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Full-Text Articles in Engineering
Bio-Inspired Algorithms For The Design Of Multiple Optimal Power System Stabilizers: Sppso And Bfa, Tridib Kumar Das, Ganesh K. Venayagamoorthy
Bio-Inspired Algorithms For The Design Of Multiple Optimal Power System Stabilizers: Sppso And Bfa, Tridib Kumar Das, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
Power System Stabilizers (PSSs) provide stabilizing control signals to excitation systems to damp out inter-area and intra-area oscillations. The PSS must be optimally tuned to accommodate the variations in the system dynamics. Designing multiple optimal PSSs is a challenging task for researchers. This paper presents the comparison between two bio-inspired algorithms: a Small Population based Particle Swarm Optimization (SPPSO) and the Bacterial Foraging Algorithm (BFA) for the simultaneous tuning of a number of PSSs in a multi-machine power system. The cost function to be optimized by both algorithms takes into consideration the time domain transient responses. The effectiveness of the …
Optimal Allocation Of A Statcom In A 45 Bus Section Of The Brazilian Power System Using Particle Swarm Optimization, J. C. Hernandez, Ganesh K. Venayagamoorthy, Yamille Del Valle, Ronald G. Harley
Optimal Allocation Of A Statcom In A 45 Bus Section Of The Brazilian Power System Using Particle Swarm Optimization, J. C. Hernandez, Ganesh K. Venayagamoorthy, Yamille Del Valle, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
This paper introduces the application of Particle Swarm Optimization (PSO) to solve the optimal allocation of a STATCOM in a 45 bus system which is part of the Brazilian power network. The criterion used in finding the optimal location is based on the voltage profile of the system, i.e. the voltage deviation at each bus, with respect to its optimum value, is minimized. In order to test the performance of the PSO algorithm in this particular application, different approaches for inertia weight are investigated; also different values of acceleration constants, number of iterations and maximum velocity are considered. A sensitivity …
Comparison Of Pso And Ga For K-Node Set Reliability Optimization Of A Distributed System, G. A. Bakare, I. N. Chiroma, Ganesh K. Venayagamoorthy
Comparison Of Pso And Ga For K-Node Set Reliability Optimization Of A Distributed System, G. A. Bakare, I. N. Chiroma, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
Particle Swarm Optimization (PSO), as a novel evolutionary computing technique, has succeeded in many continuous problems, but quite a little research on discrete problem especially combinatorial optimization problem has been reported. In this paper, a discrete PSO algorithm is proposed to solve a typical combinatorial optimization problem: K-Node Set Reliability (KNR) optimization of a distributed computing system (DCS) which is a well-known NP-hard problem is presented. It computes the reliability of a subset of network nodes of a DCS such that the reliability is maximized and specified capacity constraint is satisfied. The feasibility of the proposed algorithm is demonstrated on …
Density Estimation Using A Generalized Neuron, R. Kiran, Ganesh K. Venayagamoorthy, M. Palaniswami
Density Estimation Using A Generalized Neuron, R. Kiran, Ganesh K. Venayagamoorthy, M. Palaniswami
Electrical and Computer Engineering Faculty Research & Creative Works
Neural networks have been shown to be useful tools for density estimation. However, the training of neural network structures is time consuming and requires fast processors for practical applications. A new method with a generalized neuron (GN) for density estimation is presented in this paper. The GN is trained with the particle swarm optimization algorithm which is known to have fast convergence than the standard backpropagation algorithm. Results are presented to show that the GN can estimate the density functions for distribution functions with different means and variances. This density estimation method can also be applied to the multi-sensor data …
Particle Swarm Optimization Based Defensive Islanding Of Large Scale Power System, Wenxin Liu, Ganesh K. Venayagamoorthy, David A. Cartes
Particle Swarm Optimization Based Defensive Islanding Of Large Scale Power System, Wenxin Liu, Ganesh K. Venayagamoorthy, David A. Cartes
Electrical and Computer Engineering Faculty Research & Creative Works
Defensive islanding is an efficient way to avoid catastrophic failures and wide area blackouts. Power system splitting especially for large scale power systems is a combinatorial explosion problem. Thus, it is very difficult to find an optimal solution (if one exists) for large scale power system in real time. This paper proposes to utilize the computational efficiency property of Binary Particle Swarm Optimization (BPSO) to find some efficient splitting solutions in limited timeframe. The solutions are optimized based on a cost function considering the balance between real power generation and consumption, the relative importance of customers, the capacities of distribution …
Comparison Of Two Optimal Control Strategies For A Grid Independent Photovoltaic System, Richard L. Welch, Ganesh K. Venayagamoorthy
Comparison Of Two Optimal Control Strategies For A Grid Independent Photovoltaic System, Richard L. Welch, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents two optimal control strategies for a grid independent photovoltaic system consisting of a PV collector array, a storage battery, and loads (critical and non-critical loads). The first strategy is based on Action Dependent Heuristic Dynamic Programming (ADHDP), a model-free adaptive critic design (ACD) technique which optimizes the control performance based on a utility function. ADHDP critic network is used in a PV system simulation study to train an action neural network (optimal neurocontroller) to provide optimal control for varying PV system output energy and loadings. The second optimal control strategy is based on a fuzzy logic controller …