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Full-Text Articles in Engineering
Neural Network Stabilizing Control Of Single Machine Power System With Control Limits, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow
Neural Network Stabilizing Control Of Single Machine Power System With Control Limits, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow
Electrical and Computer Engineering Faculty Research & Creative Works
Power system stabilizers are widely used to generate supplementary control signals for the excitation system in order to damp out the low frequency oscillations. This paper proposes a stable neural network (NN) controller for the stabilization of a single machine infinite bus power system. In the power system control literature, simplified analytical models are used to represent the power system and the controller designs are not based on rigorous stability analysis. This work overcomes the two major problems by using an accurate analytical model for controller development and presents the closed-loop stability analysis. The NN is used to approximate the …
Dynamic Optimization Of A Multimachine Power System With A Facts Device Using Identification And Control Objectnets, Ganesh K. Venayagamoorthy
Dynamic Optimization Of A Multimachine Power System With A Facts Device Using Identification And Control Objectnets, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This work presents a novel technique for dynamic optimization of the electric power grid using brain-like stochastic identifiers and controllers called ObjectNets based on neural network architectures with recurrence. ObjectNets are neural network architectures developed to identify/control a particular object with a specific objective in hand. The IEEE 14 bus multimachine power system with a FACTS device is considered in this paper. The paper focuses on the combined minimization of the terminal voltage deviations and speed deviations at the generator terminals and the bus voltage deviation at the point of contact of the FACTS device to the power network. Simulation …
Supervisory Level Neural Network Identifier For A Small Power System With A Statcom And A Generator, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Ronald G. Harley
Supervisory Level Neural Network Identifier For A Small Power System With A Statcom And A Generator, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
A neural network based identifier is designed for effective control of a small power system. The power network in this work is considered from an external point of view, i.e., from a supervisory level. Such a neuroidentifier can serve as a general model of such a plant, and then used for different neural network based control schemes.
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 …
Neuroidentification Of System Parameters Of The Upfc In A Multimachine Power System, Radha P. Kalyani, Ganesh K. Venayagamoorthy
Neuroidentification Of System Parameters Of The Upfc In A Multimachine Power System, Radha P. Kalyani, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
The crucial factor affecting the modern power systems today is load flow control. The Unified Power Flow Controller is an effective means for controlling the power flow. The UPFC is controlled conventionally using PI controllers. This paper presents the designs of neuroidentifiers that models the system dynamics one-time step ahead making the pathway for the design of adaptive neurocontrollers. Two neuroidentifiers are used for identifying the nonlinear dynamics of a multimachine power system and UPFC, one neuroidentifier for the shunt inverter and another for the series inverter. Simulation results carried out in the PSCAD/EMTDC environments on multimachine power system are …
Modified Takagi-Sugeno Fuzzy Logic Based Controllers For A Static Compensator In A Multimachine Power System, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Ronald G. Harley
Modified Takagi-Sugeno Fuzzy Logic Based Controllers 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
Takagi-Sugeno (TS) based fuzzy logic controllers have been designed for controlling a STATCOM in a multimachine power system. Such controllers do not need any prior knowledge of the plant to be controlled and can efficiently control a STATCOM during different disturbances in the network. Two different approaches for the TS fuzzy logic controller are proposed: a conventional TS fuzzy logic design and a modified TS fuzzy logic design based on shrinking span membership functions. Simulation results, along with a comparison of the conventional TS fuzzy logic controller performance with that of the proposed controller are presented.