artificial Neural Networks (ANN) and Neuro - Fuzzy controllers can be used as intelligent controllers to control non-li¬near dynamic systems through learning, which can easily accommodate the non-linearity’s, time dependencies, model uncertainty and external disturbances. Modern power systems are complex and non-linear and their operating conditions can vary over a wide range. The Nonlinear Auto-Regressive Moving Average (NARMA-L2) model system is proposed as an effective neural networks controller model to achieve the desired robust Automatic Voltage Regulator (AVR) for Synchronous Generator (SG) to maintain constant terminal voltage. The essential part of Neuro-Fuzzy comes from a common framework called adaptive networks, which unifies both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks are called Adaptive-Network-based Fuzzy Inference System (ANFIS), which possess certain advantages over neural networks. The concerned neural networks and Neuro - Fuzzy controllers for AVR is examined on different models of SG and loads. The results show that the Neuro-controllers and Neuro - Fuzzy controllers have excellent responses for all SG models and loads in view point of transient response and system stability. Also it shows that the margins of robustness for Neuro - Fuzzy controller are greater than Neuro-controller.
Dr. Abdulrahim Thiab Humod
Volume 13, Issue 3 , December 2013, , Page 51-64
Abstract
Abstract – Artificial Neural Networks (ANN) can be used as intelligent controllers to
control non-linear dynamic systems through learning, which can easily accommodate
the non linearity’s, time dependencies, model uncertainty and external disturbances.
Modern power systems are complex and non-linear ...
Read More ...
Abstract – Artificial Neural Networks (ANN) can be used as intelligent controllers to
control non-linear dynamic systems through learning, which can easily accommodate
the non linearity’s, time dependencies, model uncertainty and external disturbances.
Modern power systems are complex and non-linear and their operating conditions can
vary over a wide range. The Nonlinear Auto-Regressive Moving Average (NARMAL2)
model system is proposed as an effective neural networks controller model to
achieve the desired robust Automatic Voltage Regulator (AVR) for Synchronous
Generator (SG) to maintain constant terminal voltage. The concerned neural networks
controller for AVR is examined on different models of SG and loads. The results shows
that the neuro-controllers have excellent responses for all SG models and loads in view
point of transient response and system stability compared with conventional PID
controllers. Also shows that the margins of robustness for neuro-controller are greater
than PID controller.
Dr. Abdulrahim Thiab Humod
Volume 11, Issue 2 , December 2011, , Page 56-64
Abstract
Abstract:
A Tuning Model Reference Adaptive Controller (TMRAC) for a synchronous generator is presented in this paper. The controller performs the function of terminal voltage of the machine. The proposed controller is used to overcome the problems of nonlinearities and parametric uncertainties for ...
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Abstract:
A Tuning Model Reference Adaptive Controller (TMRAC) for a synchronous generator is presented in this paper. The controller performs the function of terminal voltage of the machine. The proposed controller is used to overcome the problems of nonlinearities and parametric uncertainties for Synchronous Generator (SG). The results verify improved performance of TMRAC compared to conventional Automatic Voltage Regulator (AVR) under various operating conditions.